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Product User Manual - H-SAF

1. Figure 111 Comparison of selected statistical scores for the PR OBS 2 product as in previous figure except for event Mean Error Multiplicative Bias T Graingauge Graingauge E E radar E radar O INCA O INCA 0 25sPR lt 1 1 lt PR lt 10 PR210 PR20 25 0 25sPR lt 1 1 lt PR lt 10 PR210 PR20 25 Class mm h Class mm h Relative RMSE Correlation Coefficient 400 350 300 a 2507 Graingauge H raingauge Q 200 radar H radar 150 O INCA OINCA 100 50 0 0 25sPR lt 1 1 lt PR lt 10 PR210 PR20 25 0 25sPR lt 1 1sSPR lt 10 PR210 PR20 25 Class mm h Class mm h Probability of detection False alarm rate a G raingauge i G raingauge Q H radar q E radar O INCA O INCA RR 2 0 25 mm h RR 2 1 mm h RR 2 0 25 mm h RR 2 1 mm h Threshold Threshold Critical success index _ G raingauge radar O INCA RR 2 0 25 mm h RR 2 1 mm h Threshold 4 stratiform Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 161 177 Product Validation Report PVR 02 Conclusion The INCA system as a potential tool for the precipitation products validation is available in Slovakia and Poland in both countries being run in pre operational mode It is still relatively new system undergoing continuous development More sophisticated algorithms of the precipitation analysis e g assimilation of the 3 D radar data can be expected fr
2. f a 0 De i 0 i Patin 10 a Torche blarinewnr n E ER ei 0 4 4 s Pad Solze n Te lo e ns Te 1 i lo 7 Gabika ki Escon Roane ik Kannat P Disetpaild 23 Y 1 A h iq ew Scan tf ha Erua VU eT o rt S i ENA p em gt e Ho f s i N UES ag ri Y d 06 k l i l 2 Taaa elie a 0g 0 0 J i3 S dnra kin 0 Oo i j SE alll HE ae 4 F Jar f a 4 ET 06 Wingard 3 d s y 29 7 ag sada abet Aaa i re NA firii Jh 1 i tr E RS eae E 2 bag i ai ater ay dee ANS ere Terai i te Figure 63 12h totals of precipitation ending FU at 3rd June 2010 7 UTC Figure 64 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 06 03 01 50 UTC station Athens and for RADOLAN RW left filled raster 2010 06 03 01 50 UTC and station data right dots 2010 06 03 02 00 UTC Data used HO2 data for Bavaria in the given period were available for 1 30 UTC Rome 1 49 UTC Mos 1 50 UTC Athens 11 42 UTC Rome 13 18 UTC Rome and 13 22 UTC Lannion Only these data are analysed for this case study Comparison A first look to the results shows that rain rates detected by satellite product are in the same area of Germany as those indicated by the ground data Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 92 177 Product Validation Report PVR 02 Statistical score In the next two ta
3. H02 NOAA NP 06 Jul 2010 12 16 UTC H02 NOAA NN 06 Jul 2010 12 27 UTC 0 20 E mm h Figure 77 H02 precipitation map at 12 16 UTC top left at 12 27 top right and raingauges hourly precipitation cumulated at 13 00 UTC bottom panel of 06 July 2010 Please note different colour scales zero rainrate gauges are not shown Conclusions For this convective case the performances of h02 are satisfactory pointing out that no parallax correction is applied and this could be particularly effective in case of small convective structures This case is also useful to understand the inadequacy of hourly gauge measurements in validating instantaneous satellite snapshots as in case of h02 As a matter of fact the variability of the precipitation field well described by the two overpasses shown above cannot be caught by hourly integrals provided by the raingauges especially in case of convective precipitation The skill score The saa i n P d tV lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 satene Appice EnH H SAF roduct Validation Report Issue Revision Index 1 1 racy ond wee Product H02 PR OBS 2 Date 30 09 2011 Page 103 177 values reported above indicate rather different performances between the two satellite overpasses HSS increases from 29 to 37 in 10 minutes It seems the main problem of this technique for convective precipitation is related to the relatively large IFOV of the AMSU data that preve
4. Resolution 2 km Threshold 7dBZ Rain gauge correction applied for 12 24 hourly data Table 12 Inventory of the main radar data and products characteristics in Belgium Italy Hungary pf POLAND SLOVAKIA TURKEY List of Available PPI PCAPPI RHI MAX Products EHT SRI PAC VIL VVP HWIND VSHEAR HSHEAR LTB SWI MESO WRN List of non operational products LMR CMAX UWT VAD SHEAR SWI MESO ZHAIL RTR CTR WRN Doppler method clutter removal attenuation correction yes VPR gt No Z R a 200 b 1 6 Processing chain Is any quality NO in development map available CAPPI 2 km Etops PPI 0 2 Base Cmax Hmax VIL Precip Intensity 1h 3h 6h 24h acc precip Lh acc SRI 1km 2km agl Clutter filtering frequency domain IIR filter Atmospheric attenuation correction Z R a 200 b 1 6 RLAN filtering in development MAX PPI CAPPI VIL ETOPS EBASE RAIN Accumulation 1 3 6 12 24h Clutter Correction b 1 6 Removal VPR Z R A 200 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 42 177 Product Validation Report PVR 02 Description of National composite SRI National composite CAPPI Projection instantaneous Projection azimutal CAPPI 2 km Azimuthal Equidistant radar product equidistant standard Projection Mercator Resolution 250 m used in HSAF ellipsoid Re
5. composite Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Product Validation Report PVR 02 Product H02 PR OBS 2 Page 142 177 No rain gauge correction stereographic S60 Resolution 2 km Threshold 7dBZ Rain gauge correction applied for 12 24 hourly data POLAND SLOVAKIA TURKEY List Available Products of PPI PCAPPI RHI MAX EHT SRI PAC VIL VVP HWIND VSHEAR HSHEAR LTB SWI MESO WRN List of non operational products LMR CMAX UWT VAD SHEAR SWI MESO ZHAIL RTR CTR WRN Doppler method clutter removal attenuation correction yes VPR gt No Z R a 200 b 1 6 Processing chain Description of National instantaneous SRI radar product azimutal equidistant used in HSAF standard elipsoid Validation Resolution 1 km Activities Threshold 5 dBZ No rain gauge correction Description of Acc Periods 1 6 24h accumulated National composite radar product PAC Projection used in HSAF azimuthal equidistant Validation standard elipsoid Activities Resolution 1 km Threshold 0 1 mm No rain gauge correction composite Projection CAPPI 2 km Etops PPI 0 2 Base Cmax Hmax VIL Precip Intensity 1h 3h 6h 24h acc precip Lh acc SRI 1km 2km agl Clutter frequency domain filter Atmospheric attenuation correction Z R a 200 b 1 6 RLAN filtering filtering IIR de
6. 6 3 The multi categorical statistic Two sets of validation have been performed e one set for Countries Teams that has compared satellite data with meteorological radar in inner land areas Belgium BE Germany DE Hungary HU and Slovakia SL e one set for Countries Teams that has compared satellite data with rain gauges in inner land areas Italy IT Germany DE Poland PO and Turkey TU Each Country Team contributes to this Chapter by providing the monthly contingency table and the Statistical scores The Validation Cluster Leader has collected all the validation files has verified the consistency of the results and evaluated the monthly and yearly contingency tables and the statistical scores 6 3 1 radar validation cate a ith o 0 0 27 0 27 27 0 61 61 0 61 61 0 63 63 0 65 65 0 65 65 ith RR gt o 2smm n 0 63 0 82 0 77 0 70 0 63 0 41 0 34 0 35 0 39 0 38 0 61 0 39 ith eE 0 18 0 10 ry my 0 43 0 47 0 47 ros ith a 0 16 0 31 0 27 0 58 0 53 0 33 0 26 0 43 ith RRsimm h 0 83 0 82 0 85 0 76 0 81 0 60 0 53 0 52 0 54 0 59 0 74 0 61 0 62 ith RRetmm n 0 09 0 11 0 08 0 15 0 13 0 29 0 33 0 35 0 35 0 30 0 17 0 19 0 25 Table 45 The averages POD FAR and CSI deduced comparing H02 with radar data Radar data SSS E T ie eareeie ozsa 2 el tm sooo o ow e Table 46 The contingency table for the three precipitation classes defined in table
7. At this stage upscaling technique is straightforward the upscaled value associated to every grid cell is Simply the arithmetical average of all the ground observations falling into that cell Software development extraction of a regular subset in the PR ASS 1 files The first program we developed useful to all groups with both the validation approaches allows to select a fixed number of rows and columns in the PR ASS 1 files given the geographical extremes of the chosen validation area In this way it s possible to process uniquely the data falling in and around the region of interest Software development upscaling of fine resolution data to the COSMO grid A prototype version of the upscaling procedure has been developed and successfully tested over Belgium It consists of two programs the first creates a lookup table a file which states a correspondence between every point of the observational grid radar in this case and the corresponding cell of the chosen subsection of the COSMO grid in which it falls The second upscales every observational file to the COSMO grid given the lookup table and it is part of the Belgian validation procedure previously developed by E Roulin RMI Belgium Preliminary testing results Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 164 177 Product Validation Report PVR 02 Here are the preliminary results obtained by testing the
8. Y Y Yes No aa Scan strategy scan Scan frequency 5 min Scan frequency 5 min frequency elevations maximum nominal range distance range resolution Range 240 Km Range 200 Km Elevations deg 0 2 0 7 1 4 2 5 Elevations deg 0 5 0 0 0 5 1 0 3 8 5 4 7 3 9 5 13 0 17 0 25 0 1 5 2 5 4 0 6 0 10 0 20 0 Resolution 1000m Resolution 125m Table 19 Characteristics of the SHMU radars The data processing For ground clutter removal the Doppler filtering is used In case of radar at Maly Javornik the frequency domain IIR filter is used at Kojsovska hola the Doppler filtering is supplemented with moving target identification MTI technique Isolated radar reflectivity and Doppler velocity bins are removed by the Speckle removal filter The data with signal to noise ratio below the specified threshold are also eliminated The measured radar reflectivity is corrected for atmospheric clear air attenuation of the radar beam Neither beam blocking correction nor vertical profile of reflectivity VPR is applied at SHMU However implementation of the beam blocking correction is being considered for the H SAF validation due to complicated orographical conditions in Slovakia Precipitation intensity is derived from radar reflectivity according to the Marshall Palmer equation Z a Rb with constant coefficients valid for stratiform rain a 200 b 1 6 Polarimetric techniques for quantitative precipitation estimation in case of dual
9. elevations for deriving rainfall intensities However the rest of the countries have also chosen different elevation angles for the CAPPI product which provides the basis for rain rate estimations Additionally we have to say that the countries apply different techniques of composition of radar data that were not specified in this questionnaire The composition technique is important in areas which are covered by more than one radar measurements Also the projection applied is varying from one country to the other To sum up the radar products used are not harmonized different techniques are applied However each of them is capable to grasp rainfall and to estimate rainfall intensity As for the accumulated products we see that Belgium uses 24 hourly accumulations with rain gauge correction Italy uses 3 6 12 24h accumulations without gauge correction in Hungary 3 6 12 24h data is used but only the 12h and 24 hourly accumulations are corrected by rain gauges in Poland and Slovakia no rain gauge correction is applied Poland has only 6 and 24 hourly data Turkey has 3 6 12 24h data and applies rain gauge correction for 1 hourly data It is important to note that techniques used for accumulation are numerous even within the same country the can differ from one accumulation period to another E g in Hungary the 3 6h accumulations are derived from summing up the interpolation of the 15minute frequent measurements into 1 minute intervals
10. located in mount Settepani operated by Piemonte and Liguria regions Next figure shows the hydrometeor classes detected by the classification algorithm corresponding to the event illustrated before Hydrometeors IC WS DS G H HA R H HR Distance North km MR LR LD Distance East km Figure 33 Hydrometeor classes as detected by the classification algorithm starting from the radar variables observed on 09 14 08 at 0500 U T C by the polarimetric radar operated by Piemonte and Liguria regions Note LD Large Drops LR Light Rain MR Moderate Rain HR Heavy Rain R H Rain Hail mixture HA Hail G H Graupel or small Hail DS Dry Snow WS Wet Snow IC Ice Crystals Reconstruction of vertical profile of reflectivity Rainfall estimation might be heavily perturbed by the presence of melting snow due to the enhancement of reflectivity factor caused by the increase in size and dielectric constant without a corresponding increase of rain rate This well known problem is usually handled by retrieving the Vertical Profile of Reflectivity VPR and correcting the observed measures The algorithm developed by ARPA SIM for VPR retrieval and correction is currently under test in order to be implemented within the DATAMET system It is based on the computation of mean VPR shape Germann and Joss 2002 and assuming it to be uniform in the whole radar domain on the retrieval of the reflectivity at the desired lev
11. whereas the 12 and 24 h accumulations are summed up from 15 minute measurements but corrected with rain gauge data All above implies that more probably the quality and error of rainfall and rain rate accumulations is differing from one country to another and cannot be homogeneously characterized Conclusion of the questionnaire Maintenance All the contributors declared the system are kept in a relatively good status Correction factors for error elimination These correction factors are diverse in the countries not homogeneous distribution of correction methods gt all contributors compensate for non meteorological echoes Clutter gt RLAN interferences implemented in Hungary Slovakia in development gt Poland and Slovakia correct attenuation In other countries it is not accounted for gt Some of the countries are testing new procedures for dealing with VPR Italy and Partial Beam Blockage PBB effects VPR Vertical Profile of Reflectivity used in Turkey This means that the corresponding rainfall estimates are diverse and the estimation of their errors cannot be homogenized However each county can provide useful information of the error structure of its rainfall products based on its own resources e g if they have already defined Quality Indicators or estimations of errors based on studies of comparison of radar and rain gauge data in the country itself In the future possible separation of reliable and quasi
12. 1 1 Date 30 09 2011 Page 159 177 Mean Error Multiplicative Bias r Hraingauge Graingauge E radar E radar Ww OINCA OINCA 0 25sPR lt 1 1SPR lt 10 PR210 PR20 25 0 25sPR lt 1 1 lt PR lt 10 PR210 PR20 25 Class mm h Class mm h Relative RMSE Correlation Coefficient 1200 1000 o 800 Graingauge G raingauge w 600 E radar E radar O INCA O INCA 400 200 0 0 25sPR lt 1 1sPR lt 10 PR210 PR20 25 0 25sPR lt 1 1 lt PR lt 10 PR210 PR20 25 Class mm h Class mm h Probability of detection False alarm rate 0 9 0 85 a 0 8 Hraingauge o H raingauge Q H radar lt W W radar 0 75 OINCA OINCA 0 7 0 65 RR 2 0 25 mm h RR21mm h RR 2 0 25 mm h RR 2 1 mm h Threshold Threshold Critical success index G raingauge G E radar O INCA RR 2 0 25 mm h RR 2 1 mm h Threshold Figure 110 Comparison of selected statistical scores for the PR OBS 2 product obtained by different ground reference data valid for event 1 convective The EUMETSAT Network of Satellite Application Facilities HSAF Support to Operational Hydrology and Water Management Product Validation Report PVR 02 Product H02 PR OBS 2 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 160 177
13. Figure 3 Geometry of cross track scanning for AMSU cccccssscccceeseccccesececeeescceeeesececseenecessueeeeeseuaeees 16 Figure 4 Flow chart of the AMSU MHS precipitation rate processing chain ccsseccccsssececeeeeeeeeeeneees 17 Figure 5 Structure of the Precipitation products Validation team c ccescccccsssececesececeesececeeeeeeeeeeneees 18 Figure 6 The network of 3500 rain gauges used for H SAF precipitation products validation 21 Figure 7 The networks of 54 C band radars available in ther H SAF PPVG ccceseccccsssececeeeeeeeeeeseees 23 Figure 8 Geometry Geometry of cross track scanning for AMSU cccccccsssscceeessecceceseceseeeeeeeeeeneces 25 Figure 9 Left Gaussian filter Right section of gaussian FiITEL ccc eeeseccccceeeseeeceeeeeeeeeeeeeeeeseeeeeeeas 25 Figure 10 Main steps of the validation procedure in the PPVG cccccsssececcessececeesececceeseceseueeceesenaeees 28 Figure 11 Rain gauge networks in PPVG cccccesecccessccceesccceenceeeescecseseeceeceeeeeeeseueeseeaeesseaeesseeetseaeetsees 33 Figure 12 Correlation coefficient between raingauge pairs as function of the distances between the gauges Colours refer to the months of the year 2009 ccccccssececesececeecceeeeseceeseeeeeeseseeneeeeeaeeeeeneeees 33 Figure 13 Radar networks in PPVG ccccccssscccesscccsscecsesceceescecseseeceescesseneeeseeesseaeeeeeaeesseaeesseeeeseneete
14. ME 0 38 mm h MAE 1 5 mm h and rain gauge ME 1 61 mm h MAE 1 9 with a standard deviation respectively of 2 21 mm h and 1 75 mm h 6 2 3 The summer period Version 2 2 lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt Imm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h radar__ radar___ radar__ radar__ radar__ gauge gauge gauge gauge gauge __ gauge _ Table 42 The main statistical scores evaluated by PPVG for H02 during the summer period The rain rates lower than 0 25 mm h have been considered as no rain In Table 42 it can be seen that the scores obtained by radar data are quite similar to the scores obtained by rain gauge data for all precipitation classes The RMSE for the light precipitation has the highest value as during the other seasons A general precipitation underestimation by HO2 is reported in table 42 using both rain gauge and radar data for rain rate greater than 1 mm h Besides a precipitation overestimation by HO2 has been found for light precipitation rain rate lt 1mm h using radar data The spring average RMSE evaluated using radar data have been RMSE Cl1 226 CI2 127 Cl3 66 and using rain gauge RMSE Cl1 235 Cl2 109 Cl3 84 The worst statistical scores during this season have been obtained over coastal areas by Tur
15. Moreover synoptic cards from UK MetOffice have been taken for understanding the meteorological situation Comparison Comparison of H02 product and rain gauge can be seen in next two figures Values of HO2 product are between 0 25 to 8 50 mm h but they vary from 0 25 to 4 00 mm h for gauge Main patterns of product and gauge are similar except western part of product pattern in the following figure HO0O2 RAIN RATE mm h 20 10 2010 10 44 GMT 42 05 lt a 41 04 PN En dn A ii aaar 7 Rain rate mm h B E S 39 04 z MM 0 25 to 1 00 go oe 2 MJ 1 01 to 2 00 lt EOE B 2 01 to 4 00 37 0 ee es E 4 01 to 6 00 36 0 _ E 6 01 to 8 50 Ta m ee 35 0 ee 5 ian j f I 26 5 28 5 30 5 32 5 34 5 36 5 38 5 40 5 42 5 44 5 LONGITUDE RG RAIN RATE mm h 20 10 2010 10 44 GMT 42 04 _ ae r 41 045 i Ae 40 0 a wu ane a Rain rate mm h A 39 04 HM 0 25 to 1 00 eed 4 38 04 e L B 1 01 to 2 00 lt E E T MM 2 01 to 4 00 l J a ea SN 37 0 ee 4 01 to 6 00 36 0 E 6 01 to 8 50 35 0 T 7 i a i f i i i f I 26 5 28 5 30 5 32 5 34 5 36 5 38 5 40 5 42 5 44 5 LONGITUDE Figure 91 Comparison of H02 product and rain gauge Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 114 177 Product Validation Report PVR 02 According to ME mean error
16. Q gt 2 Sean c O oO cD on 4 c cD 8 Sas co a N md T 00 25 0 251 0 110 Rain rate RG mm h Figure 86 Percentage distribution of PR OBS 1 precipitation classes in the rain classes defined using rain gauges RG data on the 27th of September 2010 Conclusions The analysis performed for situation with stratiform precipitation showed reasonably good ability of HO2 product in recognition of precipitation however the product tends to underestimate the precipitation areas and has some difficulties with proper recognition of rainfall maximum The stratiform rain rate is underestimated especially for light precipitation For the moderate rainfall the underestimation is not so strong 5 7 Case study analysis in Slovakia 5 7 1 Case study August 15 2010 Description During the day a cold front was moving over Slovakia territory towards North East next figure The cold front was accompanied by thunderstorms and occasional torrential rainfall causing severe floods in some river catchments in the western half of Slovakia nede a 15 08 2010 00 00 UTC Figure 87 Synoptic situation on 15 August 2010 at 0 00 UTC Data used Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 110 177 The EUMETSAT ae Product Validation Report PVR 02 etw Satellite Applic Focilitie The H02 v2 2 data from two temporally close satelli
17. River Nei e Oder Spree and Elbe catchments Description At 7 August 2010 there was a baroclinic zone reaching from the Baltic sea across Poland and Czechia until Austria where sub tropical air was advected from south to north at the eastern flank of the associated low pressure During the 7 3 August 2010 the precipitation reached about 35 mmh 150 mm in 48 hours in parts of Germany especially in Saxony causing floods in the upper parts of the rivers Nei e Spree and Elbe with catastrophic damages Samstag 07 08 2010 moz Hammer Bor nth So eA B DE fa The TJ Paap of eee Ae al ib pi A paa a i d BNW gS 9 Dy PA ran A 42 erg a fy n Ss Zt ae it ae AR E Bic a a we of 01 5 AS Le agis a age 4 s ae A oe Gi X E LJ Gebauer A Precipitation in mm sys gauge error corrected for B 9 August 2010 Figure 58 two day totals ending at 9th August 0 UTC interpolated on a 1 x1 evaluation grid as derived from SYNOP messages Global Precipitation Climatology Centre GPCC operated by DWD gt Zur Rolle des Starkniederschlages am 7 9 August 2010 im Dreilandereck Polen Tschechien Deutschland bei der Entstehung der Hochwasser von Nei e Spree und Elbe Bissolli at all Rapp Friedrich Ziese Weigl Nitsche Gabriele Malitz Andreas Becker Floods in Eastern Central Europe in May 2010 FU Berlin 2010 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 The EUMET
18. VRBOVCE a NIZNY_KOMARNIK pmm RAZTOCNO mm NIZNA_POLIANKA mumm ZLATA_BANA pmm STOS KU SENOH VALASKA BELA pmm MODRA PIESOK n BANSKA_STIAVNICA p VIGLAS_PSTRUSA m LIPTOVSKA_OSADA VYSOKA_NAD_UHOM fl KREMNICKE POHRONSKA_POLHORA mmm 2 gt x 3 q gt e Q a a ao 9 a DEMANOVSKA_DOLINA JASNA Figure 96 Distribution of rain gauges according their altitude above the sea level To simulate terrain visibility by meteorological radars Shuttle Radar Topography Mission SRTM data were used as an input into radar horizon modeling software developed in SHMU Details about SRTM can be found at http en wikipedia org wiki Shuttle Radar Topography Mission or directly at http www2 jpl nasa gov srtm SRTM model provides specific data set of terrain elevations in 90 m horizontal resolution in the whole HSAF area where HSAF validation by radars is performed Modelling software parameters were adjusted for single radar according real scanning strategy Radar Site Maly Javornik Kojsovska hola Tower height 25m 25m Range 1200pixels 240km 1200pixels 200km Resulted resolution 200m pixel 166 67m pixel Min elevation 0 1 deg 0 8 deg Refraction 1 3 standard atmosphere 1 3 standard atmosphere Elevation step 0 01 deg 0 01 deg Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 rvactogr ard woe Product H02 PR OBS 2 Date 30 09 2011 Page 144 177 The EUMETSAT PRE orca Product Validation
19. ae i tiki dh gH t E Prihti g k tyt t i HERE GE SEE EUr EE ERRE gh f tpi HE iths i o f EE id i i i f FE SEEE SETENE Bett HEE te Bs et H tett t i E s ATE Fi t d s TREF Fy i tF t t t f pee EE Hit fip pi hitii i Hite es H a see 4 at 1 rH ai fs i TF ee ae tt i SH FF t e a TE e eL aoe e t att Hite A i t 2 a a a i ner E eit H t 27 F Di E 4t it cs t 3 p P f i 7 a i ai i H i t i oi 3 t 4 4 4 4 a t a t 2 4 4 a 2 4 a 2 a t 4 s t 4 0 200 400 600 800 1000 1200 1400 Rain gauge elevation above the sea level m Figure 100 Scatterplot of log R G versus station altitude shows general underestimation of precipitation by radar Log RiG log Radar Gauge versus elevation of radar beam above the rain gauge 3 aa y 1E 06x 0 001x 0 5292 2 Eti A t t4 H 1 a a Pc SG i r eae pe pt h eg t t t f iH K bi t a H t t t H f j K E 7 H A t i ri i H i H TEMIS t ii i z i t org t siot ot FE eure t t t 4 i H LH BS 4 t 1 hi g 2 f ely E t f t t T t tet r 4 i t i i t t4 4 t lt a e e F Ge F 0 t it i F Ef i H i z et ii sce ES HINE i g 3 i i 3 t4 E P E a E F
20. rain gauge adjustment Radar data are available within 5 minutes around the satellite passage Comparison Here are three examples of H02 files compared with radar data upscaled to the same grid The first is in the morning of August 23 the second around noon and the third one refers to the early morning of August 24 ia j dt Figure 49 H02 image of August 23th 2010 at 2 35 left compared with upscaled radar at the same time right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 Figure 50 H02 image of August 23th 2010 at 12 23 left compared with upscaled radar at 12 25 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 The EUMETSA Doc No SAF HSAF PVR 02 1 1 cote epic Product Validation Report PVR 02 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 82 177 Sy 1 LETT s RTT IA PAG KEI BEE p IT TN PHL A an ee RE E F F Figure 51 H02 image of August 23th 2010 at 12 23 left compared with upscaled radar at 12 25 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 We can see that in all the shown cases the satellite is able to detect the presence of rainfall in the area but it tends to underestimate the extension and the amount of it Scores evaluation The score evaluation results Table 1 are not as good as the other summer case They a
21. score in Table 37 there is no any underestimation or overestimation but HO2 product has higher rain rate values than rain gauge values in next figure H02 rain rate mm h O N WO FOLD N CO CO RG rain rate mm h Figure 92 Scatter diagram of rain gauge and HO2 product Red line is 45 degree line Statistical scores Statistics scores can be seen from Table 37 Correlation coefficient is 0 54 for HO2 product POD FAR and CSI are respectively 0 64 0 04 and 0 62 for this case study MAE MB CC RMSE URD POD FAR CSI Ns NR ME SD 494 752 0 01 151 1 12 0 99 0 54 151 132 0 64 0 04 0 62 Table 37 Statistic scores for H02 Conclusions HO2 product is not well enough to catch rainy area In other words frontal system is not well described generally by this product algorithm in terms of areal matching and quantitative estimate 5 9 Conclusions Eleven case study analysis of HO2 have been here reported for 2010 Stratiform and convective precipitations during summer and winter periods have been analysed in different countries Rain gauges with 10 minutes refresh time radar data and nowcasting tools have been used to highlight different characteristics of the satellite product The case studies here proposed have pointed out that different statistical score values are obtained during summer and winter period In summer when more convective events occur all the countries have
22. 0 20 Correlation coefficient 0 13 Root mean square error 0 80 URD RMSE 0 88 POD 0 19 FAR 0 37 CSI 0 17 Table 23 Scores obtained with the comparison with radar data in mm h These results unlike the summer case show performances lower than the ones of the long period analysis with low probability of detection high false alarm ratio and large underestimation It can be added that the radar data cumulated over 24h revealed to be underestimated compared with interpolated rain gauge data This of course only worsens the conclusion about the product The time evolution of the fraction area with rain the average rain rate over this area the Equitable Threat Score ETS and the root mean square error RMSE is reported in next figure gt 0 25 mm h O Fraction area gt 0 25 mm h Precipitation RMSE Figure 56 Time evolution of fraction area with rain average rain rate over this area threshold 0 25 mm h RMSE and ETS during the present case study Conclusions From the visual and statistical comparison with radar data it appears that the HO2 product fails to reproduce the rainfall patterns and amount in this winter situation Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 vac a Woe Product H02 PR OBS 2 Date 30 09 2011 Page 86 177 Th a arts pan sotene Avice amp H SAF Product Validation Report PVR 02 5 3 Case study analysis in Germany BfG 5 3 1 Case study August 7 2010
23. 1 lt Step THEN Obser flag is Good IF Obser Obser 1 gt Step THEN Obser flag is Suspect Step threshold is determined again for each site on a monthly basis For each site the dataset containing the absolute difference of the sequential observations is determined by considering the observations for the matching month The 99 9 cumulative histogram value of the dataset is set as the Step threshold for the related site and month Persistence Test Persistence test is used to determine if any group of observations are due to instrument failures The test procedure applied is defined as IF T lt A THEN Flag for all Obser in T Good IF T gt A THEN Flag for all Obser in T Suspect where T is the total number of the sequentially repeating observations forward in time and A is the possible maximum number of sequentially repeating observations As in the other two tests A threshold is determined for each site on a monthly basis For any site the data belonging to the same month is taken into account to determine the repeating number of the sequential observations Then 99 9 cumulative histogram value of the repeating number dataset is assigned as the A amount for the corresponding site and month Since there is a high possibility of no precipitation data zero the sequential zero observations are excluded in this test during the determination of the A threshold amount and application of the test QA Test
24. 1 Rain gauge The network The maximum number of available raingauges for this project is 37 distributed over 3 basins The average distance between stations is about 7 km with a very high variance Generally in the plain area distance is lower than in the mountainous areas Watershed of r Iskar up to station Novi Iskar eteorological stations precipitation stations climatic stations rivers Figure 18 Distribution of the raingauge stations of Iskar River Basin Goudenhoofat E and L Delobbe 2009 Evaluation of radar gauge merging methods for quantitative precipitation estimates Hydrol Earth Syst Sci 13 195 203 ee Product Validation R PYRO Doc No SAF HSAF PVR 02 1 1 Sotellite seelcaion roquc alidation Report Facilities a H S AF p Support to Operational Issue Revision Index 1 1 aide i Product H02 PR OBS 2 Date 30 09 2011 Page 49 177 i conventional af a conventidnal automatic rivers Figure 19 Distribution of the raingauge stations of Chepelarska River Basin Watershed of r Varbitsa up to sp Djebel Figure 20 Distribution of the raingauge stations of Varbica River Basin The instrument The following information should be provided in this section Tipping bucket with heating measures the precipitation with increments of 0 1 mm quality index of the measurements between 1 and 10 7 8 Weighing type measurement with heating rim measures the p
25. 4 Case study analysis in Hungary OMSZ ccccccccsssseccccccessecccceeeeeseccceeeeeeeecceseaeeeceesseeeaeceeeeeas 98 5 4 1 Case study July 18 PA OAE O EEIE EEE EPERE AE A PTE E A AAT 98 5 4 2 case study septermniper Z010 ercraa ee ee E E eee 99 5 5 CASE study analysis in Italy Uni FO ssssessssssesssernsessrrrrssssrrrresssrreresssrreressrrrrressrrereessrrereeseene 100 5 5 1 Case study July 06 2010 ceccccccsccccscsesesesesescscsescscscscscscacacscacacacacacacecacacacacacacecececececeees 100 5 6 Case study analysis in Poland IMWDM ccccccccsssseccccceessecccceeeesecceceeeeeseeeeeseeeaeeeeeeeegeeeeeeeeas 103 5 6 1 Case study August 15 2010 cceccccccsssesesesesesescscscscscscscscscscacacacacacecacacacececacececececerenes 103 5 6 2 Case study September 27 2010 ccccccscsssssssssscsescscsescscscscscecscecececececececececececececececeres 106 57 Casestudy analysis in SIOVAK Ae sissacscasacesisestgssteeisesieasteesestgariteseaisensaieenantaeniesties 109 5 7 1 Case study August 15 DONO EEE E AE EA E E T 109 Bee Casestudy analysis in TUKEY ITU vercccsccsacztecsssaunecesadsaacetesancaeseaubedssaaetecsausnueuesaisadehacesacmaciin 112 5 8 1 Case study October 20 2010 ccceccccscscesesesesesescscscscscecscscscecacecacacacececacacacecacecececececeees 112 5 9 Conclusions cccesecccsseccceseccceececeececensceceececaecesausceseueceseeuceseucessuncesseuceseeacessencesseneesseaeeeseaess 114 6 Validati
26. 5 km Also the False Alarm Rate FAR is different for RADOLAN it was 0 49 for rain gauge 0 44 worse than for whole August 2010 For both kinds of ground data there were no valid pairs in the class RR gt 10 mmh so that for this class we have no statement on validation The EUMETSAT Doc No SAF HSAF PVR 02 1 1 sarale Aaea Product Validation Report PVR 02 B rece m SA Issue Revision Index 1 1 oo Product HO2 PR OBS 2 Date 30 09 2011 Page 88 177 066 037 0 00 054 035 0 00 _ 0 00 FAR 047 063 0o98 035 051 075 SI Table 25 Results of the categorical statistic of the validation for whole August 2010 In comparison with categorical statistic of the whole August 2010 we can see that in case of the 7 August we got worse results for POD in all classes for validation with radar data for validation with gauge data it is the converse Better results generally were received by validation with radar data For 7 August the validation with both kind of ground data provide a POD in the second class 0 37 radar 0 35 rain gauge less than the FAR 0 51 radar 0 5 rain gauge The Critical Success Index CSI is more stable and differs only by 1 3 percent between the different validation methods A CSI of 0 41 0 38 means that 41 resp 38 of the predictions H02 of precipitation gt 0 25 mmh of all predicted observed rain events are correct Next two figures show the contingency table of four cla
27. 7 evaluated by comparing H02 with radar data ge ap xo N The averages of POD 0 47 FAR 0 47 and CSI 0 33 have been obtained using radar data on one year of data 1 December 2009 30 November 2010 In table 46 it is possible to see that 98 of no rain is correctly classified by H02 There is a general precipitation underestimation by H02 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 123 177 Product Validation Report PVR 02 6 3 2 rain gauge validation peg ee ce eee ith mourn 0 11 0 09 0 11 0 13 0 18 0 93 ith RR gt 0 25mm n 0 31 0 33 0 34 0 45 0 49 0 47 0 46 0 47 0 36 0 36 0 30 0 32 h T 0 10 0 08 ry ee rors 0 26 0 26 0 29 A h eae 0 13 015 014 0 36 0 25 0 27 0 18 0 18 ith Ret mm h 0 50 0 50 0 46 0 57 0 66 0 59 0 57 0 53 0 43 0 48 0 44 0 43 0 52 ith RReimmpn 0 11 0 10 0 12 0 13 0 11 0 18 0 19 0 26 0 28 0 20 0 22 0 16 0 15 Table 47 The averages POD FAR and CSI deduced comparing H02 with rain gauge data a Radar data mm h PR lt 0 25 0 25 lt PR lt 1 00 1 00 lt PR lt 10 00 10 00 lt PR ozerao 1 re 10 00 lt PR Table 48 The contingency table for the three precipitation classes defined in table 7 evaluated by comparing H02 with rain gauge data ge ap xo Y The averages of POD 0 18 FA
28. All these activities will be developed and coordinated inside the Working Groups Annex 1 7 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 32 177 Product Validation Report PVR 02 4 Ground data used for validation activities 4 1 Introduction In the following sections the precipitation ground data networks used in the PPVG are described radar and rain gauge data of eight countries Belgium Bulgaria Germany Hungary Italy Poland Slovakia and Turkey H02 has been submitted to validation in all these countries except Bulgaria Until now the Bulgarian data are used only for HO5 validation activity according to the Project Plan Their use in the next months is under consideration It is well know that radar and rain gauge rainfall estimation is influenced by several error sources that should be carefully handled and characterized before using these data as reference for ground validation of any satellite based precipitation products In the last months working groups Annex 1 2 3 4 5 and 7 have been composed in order to provide complete information on the ground data characteristics and to evaluate the associated errors In this chapter a complete analysis of the ground data available in the PPVG is reported by the rain gauge and radar data in PPVG summaries Section 4 2 and 4 3 the Rain gauge and radar data integrated products in PPVG first report Section 4 4 and a co
29. Group have been using a variety of different strategies to treat gauge data and to compare them with satellite estimates Some are using interpolation algorithms to get spatially continuous rainfall maps while others process directly the measurements of individual gauges All the data in the network except for cold months in Poland are quality controlled there is no information about the techniques used but usually quality control rejects data larger than a given threshold and in case of too high rainrate difference exceeding given thresholds among neighbouring gauges and between subsequent measures of the same instrument Table 10 summarizes the data pre processing performed in different Countries while Table 11 reports the different matching approaches for HO1 HO2 respectively As for the temporal matching the used approaches are rather homogeneous within the Groups instantaneous measurements are matched with next ground cumulated values over the different available intervals ranging from 1 minute Turkey to 1 hour Italy Germany Cumulated estimates obviously are compared to ground measured rain amounts over the same cumulation intervals As for spatial matching different approaches are considered also taking into account the different Spatial structure of the satellite IFOVs Two basic ideas are pursued pixel by pixel matching or ground measure averaging inside satellite IFOV The second approach seems to be more convenient especial
30. PR OBS 1 Precipitation rate at ground by MW conical scanners with indication of phase PR OBS 2 4 02 Precipitation rate at ground by MW cross track scanners with indication of phase PR OBS 3 H H Product Validation Report PVR 02 Precipitation rate at ground by GEO IR supported by LEO MW PR OBS 4 04 Precipitation rate at ground by LEO MW supported by GEO IR with flag for phase PR OBS 5 1 05 Accumulated precipitation at ground by blended MW and IR PR OBS 6 Blended SEVIRI Convection area LEO MW Convective Precipitation Instantaneous and accumulated precipitation at ground computed by a NWP PR ASS 1 model SM OBS 2 Small scale surface soil moisture by radar scatterometer SM OBS 3 Large scale surface soil moisture by radar scatterometer SM DAS 2 SN OBS 1 Table 1 H SAF Product List SN OBS 2 SN OBS 3 SN OBS 4 2 Introduction to product PR OBS 1 2 1 Sensing principle Product PR OBS 2 is based on the instruments AMSU A and AMSU B or MHS flown on NOAA and MetOp satellites These cross track scanners provide images with constant angular sampling across track that implies that the IFOV elongates as the beam moves from nadir toward the edge of the scan see next figure The elongation is such that e for AMSU A the IFOV at nadir is 48 x 48 km at the edge of the 2250 km swath 80 x 150 km e for AMSU B and MHS the IFOV at nadir is 16 x 16 km at the edge 27 x 50 km Doc No SAF HSAF PVR 02 1 1 Issu
31. Poland Slovakia and Turkey will be shown in the next sections The country validation results are here reported in order to respond not only to the question whether the product meets the requirements or not but also where meets or approaches or fails the requirements The average performance of H02 for all sites is presented in a compact synoptic way in this chapter The contents of the monthly statistical scores have been provided by the individual Countries Teams and verified by the Validation Cluster Leader step by step as described in the Chapter 3 As stressed in Chapter 4 the average scores reported in the following tables have been obtained on measurements collected in heterogeneous geographical orographical and climatological conditions 6 2 The continuous statistic There are three sets of columns e one set for Countries Teams that has compared satellite data with meteorological radar in inner land areas Belgium BE Germany DE Hungary HU and Slovakia SL and their average weighed by the number of comparisons e one set for three Countries Teams that has compared satellite data with rain gauges in inner land areas Italy IT Germany DE Poland PO and Turkey TU and their average weighed by the number of comparisons e one column for Turkey TU that has compared satellite data with rain gauges in coastal areas In order to highlight the seasonal performances of HO2 the statistical scores have been presented not only for yearly
32. Proceed of 5th European Radar Conference ERAD Helsinki Finland http erad2008 fmi fi proceedings extended erad2008 0270 extended pdf Slovakia D Kotl rikov J Kali k and I Strmiska Radar horizon modelling as a requirement of SHMI radar network enhancement Physics and Chemistry of the Earth Volume 25 Issues 10 12 2000 Pages 1153 1156 First European Conference on Radar Meteorology doi 10 1016 S1464 1909 00 00170 2 Poland Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 141 177 Product Validation Report PVR 02 Product H02 PR OBS 2 Szturc J O r dka K and Jurczyk A 2008 Parameterization of QI scheme for radar based precipitation data Proceedings of ERAD 2008 http erad2008 fmi fi proceedings extended erad2008 0091 extended pdf Szturc J O r dka K and Jurczyk A 2009 Quality index scheme for 3D radar data volumes 34 Conf on Radar Meteorology Proceedings AMS 5 9 10 2009 Williamsburg VA USA Katarzyna Osrodka Jan Szturc Anna Jurczyk Daniel Michelson Gunther Haase and Markus Peura Data quality in the BALTRAD processing chain Proceed of 6th European Radar Conference ERAD 2010 Sibiu Romania http www erad2010 org pdf oral wednesday dataex 06 ERAD2010 0240 pdf Szturc J O r dka K and Jurczyk A Quality index scheme for quantitative uncertainty characterization of radar based precipitation Meteorological Applications 2010 do
33. Radar 8 an gage O E Rede Rain gauge interpolated f O oo a adjusted radar ee IMWM S TSMS Table 61 Errors of the ground reference provided by all validation groups The values of table 64 apart from details indicates that the errors due to the ground reference are of the same order than the threshold requirements It is interesting to note that the validation activity has indicated that the results from rain gauge and radar are comparable whereas the error of radar Should be definitively higher This means that radar is favored in the third error type RMSEcomparison RMSEcomparison iS in reality a Composition of several errors It refers to the limitations of the comparison method that in spite of all efforts envisaged and implemented by the validation teams has left residual errors difficult to be further reduced but needing evaluation by in depth investigation A short list is upscaling downscaling processes to make compatible the instrument resolution and the ground station representativeness have been applied for instance by applying Gaussian filters but the Statistics of residual errors are not available this problem affects radar to a minor extent than raingauge that may explain why comparisons with radar finally are not worse than with rain gauge the raingauge s representativeness of IFOV Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 173 177 P
34. Van de Vyver H and E Roulin 2008 and A Rinollo An optimization of this code to be used by all the partners of the PPVG represent one of the next step Anyway different approaches over different Countries are leading to very similar values in the considered skill scores indicating probably two things 1 none of the considered approaches can be considered as inadequate and more important 2 the differences between ground fields and satellite estimates are so large that different views in the data processing do not results in different numbers 4 3 Radar data in PPVG In this section the complete inventory of the radar data used in the PPVG with some considerations are reported as first results of the Radar WG Annex 3 4 3 1 The networks In the HSAF project satellite based precipitation estimations are compared regularly with the radar derived precipitation fields However radar rainfall products are influenced by several error sources that should be carefully analyzed and possibly characterized before using it as a reference for validation purposes However we have to emphasize that the radar data used for validation purposes is not developed by the validation groups themselves They are developed within specialized radar working teams in many Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 tycka a Waler Product H02 PR OBS 2 Date 30 09 2011 Page 37 177 The EUMETSAT Satellite Applealien y j i _ VR 2 ieote
35. can be removed from the radar data measurements effectively however not all of them have dual polarization which would be important to correct rain path attenuation Scan strategies We have explored the scan strategy for each of the radars used In this matter all countries have shared their information on the number of elevations minimum and maximum elevations scan frequency maximum nominal range distance and range resolution Height Azimuth lt Volume Scan Procedure Figure 95 Volume scan procedure We can conclude that the scan frequency ranges from 5 minutes in Belgium Germany and Slovakia to 10 minutes in Turkey and Poland and 15 minutes in Hungary and varying frequency for Italian radars The number of elevation stays between 4 and 15 in average around 10 The range distance used is 240 km in general But in some places in Italy and for the Turkish radars the maximum range distance used is 120 km or even less e g 80 km Range resolution is 250 m in Belgium 250 340 225 and sometimes 500 m for the Italian radars 500 m for one of the Hungarian radars and 250m for the other two Polish radars can work with 125 m and 250 m resolution and in Turkey it is 250 m for all the radars All in all the scan strategies within the PPVG countries are well balanced and similar to each other though they vary from one radar to the other even within countries All radars are regularly maintained and calibrated which is a go
36. citar tec td tsetse aceite ssa rf tate ti lets ett lcs tpi 95 Figure 70 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 12 06 02 18 UTC and for RADOLAN RW left filled raster 2010 12 06 02 50 UTC and station data right dots ZOOM T2206 CFO UTE ee E 95 Figure 71 Contingency table statistic of Rain Rate mm h for H02 vs radar data Left for 5 6th December Right Tor whole December 20l0rcreriomieniecanisen a 96 Figure 72 Contingency table statistic of rain rate mm h for HO2 vs rain gauge data Left for 5 6th December Right for whole December 2010 ceecccccssseccccessececeeeccceeeececeeeeccesseneceeeeueceseeaeeeesegaeees 97 Figure 73 Synoptic chart at OO UTC on 18 July 2010 20 eccccsseeceeeeeceeseeeeeeeeeeeeeeeeaeeeeeeeeseneeeeees 98 Figure 74 HO2 product left panel Precipitation rate from the Hungarian radar network at its original resolution right panel at 00 30 UTC at 2 15 UTC and at 12 UTC oe ceeccccceeseceseeeeeseseeeeeeseeeeeess 99 Figure 75 Synoptic chart at 00 UTC on 10 September 2010 ee eccccseseccccessececceeeseeeseusceesaeeeeesseees 100 Figure 76 Precipitation rate from the Hungarian radar network at its original resolution at 11 UTC right Daniel HO2 product at 11 UTC Vert Panel cseccisscessactecnansennsncenevsansneaseesaneiaesanmneianeasatiecmnmeemiaeaer 100 Figure 77 HO2 precipitation map at 12 16 UTC top left at 12 27 top right and raingauges hourly precipitation cum
37. county can provide useful information of the error structure of its rainfall products based on its own resources The Radar WG Annex 3 is now working to define quality index static or dynamic in order to select the more reliable radar fields and to associate an error structure to the radar data Quality information should take into account the radar site geographical areas event type radar products The study performed by the Slovakian team Annex 4 and the scheme published by J Szturcn et all 2008 on the quality index evaluation are under consideration by the Radar WG In the future the satellite product testing will be carried out using only the data having a sufficient quality but the validation results showed in this document have been obtained using radar data which passed only data owner institute controls 500 1 000 km Lood a PPV Countries 5 Weather radar units O Horizontal beam extent of 100 km Horizontal beam extent of 200 km 0 10 E 20 E 30 E 40 E Figure 7 The networks of 54 C band radars available in ther H SAF PPVG Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 24 177 Product Validation Report PVR 02 The studies that have been carried out in the PPVG on comparison of radar data with rain gauge data have shown that RMSE error associated with radar fields depends considerably on radar minimum visible height above the rain gauge in mountainous terr
38. detection The resolution of the radar data used for validation is 2km by 2km This is true for the accumulated and the instantaneous products as well As We have already mentioned the threshold of detection in Hungary is 7dB Hungarian radar data is available operationally in stereographic S60 projection References P ter N meth Complex method for quantitative precipitation estimation using polarimetric relationships for C band radars Proceed of 5th European Radar Conference ERAD Helsinki Finland http erad2008 fmi fi proceedings extended erad2008 02 70 extended pdf 4 9 Ground data in Italy DPC Uni Fe 4 9 1 Rain gauge The network Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 56 177 Product Validation Report PVR 02 The maximum number of available raingauges is about 1800 irregularly distributed over the surface On the average however a number of stations have low quality data failure or data transmission problems and their data are missing 9999 recorded This number of no data stations is highly varying on hourly daily basis and ranges from few units to a hundred In case of data acquired but not transmitted recorded the first transmitted measure is the cumulated value over the time when the data were not transmitted The average minimum distance between closest stations is about 9 5 km with a very high variance in some regions such as Tuscany in
39. distance is defined as the minimum distance between two measures to get the correlation coefficient Pearson Coefficient reduced to e A recent study on the H SAF hourly data for Italy shows this decorrelation distance varies from about 10 km in warm months where small scale convection dominates to 50 km in cold months when stratified and long lasting precipitation mostly occur In figure 97 the value of the linear correlation coefficient is computed between each raingauge pair in the Italian hourly 2009 dataset as function of the distance between the two gauges 200812 200901 200902 200903 200904 200905 200906 200907 200908 200909 200910 200911 Figure 94 Correlation coefficient between raingauge pairs as function of the distances between the gauges Colours refer to the months of the year 2009 Assuming these values significant for the other Countries involved in this study we can conclude the distribution of gauges is capable to resolve the spatial structure of rain patterns only for stratified systems but it is inadequate for small scale convective events km Table 51 Number and density of raingauges within H SAF validation Group Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 132 177 Product Validation Report PVR 02 the number of raingauges could vary from day to day due to operational efficiency within a maximum range of 10 15 only in t
40. elevation up to 1500m 147 Figure 106 Final relative root mean square error map of radar measurements with regard to terrain visibility by current radar network Of SHMU ccceccsccsscecscecscssececcceccssccaeecasceaeceseceseseeessneensecueeaseeaees 147 Figure 107 Final mean error map of radar measurements with regard to terrain visibility by current radar network of SHM General underestimation of precipitation by radars is observed 0 148 Figure 108 Coverage of Europe by the INCA and RADOLAN SySteMS ccccccssssseceecseeeseeeeeseeeeeeeeeeeas 151 Figure 109 Procedure of the RADOLAN online adjustment hourly precipitation amount on 7 August 2004 a es 0 T Danner ee ne nn ne ne TE rn ne eee en ere ee ear ae rene 153 Figure 110 Precipitation intensity field from 15 August 2010 15 00 UTC obtained by a radars b interpolated raingauge data c INCA analysis and d PR OBS 1 Product ccccccsesssseceeeeeeseeseeeeeeees 155 Figure 111 Precipitation intensity field from 15 August 2010 6 00 UTC obtained by a radars b interpolated raingauge data c INCA analysis and d PR OBS 3 product 5 57 UTC supplemented with map of minimum visible height above surface level SHMU radar network ccccccscsseessseeeeeseeeeees 156 Figure 112 Precipitation intensity field as in previous figure except for 8 00 UTC ceccceeeeeeeeee ees 157 Figure 113 Comparison of selected statistical scores for the PR OBS 2 product
41. estimate of the errors due to the various effects impacting the RMSEcomparison is not difficult It is not necessary to build a large statistics but just perform experiments using a few campaigns carried out over one dense rain gauge network and one well calibrated radar In fact the purpose is simply to evaluate the size of RMSEcomparison not to reduce it that would require a large effort probably improductive For the sake of providing an example it is noted that if the three contributions RMSEsat RMSEground and RMSEcomparison were Of comparable size equipartitioning of the error would improve the RMSD by a factor 3 1 7 and the figures resulting from the current validation would match at least the threshold requirements In order to obtain an estimate of RMSEcomparison and then a more accurate estimation of RMSEsat CNR ISAC performed an experiment based on its polarimetric C band radar Polar 55C located close to Rome surrounded by a network of 14 rain gauges in an area of 14 km x 14 km approximately the pixel of SSM I at 85 5 GHz and of AMSU B MHS generally used for the radar calibration Assuming Polar 55C as reference the the spread of rain gauge measurements resulted as follows RR gt 10 mm h 50 1 lt RR lt 10 mm h 80 RR lt 1 mm h 150 A similar experiment with 2 rain gauges in reduced area of 5 km x 5 km approximately the pixel of SEVIRI at middle latitude shows similar results That s means
42. i _ J i p i EH Fa 5 ia 5 ee Ets eit ag rAr tea 4 4 t ral Pe I 7 3 pote eb br ol dpi agg fie g t 4 Po ths t tt ae k cee Bi 4 rH 4 na Pe ors es 17s 12 2 i p PI i j z t i ET Tik t i t t t ii bi f t a 2 t E F t t i t i r i i i t i A t 3 F t F t 4 4 4 4 1 4 4 4 L 4 1 1 1 4 4 4 L 1 4 4 4 4 4 4 L 4 L 4 0 200 400 600 800 1000 1200 Elevation of beam above the rain gauge m Figure 101 Scatterplot of log R G versus radar beam altitude shows increased underestimation of radar for high and close to zero radar beam elevations Polynomial trend line on the Figure 98 differs from trend line of Figure 101 While in case of rain gauge altitudes the lowest underestimation by radar can be observed for the lowest rain gauge altitudes in case of radar beam altitudes the lowest underestimation by radar is observed for radar beam elevation about 500m Stronger underestimation for rain gauges with close to zero radar beam elevation can be explained by partial signal blocking by terrain obstacles These are the cases when rain gauge station is close to the top of terrain obstacle Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 nya ona Waler Product HO2 PR OBS 2 Date 30 09 2011 Page 147 177 The EUMETSAT ste Aoi H SAF Product Validation Report PV
43. ia iaai Eno Tis 26 3 7 Temporal comparison of precipitation intensity eesseseseeeseseerssrreresrreresrrressreresereresrrresseerese 27 3 8 Large statistic Continuous and multi categorical sssesssensseessseessreresressrrrssrerssrrsseesssrreseeres 27 aF Ce a E E er ee eee ee en eee ee 31 IN OD E cassette spe saeco ots raadpleeg gic cathe ig ein nett gi selene cc ion cnbenpt oot p aoe orltseniay eh 31 Ground data used TOF validation activities xsssiascesesaneusasneasiananeaisuiaseiessiivevedsansesisbanguveuvaiosubnseniogeentess 32 dA IN OAC ON eeraa E EEr cnc Spindle ace gece ee ee et he ie te deste Go ioe do E EEE ETE 32 4 2 Pra lla Gate i PPV Gre cccacceaccanescnescnssoesecesesauesacesnssoneecsnes aos eeeeene sees scence aesacsaanesossaenanes sees eneuaneae 32 4 2 1 TAE CON ONS acces cas ts ct cscs E S 32 4 2 2 WAS WIS Cr UNS S ess acess race ctqecaceuscectqs seers races ec setaantnanans svraceus sare aca A A A 34 4 2 3 Data processing cccsccssccssccseccsccesecseceseceeeeseeseeceecsseeseeseeesseceseeeecsseeeseseeesaeeeesaeetseeeeeeasenaes 35 4 2 4 SO TINS COC SOS EEE E aaeit cts rae aad EEE E E 36 AS PRU ee eee canst cna nse snes see i nse eee ines EEEa ENEE EENEN 36 4 3 1 WANS TEV OIG erson AEAEE 36 4 3 2 The instruments ccecccesecccessceceeececeenceceesceeensceceusceseuscesaeeessescesseeessuaeesseneetseeeeseneetagaess 38 4 3 3 Data processing s ss ssesessesresrrsessessrsrrsresessessrereseosreresreseosessrere
44. is evident that the best visibility of SHMU radars corresponds to the lowest URD RMSE of 60 displayed by light violet colors URD RMSE is of quite homogeneous distribution with average of 69 in prevalent lowlands of Slovakia displayed by bluish colors But in central and north west mountainous areas this error exceeds 100 Figure 39 Map of relative RMSE left and Mean Error right over the SHMU radar composite Similar studies that have been carried out in the PPVG on comparison of radar data with rain gauge data have shown in general that RMSE error associated with radar fields depends considerably on radar minimum visible height above the rain gauge especially in mountainous countries In lowlands this dependence is not so significant but no negligible The reason can be the location of radar sites at the top of hills and impossibility of the lowest elevation to reach the lowland s surface In case of Slovakia The URD RMSE error of radar accumulated fields is between 60 90 with an average URD RMSE value of 69 3 Mean Error specified for 24 hours cumulated precipitation is 4 42mm or converted into instantaneous precipitation 0 184 mm h RMSE specified for 24 hours cumulated precipitation is 9 48mm or converted into instantaneous precipitation 0 395 mm h Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 71 177 Product Validation Report PVR 02 Complete SHMU study is avail
45. lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h 1 10mm h 104458 86797 1 10mm h 91409 239846 1 10mm h 0 53 1 66 1 10mm h 1 95 1 95 1 10mm h 1 56 2 04 1 10mm h 0 73 0 27 1 10mm h 0 30 0 21 1 10mm h 2 23 2 60 1 10mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h Table 44 The main statistical scores evaluated by PPVG for H02 during one year of data 1 December 2009 30 November 2010 The rain rates lower than 0 25 mm h have been considered as no rain The yearly averages obtained by all the countries using both radar and rain gauge data are similar The worst RMSE has been evaluated for light precipitation comparing HO2 precipitation estimations with radar data In this case there is a precipitation overestimation by the satellite product but in general a clear precipitation underestimation by HO2 is reported in tab 44 The yearly averages of RMSE obtained with radar data RMSE Cl1 222 Cl2 115 Cl3 69 and with rain gauge RMSE Cl1 174 Cl2 107 Cl3 87 A small Mean Error and Mean Absolute errors have been obtained for medium precipitation rain rate between 1 10 mm h with radar ME 0 53 mm h MAE 1 56 mm h and rain gauge ME 1 66 mm h MAE 2 04 with a standard deviation respectively of 1 95 mm h and 1 95 mm h Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 122 177 Product Validation Report PVR 02
46. lt 1mm h lt 1mm h lt 1mm h lt 1mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h Table 41 The main statistical scores evaluated by PPVG for H02 during the spring period The rain rates lower than 0 25 mm h have been considered as no rain In Table 41 it can be seen that the scores obtained by radar data are quite different from the scores obtained by rain gauge data for light precipitation rain rate lt 1 mm h Besides the RMSE of this precipitation class has the highest value This is due to the RMSE evaluated by Hungary An investigation on this result is in progress The statistical scores evaluated for precipitation classes 2 and 3 using both rain gauge and radar data are quite similar A general precipitation underestimation by H02 is reported in table 42 using both rain gauge and radar data for all precipitation classes The spring average RMSE evaluated using radar data have been RMSE Cl1 347 Cl2 145 Cl3 81 and using rain gauge RMSE Cl1 201 Cl2 108 Cl3 91 The shi ens eo Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 sac an Product HO2 PR OBS 2 Date 30 09 2011 Page 119 177 idati PVR 02 JE H SAF Product Validation Report 0 A small Mean Error and Mean Absolute errors have been obtained for medium precipitation rain rate between 1 10 mm h with radar
47. observed from the satellite and also not observed from the ground The scores evaluated from the contingency table are Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 30 177 Product Validation Report PVR 02 Probability Of Detection POD hits E hits on Range 0 to 1 Perfect score 1 hits misses observed yes POD False Alarm Rate FAR falsealarms _ falsealarms mil n Range 0 to 1 Perfect score 0 hits falsealarms forecast yes Critical Success Index CSI hits e e Range 0 to 1 Perfect score 1 hits misses falsealarm CSI Equitable Threat Score ETS hits hits random with hits hits misses falsealarm hits ETS ranges from 1 3 to 1 O indicates no skill Perfect score 1 _ observed yes forecast yes ETS random total random Frequency Blas FBI FBI ea E sO E Range 0 to ee Perfect score 1 hits misses observed yes Probability Of False Detection POFD falsealarms _ falsealarms POFD z correctnegatives falsealarms observed no Range 0 to 1 Perfect score O Fraction correct Accuracy ACC hits correctnegatives Range O to 1 Perfect score 1 total ACC Heidke skill score HSS HSS hits correct negatives ex pected correct naom with N ex pected correct andom ex pected correct andom lobserved yes forecast
48. obtained by different ground reference data valid for event 1 CONVECTIVE ccccccceeseccccesececeeeeesceeeecseeeuseseeeeneeeeseuausss 159 Figure 114 Comparison of selected statistical scores for the PR OBS 2 product as in previous figure EXEC OF CV el 4st UI OUI 05222045052 s5esesascnseasacesenceesesss4seaenssaieses T 160 Figure 115 The Wideumont radar image of 1 2 2010 cumulated rainfall in the previous 24 hours HMI SUN IG EON aos ce eases an sae we T 164 Figure 116 The Wideumont radar image of 1 2 2010 upscaled to the COSMO grid ccecce 164 Figure 117 The Wideumont radar image of 2 2 2010 cumulated rainfall in the previous 24 hours Ge alee aa eE a1 neem nn an rm nD 165 Figure 118 The Wideumont radar image of 2 2 2010 upscaled to the COSMO grid cccceceeeeeeees 165 Figure 119 The Wideumont radar image of 4 2 2010 cumulated rainfall in the previous 24 hours HAMAS VS SAC MIG FOC eieiei rea E E savanesateseceautete bederutebaocgeneietets 165 Figure 120 The Wideumont radar image of 4 2 2010 upscaled to the COSMO grid scce 166 Figure 121 Distribution of the monthly average H 05 3 h cumulated precipitation Mean Error calculated for July 2010 using three methods a Ordinary Kriging b Natural Neighbour and c IDW 2 Figure 122 Cross validation results obtained for three different methods for spatial interpolation 169 Figure 123 Example of sampled data for a regular grid In right on the upper part a deta
49. of raingauges within H SAF validation Group ccccccsseseceeseseeeeseeeeeeeees 34 Table 9 Summary of the raingauge characteristics cccccccseeccceececeeececeececeueceeeuecseeueceseeeceseuneeseeaeeees 34 Table 10 Data pre processing Strategies ccccccssccccssecccssececesececesececeeeceseeceseaucesseecessuscesseeesseaeetseneeees 35 Table 11 Matching strategies for comparison with HO1 and HO2 eseesssesssesseserrnserrssrrrserrrserererrrsereessees 36 Table 12 Inventory of the main radar data and products characteristics in Belgium Italy Hungary 41 Table 13 Inventory of the main radar data and products characteristics in Poland Slovakia Turkey 42 Table 14 INCA Questionnaire ccccecccesecceseccencccenecceecseeceeecseeceeueceeeceeueceeueseeeeceeeeceseeseeeeseeeeseeeeseeeass 46 Table 15 Precipitation data used at BfG for validation Of H SAF products cccccssseccccesseceeeeeeeeeeeneess 50 Table 16 Location of the 16 meteorological radar sites Of the DWD c cesscccceeseceecesececeeeeceeeaeneees 52 Table 17 Main characteristics of the Hungarian radar NEtWOFK cccccesseccceesseceeeeseceeceesececeeeeceeseuneess 54 Table 18 Characteristics of the three radar instruments IN Hungary ccccccssseccceesececceesececeeeceesaueeess 54 Table 19 Characteristics of the SHM radars cccecccesscescccecsceeceescecesccecesececeaeceaceseueceaeeeatecseeeeaseeereesaees 69 Table 20 QA fla
50. of the satellite product performances e indications to satellite product developers e indication on the ground data if requested availability into the H SAF project In the future the PPVG will test the possibility to present case study analysis in the test sites indicated by the hydrological validation team in order to provide a complete product accuracy and hydrological validation analysis to the users Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 nvaciogy are Wale Product H02 PR OBS 2 Date 30 09 2011 Page 77 177 The EUMETSAT PE rere Product Validation Report PVR 02 HSAF uct Validation Rep 5 2 Case study analysis in Belgium IRM 5 2 1 Case study August 14 17 2010 Description This event has been select because convective precipitation occurred during 14 17 August and covering large parts of the study area during 15 and 16 August A low was moving from Germany to The Netherlands next figure Warm air from Central Europe was lifted over oceanic cold air over the study area E N 7 7 Figure 43 Synoptic situation on 15 August 2010 at 6 UTC zoom in the surface map Data used Products H02 from August 14 at 6 00 UTC to August 17 at 18 00 UTC have been considered in this study The total is 32 satellite passages distributed as follows 4 in the afternoon of August 14 4 in the morning of August 15 7 in the afternoon of August 15 8 in the morning of August
51. on NOAA 18 and 19 and on MetOp MSG Meteosat Second Generation Meteosat 8 9 10 11 MVIRI Meteosat Visible and Infra Red Imager on Meteosat up to 7 MW Micro Wave NESDIS National Environmental Satellite Data and Information Services NMA National Meteorological Administration of Romania NOAA National Oceanic and Atmospheric Administration Agency and satellite NWC SAF SAF in support to Nowcasting amp Very Short Range Forecasting NWP Numerical Weather Prediction NWP SAF SAF on Numerical Weather Prediction The EUMETSAT Network Sotellite Application acilities Hines O3M SAF OMSZ ORR OSI SAF PDF PEHRPP Pixel PMW PP PR PUM PVR RMI RR RU SAF SEVIRI SHMU SSM I SSMIS SYKE Teg TKK TMI TRMM TSMS TU Wien U MARF UniFe URD UTC VIS ZAMG Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 13 177 Product Validation Report PVR 02 Product H02 PR OBS 2 SAF on Ozone and Atmospheric Chemistry Monitoring Hungarian Meteorological Service Operations Readiness Review SAF on Ocean and Sea Ice Probability Density Function Pilot Evaluation of High Resolution Precipitation Products Picture element Passive Micro Wave Project Plan Precipitation Radar on TRMM Product User Manual Product Validation Report Royal Meteorological Institute of Belgium alternative of IRM Rain Rate Rapid Update Satellite Application Facility Spinning Enhanced Visible and I
52. only with 15 minutes periods This provides satisfying results However there is still a need for rain gauge adjustment because there are obviously places behind mountains that the radar does not see The radars are corrected with rain gauge data every 12 hours The correction method using rain gauge data for 12 hour total precipitation consists of two kinds of corrections the spatial correction which becomes dominant in the case of precipitation extended over a large area whereas the other factor the distance correction factor prevails in the case of sparse precipitation These two factors are weighted according to the actual situation The weighting factor depends on the actual effective local Station density and also on the variance of the differences of the bias between radar and rain gauge measurements On the whole we can say that our correction method is efficient within a radius of 100 km from the radar In this region it gives a final underestimation of about 10 while at bigger distance the underestimation of precipitation fields slightly increases Besides we also produce 12 hour total composite images first the three radar data are corrected separately and then the composite is made from them The compositing technique consists of weighting the intensity of each radar at a given point according to the distance of the given point from the radars This is also true for the 24 hourly accumulations Resolution projection threshold of
53. procedure Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 73 177 Product Validation Report PVR 02 By applying the control procedures of the QA test mentioned above each individual precipitation observation receives three flags referring to the corresponding test For the corresponding observation if all the test flag is not Good then the observation is excluded from the validation process Use of spatialization technique Due to the time and space structure of precipitation and to the sampling characteristics of both the precipitation products and observations used for validation care has to be taken to bring data into comparable and acceptable range At a given place precipitation occurs intermittently and at highly fluctuating rates Various maps time series analysis statistical and probabilistic methodologies are employed in the validation procedure classically but some additional new aspects such as the spatial coverage verification model of point cumulative semivariogram PCSV approach Sen and Habib 1998 are proposed for usage in this work Each precipitation product within the H SAF project represents a foot print geometry Among these HO1 and H02 products represent an elliptical geometry while HO3 and HOS have a rectangular geometry On the other hand the ground observation rain gauge network consists of point observations The main problem in the precipitat
54. rainfall readings the higher one is taken into account No specialization technique is used for standard validation process However for some case studies the Natural Neighbor technique is applied for satellite and ground precipitation data To match the precipitation information with satellite data spatial and temporal matching are applied e Spatial matching for each given satellite pixel the posts situated within that pixel were found The pixel size was taken into account however its shape was assumed to be rectangular If more than one rain gauge were found within one satellite pixel the ground rain rate value was calculated as a mean of all rain gauges measurements recorded within that pixel e Temporal matching satellite derived product is combined with the next corresponding ground measurement As the ground measurements are made with 10 minute time resolution the maximum interval between satellite and ground precipitation is 5 minutes 4 11 Ground data in Slovakia SHMU 4 11 1 Rain gauge The network In Slovakia there are overall 98 automatic rain gauge stations potentially available for the H SAF project The real number of usable gauges varies with time because on average about 20 of them are out of operation Mean minimum distance between rain gauges in the complete network is 7 74 km Map of the rain gauge network in Slovakia containing also climatological and selected hydrological stations is shown in next figure F
55. station in our case it was 10 km The point information from a raingauge is spread in the neighbour by an exponential function limited by the influence radius and the rainfall value for each grid point is computed as the contribution of all the closest measurements Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 58 177 Product Validation Report PVR 02 The resulting grid is a 5x5 km regular grid with 240 columns and 288 lines Moreover a Digital elevation model is used to provide a mask of Italy in order to 1 screen out sea pixels too far from the coastlines and 2 process the pixels with the elevation above sea level 4 9 2 Radar data The network The Italian radar data have been not used for the validation of the current version of HO1 but the verification of the satellite product with those data is in progress The results will be presented at the next review of the project The Italian Department of Civil Protection DPC is the authority leading the national radar coverage project in order to integrate the pre existent regional systems made of ten C band fixed regional installations five of them polarimetric and one transportable X band polarimetric radar two systems owned by the Italian company for air navigation services ENAV and three managed by the Meteorological Department of the Italian Air Force AMI After its completion the Italian radar networ
56. system provides e High resolution analyses interest of validation WG 3 e Nowcasts e Improved forecasts of the following variables e Temperature 3 d field e Humidity 3 d e Wind 3 d e Precipitation 2 d interest of validation WG 3 e Cloudiness 2 d e Global radiation 2 d The INCA precipitation analysis is a combination of station data interpolation including elevation effects and radar data It is designed to combine the strengths of both observation types the accuracy of the point measurements and the spatial structure of the radar field The radar can detect precipitating cells that do not hit a station Station interpolation can provide a precipitation analysis in areas not accessible to the radar beam The precipitation analysis consists of the following steps i Interpolation of station data into regular INCA grid 1x1 km based on distance weighting only nearest 8 stations are taken into account to reduce bull eyes effect ii Climatological scaling of radar data by means of monthly precipitation totals of raingauge to radar ratio partial elimination of the range dependence and orographical shielding iii Re scaling of radar data using the latest rain gauge observations iv Final combination of re scaled radar and interpolated rain gauge data V Elevation dependence and orographic seeding precipitation In the final precipitation field the raingauge observations are reproduced at the raingauge station loc
57. that In order to obtain an estimate of upscaling downscaling and interpolation process theoretical experiment of some methodologies has been implemented Hypothetic perfect fields are been defined and a grid of perfect measurements has been defined The experiment assumed different typologies of precipitation field respect the variance of precipitation intensity in the field To obtain the field of perfect measurements some grid points from the precipitation field are been The EUMETSAT EN mtr Product Validation Report PVR 02 Facilities z H SA p Fe Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 yaaa ara Waler Product HO2 PR OBS 2 Date 30 09 2011 Page 174 177 removed The experiment removed the perfect rain gauge long a regular grid to simulate an unreal distribution of non realistic rain gauges The sampling has been done at different grid spacing 2 3 and 4 time the perfect field to obtain new data at different spatial density Then the algorithm performances of up down scaling procedure to reproduce the original field are been evaluated The work has been implemented for 4 different algorithms Barnes Inverse Distance Squared IDS kriging and Nearest Neighbor NN 0 200 400 600 600 1000 1200 mm 10 20 30 40 50 60 70 80 90 100 110 E Sampled data Interpolated data STEP 2 Sampled and Interpolated data Figure 120 Example of sampled data for a regular grid In right on the upper part
58. the maximum range distance used is 120 km or even less e g 80 km Range resolution is 250 m in Belgium 250 340 225 and sometimes 500 m for the Italian radars 500 m for one of the Hungarian radars and 250m for the other two Polish radars can work with 125 m and 250 m resolution and in Turkey it is 250 m for all the radars The scan strategies within the PPVG countries are well balanced and similar to each other though they vary from one radar to the other even within countries All radars are regularly maintained and calibrated which is a good indicator of the continuous supervision of quality of radar data and the important element to sustain radar data quality 4 3 3 Data processing The Tab 08 is provided to summarize the available products generated from radar measurements and the processing chain used to generate them Finally the list of the radar products used for the validation work is included in the last row Radar rainfall products are obtained after processing the measured radar reflectivity at different elevations of the radar scan strategy After each elevation the PPI Plan Position Indicator products and the CAPPI Constant Altitude PPI products are calculated PPI is the measurement of the radar antenna rotating 360 degrees around the radar site at a fixed elevation angle CAPPI products are derived from this by taking into account the radar displays which give a horizontal cross section of data at constant altit
59. this area threshold 0 25 mm h RMSE and ETS during the present case study Conclusions From qualitative and statistics comparison it appears that for this case study summer storm characterized by convective rainfall the hO2 product could reproduce the rainfall patterns with quite good confidence slightly underestimating rainfall amounts 5 2 2 Case study August 22 24 2010 Description This event has been chosen because thunderstorms with intense precipitation resulted in local flooding in Belgium The country was at the edge of a large anti cyclone which was moving away towards South East next figure Warm but humid and unstable air was brought from South West whereas a cold front was moving from West Figure 48 Surface map on 22 August 2010 at 06 UTC MSLP and synoptic observations Data used Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 tycka a Waler Product H02 PR OBS 2 Date 30 09 2011 Page 81 177 The EUMETSAT FETE are Product Validation Report PVR 02 HSAF p Products H02 from August 22 at 6 00 UTC to August 24 at 12 00 UTC have been considered in this study The total is 12 satellite passages distributed as follows 4 in the early afternoon of August 22 2 in the early morning of August 23 3 in the early afternoon of August 23 3 in the early morning of August 24 The ground data used for validation are the Wideumont radar instantaneous measurements without
60. to smaller number of samples RRimmh 3 June2010 June2010 3 June2010_ June2010 rain gauge radar raingauge radar rain gauge radar _ rain gauge radar 0 25 lt RR lt 1 105 096 136 151 184 166 RR gt 10 RR oar 10 20 6 43 22 f 1048 453 Table 29 Continuous statistic Conclusions the EUMETSAT sauce vadat R PVR 0 Doc No SAF HSAF PVR 02 1 1 satene Appice IG H SAF roduct Validation Report Issue Revision Index 1 1 eso ond Wela Product H02 PR OBS 2 Date 30 09 2011 Page 94 177 The results for H02 were worse than for HO1 Compared with the case study for August the results were worse All the quantitative precipitation amounts were underestimated 5 3 3 Case study December 5 6 2010 River Rhine catchment Description Intense rains on 5 6 December 2010 lasting over 72 hours fell along an air mass boundary lying across France and Germany It was a result of subtropical air from south west and polar cold air over Central Europe moving forward to south First precipitation as snow and rain were observed on 5 in relation to the cyclone Liane in northern parts of Germany On the evening the precipitation deflected to the south of Germany In higher regions of the river Rhine they fell as snow In the night to 6 December in south of river Danube the snow changed to rain Over a period of 4 days precipitation sum reached 100 mm See ne
61. 1 00 convective 16 August 2010 06 00 23 45 convective 15 September 2010 15 00 mixed 18 September 2010 09 00 21 November 2010 20 00 stratiform 22 November 2010 23 45 28 November 2010 15 00 stratiform 29 November 2010 10 00 Table 58 List of precipitation events selected for statistical analysis For each precipitation event and each ground reference data a set of continuous and dichotomous Statistical scores was computed The scores and thresholds of the precipitation classes were adopted from the H SAF common validation methodology As an example the results of selected statistical scores obtained with different reference data for the event 1 and 4 are shown in next two figures respectively Due to the small number of compared PR OBS 2 observations during the selected precipitation events overall convective 1864 observations stratiform 2251 mixed 3409 the obtained results may not be representative enough Therefore it is questionable if any conclusion about dependence of the investigated ground reference data on the long term validation results can be made It is proposed that statistical analysis using longer validation period will have to be performed The EUMETSAT Network of Satellite Application Facilities HSAF Support to Operational Hydrology and Water Management Product Validation Report PVR 02 Product H02 PR OBS 2 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index
62. 1 10 mm h Accuracy RMS lt 1 mm h Table 2 Simplified compliance analysts for product PR OBS 2 PR OBS3 Requirements Result of _ threshold target optimal validation Accuracy RMS gt 10 mm h Accuracy RMS 1 10 mm h Accuracy RMS lt 1 mmh Table 60 Simplified compliance analysis for product PR OBS 1 2 3 Obviously it is RMSD gt RMSE since RMSD is inclusive of Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 172 177 The shir ae a sotene Apis Product Validation Report PVR 02 the error of satellite measurements RMSEsat that is what we would like to know from validation the error of ground measurements RMSEground that should be known by the owners of the stations the error of the comparison methodology RMSEcomparison that should be estimated by metrologists Then we should consider It should be RMSD RMSEsat RMSEground RMSEcomparison In the final part of the H SAF Development Phase attempts have been made to evaluate RMSEground All validation groups not only for precipitation but also for soil moisture and snow have been requested to quote figures to characterise the errors of the ground reference that they used The various team did this after consultation with the operational units in charge of the observing networks in their institutes For precipitation the following figures were quoted UniFerrara
63. 16 3 in the afternoon of August 16 6 in the morning of August 17 The ground data used for validation are the Wideumont radar instantaneous measurements without rain gauge adjustment Radar data are available within 5 minutes around the satellite passage Comparison Here are three examples of H02 files compared with radar data upscaled to the same grid The first two examples are observing the same scene from different satellites at noon of August 15 while the third one refers to the early morning of August 16 oat gt roduct Validation Report _PVR 02 DOC NO SAF HSAF PVR 02 1 1 Satelite Application roauct Validation Report H SAF p Issue Revision Index 1 1 tio and Water Product H02 PR OBS 2 Date 30 09 2011 Page 78 177 Figure 44 H02 image of August 15 2010 at 12 08 left compared with upscaled radar at 12 10 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 Figure 45 H02 image of August 15th 2010 at 12 09 left compared with upscaled radar at 12 10 right the same radar image as above but upscaled on a different grid The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 the EUMETSAT Proda valdaione t PVR 02 Doc No SAF HSAF PVR 02 1 1 Satelite Application roquc alldation REPOrt HSAF p Facil Issue Revision Index 1 1 Support to Operational yGrology and W pie Product H02 PR OBS 2 Date 30 09 2011 Page 79 177 Figure 46 H02
64. 8 8EB BANSKE a SMOLNIK TUCK mum hE KUKOVA zzz SKALITE MALACKY MALCOY mum SMOLENICE mmm a a SKALICA Mumm MOTYCKY m MAKOV mum VRBOVCE a MOTESICE ummm VRATNA VIGLAS_PSTRUSA mmm ORAVSKA_LESNA E VYSNA BOCA MMMmmm POLOMKA Mmmm POHRONSKA_POLHORA mmm KRALIKY LIPTOVSKA_OSADA JASENIE SUCHA HORA mummu ZUBEREC NOVVOT a DOBSINSKA_LADOVA_JASKYNA mm Figure 99 Distribution of rain gauges according to the minimum visible height of radar beam To understand dependence of radar precipitation estimations and rain gauge values on gauge altitude above the sea and on radar beam altitude the scatterplots of log R G versus station altitude shown on next figure and log R G versus radar beam altitude shown on Figure 101 were generated Quite wide scattering can be observed but quadratic polynomial trend lines indicate that in general radar underestimates precipitation and this underestimation is proportional to station elevation and radar beam elevation The EUMETSAT Produa Wahcaenk t PVR 02 Doc No SAF HSAF PVR 02 1 1 HSAF P aah Issue Revision Index 1 1 Support to Operational eae Product H02 PR OBS 2 Date 30 09 2011 Page 146 177 Log RiG log Radar Gauge versus station elevation above the sea level 3 y 3E 07x 6E 05x 0 2656 2 ri j t3 ja gS 1 5 FF t i F mn 4 a at H t t 4 ret Cani ee ee ere eG t
65. 80 Sugiura K D Yang T Ohata 2003 Systematic error aspects of gauge measured solid precipitation in the Arctic Barrow Alaska Geophysical Research Letters 30 1 5 Schutgens N A J and R A Roebeling 2009 Validating the validation the influence of liquid water distribution in clouds on the intercomparison of satellite and surface observations J Atmos Ocean Tech 26 1457 1474 Tokay A D B Wolff K R Wolff and P Bashor 2003 Rain Gauge and Disdrometer Measurements during the Keys Area Microphysics Project KAMP J Atmos Oceanic Technol 20 1460 1477 Wagner A 2009 Literature Study on the Correction of Precipitation Measurements FutMon C1 Met 29 BY 32 p available at www futmon org Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 135 177 Product Validation Report PVR 02 10 Annex 3 Working Group 2 Radar data PROPOSAL Radar rainfall estimation is influenced by several error sources that should be carefully handled and characterized before using it as a reference for ground validation of any satellite based precipitation products The main issues to deal with are 1 system calibration 2 contamination by non meteorological echoes i e ground clutter sea clutter clear air echoes birds insects W LAN interferences partial or total beam shielding rain path attenuation wet radome attenuation range dependent errors bea
66. A CE Central Europe and it is used in pre operational mode in Slovakia and Poland The RADOLAN system is used in Germany operationally and it is already utilized for the H SAF products validation Both systems consist of computational modules which enable to integrate various sets of precipitation data sources raingauge network radar network NWP models outputs and climatological standards into common precipitation product which can describe well the areal instantaneous and cumulated precipitation fields Here below a brief description of the INCA and RADOLAN systems follows More information on both systems can be found in the documentation which is available on the H SAF ftp server hsaf WP6000 precipitation WG_groups WG3 inca documentation The EUMETSAT Network of Satellite Application Facilities 4 4 1 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 tycka one Waler Product H02 PR OBS 2 Date 30 09 2011 Page 43 177 H SAF Product Validation Report PVR 02 a Google Figure 15 Coverage of Europe by the INCA and RADOLAN systems INCA system The INCA Integrated Nowcasting through Comprehensive Analysis analysis and nowcasting system is being developed primarily as a means of providing improved numerical forecast products in the nowcasting and very short range forecasting It should integrate as far as possible all available data sources and use them to construct physically consistent analyses of at
67. Bozena Lapeta Poland Ibrahim Sonmez and Ahmet Oztopal Turkey Emanuela Campione Italy Introduction This document reports on the outcomes of the inventory completed about the raingauges used as ground reference within the validation groups Moreover some general conclusion is drawn based on the raingauges validation activities carried on in the last years by the Validation Group of H SAF The inventory was structured in three sections dealing with the instruments used the operational network and the approach to match gauge data with the satellite estimates The results are summarized in the next pages HM PPV Countries Rain gauges 0 10 E 20 E 30 E 40 E Figure 93 Rain gauge networks in PPVG Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 130 177 Product Validation Report PVR 02 The Instruments Most of the gauges used in the National networks by the Precipitation Product Validation Group PPVG Partners are of the tipping bucket type which is the most common device used worldwide to have continuous point like rainrate measurement Nevertheless several source of uncertainty in the measurements are well known but difficult to mitigate First very light rainrates 1 mm h and less can be incorrectly estimated due to the long time it takes the rain to fill the bucket Tokay et al 2003 On the other side high rainrates above 50 mm h are u
68. During the Precipitation Product and Hydrological Validation workshop held in Bratislava the 20 22 of October 2010 the Precipitation Product Validation Group PPVG has decided to set up a working group for the definition of the correct verification of satellite precipitation product performances using the rain gauges data available inside the PPVG The main aims of this working group are to identify the more suitable techniques to compare rain gauge data with satellite precipitation products to analyse the application of these techniques to the rain gauge available inside the PPV to produce a well referenced documentation on the methodology defined to develop the code to be used in the PPVG for a correct verification of satellite precipitation product performances Activities First step collect e characteristics telemetric spatial distribution temporal resolution quality check applied instrument sensitivity and saturation value and accuracy of the rain gauge networks which composes the PPVG Belgium Bulgaria Germany Poland Italy Turkey Start Time End time December 2010 January 2011 First Report 31 of January 2011 Second step define on the base of published papers and the characteristics of the rain gauge data available inside the PPVG e ground data quality check to be applied to all rain gauge data e optimal spatial density of rain gauge stations to be representative of the ground precipi
69. Figure 12 value of the linear correlation coefficient is computed between each raingauge pair in the Italian hourly 2009 dataset as function of the distance between the two gauges Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 33 177 The EUMETSAT Stele Apa H SAF Product Validation Report PVR 02 Support to Operational HMM PPV Countries Rain gauges 0 10 E 20 E 30 E 40 E Figure 11 Rain gauge networks in PPVG 200812 200901 200902 200903 200904 200905 200906 200907 200908 200909 200910 200911 Figure 12 Correlation coefficient between raingauge pairs as function of the distances between the gauges Colours refer to the months of the year 2009 Assuming these values significant for the other Countries involved in this study we can conclude that the gauge network in PPVG is capable to resolve the spatial structure of rain patterns only for stratified systems but it is inadequate for small scale convective events of gauges distance km 1300 1800 330 475 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 34 177 Table 8 Number and density of raingauges within H SAF validation Group Product Validation Report PVR 02 the number of raingauges could vary from day to day due to operational efficiency within a maximum range of 10 15 only in the Wallonia Region onl
70. Group decided during the last internal workshop held in Bratislava from 20 22 of October 2010 to set up various working groups for the investigation of possible improvement of the validation methodology One of these working groups is INCA precipitation for PPV group Definition of INCA Precipitation Products INCA system consists of computational modules which enable us to integrate various sets of precipitation data sources raingauge network radar network NWP models outputs and climatological standards into common precipitation product which can describe well the areal instantaneous and cumulated precipitation fields The main aims of INCA precipitation for PPV group are to identify the INCA precipitation products which can be considered as precipitation ground reference and used for validation of H SAF products both instantaneous and cumulated precipitation fields to identify the techniques of comparison the INCA precipitation products with satellite precipitation products to develop the code to be used in the PPVG for a correct verification of satellite precipitation product performances with INCA to produce a well referenced documentation on the methodology defined to perform H SAF products validation based on these techniques and INCA precipitation products Activities First step e identify experts contact persons inside INCA community which can provide information on INCA system like methods of pre
71. IFOV area compared to the rectangular next 10 minutes rain rectangular next 10 minutes rain amount amount each overpass is each overpass is compared to the compared to the corresponding 1 corresponding 1 minute rain rate minute rain rate Product Validation Report PVR 02 Product H02 PR OBS 2 weighted mean semi variogram gauges value over centred on satellite IFOV Table 53 Matching strategies for comparison with H0O1 and H02 Belgium and Bulgaria use raingauges only for cumulated precipitation validation Spatial matching Temporal matching Spatial matching Temporal matching weighted mean semi variogram gauges value centred on satellite IFOV Belgium and Bulgaria use raingauges only for cumulated precipitation validation matching gauges are searched on a radius of 2 5 km from the IFOV centre Nearest neighbour mean gauges value over the pixel area weighted mean semi variogram gauges value centred on satellite IFOV each overpass is compared to the next hourly rain amount the average rainrate over a given hour Is compared to next hourly rain amount each overpass is compared to the next 10 minutes rain amount each overpass is compared to the corresponding 1 minute rain rate Nearest neighbour Nearest neighbour matching gauges are searched on a radius of 2 5 km from the IFOV centre Nearest neighbour mean gauges value over the pixel area weighted mean se
72. KNMI August 26 2004 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 jyaagyons wale Product HO2 PR OBS 2 Date 30 09 2011 Page 51 177 The EUMETSAT satente Arico H SAF Product Validation Report PVR 02 Facilities corrected and quality controlled in four steps with checks of completeness climatologic temporal spatial consistency and marginal checks i aa rt ca Oo O OO D E REN i Figure 21 Network of rain gauges in Germany Figure 22 Pluvio with Remote Monitoring Module 4 7 2 Radar data Radar based real time analyses of hourly precipitation amounts for Germany RADOLAN is a quantitative radar composite product provided in near real time by DWD Spatial and temporal high resolution quantitative precipitation data are derived from online adjusted radar measurements in real time production for Germany Radar data are calibrated with hourly precipitation data from automatic surface precipitation stations 4 http www dwd de bvbw appmanager bvbw dwdwwwDesktop nfpb true amp windowLabel dwdwww main book amp T1460994925114492118088 1gsbDocumentPath Navigation 2FWasserwirtschaft 2FUnsere _Leistungen 2FRadarniederschlagsprodukte 2FRADOLAN 2Fradolan _node ht ml 3F nnn 3Dtrue amp switchLang en amp pageLabel dwdwww spezielle nutzer forschung fkradar The EUMETSAT p d t V lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Sotelite Application FOQUCT Valldathon neport z H SAF p Issue Revisi
73. Meri Slovakia Firat Bestepe and Ahmet Oztopal Turkey Working Group 3 INCA products Coordinator Jan Kanak SHMU Slovakia Proposal completed first report available Participants Claudia Rachimow and Peter Krahe Germany Rafal lwanski and Bozena Lapeta Poland Silvia Puca Italy Working Group 4 COSMO grid Coordinators Angelo Rinollo RMI Belgium supported by Federico Porcu University of Ferrara and Lucio Torrisi CNMCA Italy Proposal completed First report available in ftp fto meteoam it hsaf WP6000 WP6100 precipitation WG groups software developed WG CLOSED Participants Emmanuel Roulin Eszter Labo and Judit Kerenyi Testing over Belgium successful procedure already generalized in a way that can be tested and used by all groups and delivered Testing by other members in progress Working Group 5 Geographical error map Coordinator Bozena Lapeta IMGW Poland Proposal completed Participants Silvia Puca Italy Ibrahim Sonmez and Ahmet Oztopal Turkey Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 128 177 Product Validation Report PVR 02 9 Annex 2 Working Group 1 Rain gauge data PROPOSAL The ground reference does not exist The common validation methodology inside the H SAF project has been based on the hydrologist reference end users constituted mainly by rain gauge and then by radar data
74. Product Validation Report PVR 02 class 1 class 2 class 3 class 4 class 1 class 2 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Rain Gauge RR Rain Gauge Figure 72 Contingency table statistic of rain rate mm h for H02 vs rain gauge data Left for 5 6th December Right for whole December 2010 Results of the continuous statistic next table show negative Mean Error ME in the period 5 6 December with both kind of ground data in all classes which means that H SAF product underestimated precipitation amounts Standard deviation SD with 0 64 mmh for the class RR gt 0 25 mmh is the highest for validation with radar data for 5 6 December the correlation coefficient CC with mostly less than 0 2 is more worse than for results in summer analogue to the results for POD see above RR mmh 5 6 December 2010 December 2010 5 6 December 2010 December 2010 rain gauge rain gauge rain gauge rain gauge 0 25 lt RR lt 1 RR gt 0 25 PRR ODS Me 049 040 046 009 0 70 058 0 70 0 77 sp 028 0o49 033 067 049 064 089 112 mB o10 027 o11 086 011 021 012 042 _ 1 lt RR lt 10 P 2625 a ee po 26 25 o m on os 014 031 ooo gt gt e a RMse 131 143 169 173 2630 Table 32 Continuous statistic Concl
75. Product Validation Report for product H02 PR OBS 2 Version 1 1 30 September 2011 EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management The EUMETSAT Network of Satellite Application Facilities HSAF Support to Operational Hydrology and Water Management Product Validation Report PVR 02 for product H02 PR OBS 2 Precipitation rate at ground by MW cross track scanners with indication of phase Reference Number SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Last Change 30 September 2011 About this document This Document has been prepared by the Product Validation Cluster Leader with the support of the Project Management Team and of the Validation and Development Teams of the Precipitation Cluster Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 2 177 Product Validation Report PVR 02 DOCUMENT CHANGE RECORD Baseline version prepared for ORR1 Part 2 1 0 16 05 2011 Obtained by PVR 01 delivered during the Development Phase 30 09 2011 Updates acknowledging ORR1 Part 2 review board recommendation Minor adjustments 12 16 01 2012 e Document reference number as PVR 01 instead of PVR 1 2 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 3 177 Product Validation Report PVR 02 Index The EUMETSAT Satellite Applica
76. R 0 40 and CSI 0 16 have been obtained using rain gauge data on one year of data 1 December 2009 30 November 2010 In table 48 it is possible to see that 98 of no rain is correctly classified by HO2 There is a general precipitation underestimation by the satellite product H02 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 124 177 Product Validation Report PVR 02 6 4 User requirement compliance In table 49 In table 6 9 the statistical scores obtained by the yearly validation of HO2 with radar and rain gauge data are reported The statistical scores reach the thresholds stated in the User Requirements for land areas in all cases using both rain gauge and satellite data as ground reference Only for precipitation lower than 10 mm h using rain gauge in coast areas the threshold is not completely reached This result might be explained by considering the difficulty of rain gauge to evaluate the precipitation in a coastal IFOV with a large part covered by sea Annual average of RMSE gt 10mmh 90 80 25 140mmih 120 105 50 lt tmmh 240 145 90 Precipitation Requirement RMSE radar gauge gauge class thresh target optimal land land coast Table 49 User requirement and compliance analysis for product H02 As reported in Annex 8 the results obtained by the current validation procedure represent the convolution of at least three facto
77. R 02 Finally set of statistical parameters for each single rain gauge station was computed mean error standard deviation mean absolute error multiplicative bias correlation coefficient RMSE and relative RMSE Relative RMSE and Mean Error were selected to be specified for radar precipitation measurement over the whole radar range For this purpose quadratic or linear polynomial trend lines were created as is shown on next figure Slovak radar network URD_RMSE based on radar FEE Slovak radar network Mean Error based on radar URD RMSE minimum visible height above the rain gauge mm h minimum visible height above the rain gauge 200 1 180 0 8 4 160 4 0 6 4 y 7E 07x 0 0178x 67 118 01 y 0 0001x 0 1386 T T T T T T T T T T T T T T 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 1200 1400 Radar minimum visible height above the rain gauge m Radar minimum visible height above the rain gauge m Figure 102 Relative RMSE left and Mean Error right computed independently for each rain gauge station in radar range and corresponding trend lines extrapolated for beam elevation up to 1500m Relative RMSE and Mean Error can be specified for each pixel of radar network composite map using regression equations which describe dependence on minimum radar beam elevation above the surface This can be considered as quality indicator maps of radar measurements with regard to te
78. Report PVR 02 Facilities H SAF p Azimuth step 1 40 deg 1 40 deg Layer minimum 500 m 500 m Layer maximum 1000 m 1000 m Max displayed height 5000 m 5000 m Radar horizon model provides the following outputs maps of radar range e terrain elevation e minimum visible height above the sea level e minimum visible height above the surface e Layer visibility defined by minimum and maximum levels Results of the horizon model for Maly Javorn k and Koj ovsk hola radar sites are shown on Figure 97 To evaluate the radar visibility over the whole radar network composite picture of minimum visible height above the surface was created and is shown on Figure 98 Layer visibility 500 1000m Terrain elevation Above the surface Layer visibility 500 1000m Terrain elevation Above the surface Height above the land surface level m 5000 Minimum visible height Minimum visible height abovethe sea level above the surface Minimum visible height Minimum visible height above the sea level above the surface o Figure 97 Radar horizon model output for Mal Javorn k left and Koj ovsk ho a right radar sites Colour scale on left corresponds to the products showing heights above the sea level scale on right corresponds to the products showing heights above the surface The EUMETSAT p d t V lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Satelite Appicotion roquct Vdalldatlon Report HSAF P Support to O
79. SAT sotene Apt amp H SAF Product Validation Report PVR 02 Support to Operational Hydrology and W Hired Product H02 PR OBS 2 Date 30 09 2011 Page 87 177 Figure 59 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 08 07 11 58 UTC station Rome and for RADOLAN RW left filled raster 2010 08 07 12 50 UTC and station data right dots 2010 08 07 13 00 UTC Data used HO2 data for eastern part of Germany in the given period were available for 2 01 UTC station Athens 2 02 UTC station Lannion 11 51 UTC station Athens 11 52 UTC station Lannion and 11 58 UTC station Rome Only these data are analysed in this case study Comparison A first look to the results Figure 59 shows that rain rates detected by satellite product are in the same area of Germany as those indicated by the ground data Statistical score In the following two tables the result of the categorical statistic of the validation with both RADOLAN and rain gauge data are listed The Probability Of Detection POD of precipitation gt 0 25 mmh gain for validation with RADOLAN 0 66 with rain gauge 0 54 The different results are due to the fact that RADOLAN data produce more valid pairs of satellite ground points A valid pair is given if for a satellite observation point fixed date time at least one ground observation point can be found within an surrounding area formed by a search ellipse of 2 5 km x 2
80. SD N SD gt Gat true ME Range 0 to ee Perfect score O k 1 Multiplicative Bias MBias Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 29 177 Product Validation Report PVR 02 1 N i T Range e to ce Perfect score 1 53 i true NS j MB Correlation Coefficient CC S at sat Yue true N a N __ LL__ with sat sat and true ty true N k 1 N k 1 kat sat 2 ue true 1 Range 1 to 1 Perfect score 1 Root Mean Square Error RMSE N RMSE gt Gat true p Range 0 to cv Perfect score 0 k 1 Root Mean Square Error percent RMSE used for precipitation since error grows with rate 1 5 at true noo RMSE i N k 1 true k Range 0 to Perfect score 0 The statistical scores evaluated in PPVG for multi categorical statistic are derived by the following contingency table Contingency Table o ground S S o ys no toai yes hits falsealarms forecast yes no misses correct negatives forecast no total observed yes observedno total where hit event observed from the satellite and also observed from the ground miss event not observed from the satellite but observed from the ground false alarm event observed from the satellite but not observed from the ground correct negative event not
81. SS 1 COSMO grid validation PROPOSAL The aim of the group is to find in cooperation with the developing team of PR ASS 1 the most reliable way to validate the PR ASS 1 product which is provided on the COSMO model grid in a rotated coordinate system and to develop software tools for a common validation methodology of the product Activities First step defining the best validation strategy for PR ASS 1 depending on the resolution of the ground data used Implementation of prototype softwarefor grid cutting and ground data up scaling in the COSMO grid with the help of Lucio Torrisi CNMCA Start Time End time November 2010 December 2010 First Report 20 of December 2010 Second step up scaling software tools dissemination and checks by the different validation teams Eventual improving and refining if needed Start Time End time January 2011 February 2011 Final Report 28 of February 2011 Codes delivery and related documentation 28 of February 2011 Composition of the working group Coordinator Angelo Rinollo RMI Belgium supported by Federico Porcu University of Ferrara Italy and Lucio Torrisi CNMCA Italy Participants Belgium Bulgaria Germany Italy Hungary Slovakia Turkey Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 163 177 Product Validation Report PVR 02 REPORT H SAF project WP 6100 Working Group 4 Develop
82. TC on the 15th of August 2010 right panel and 10 minute precipitation interpolated from RG data from 1210 UTC left panel On both maps the precipitating areas reveal the lightning activity seen on the Figure 79 however the HO2 overestimated the precipitation area This tendency is clearly seen in the Central Poland when HO2 reports rainfall while the ground data doesn t Figure 80 On the other hand this precipitating area seen on the satellite derived rainfall map in the Central Poland Figure 80 right panel corresponds to lightning activity observed in this region Figure 79 Fact that this rainfall is not present on the RG map may be explained by the ground network density One maximum out of two was properly recognized by satellite product while instead of the second one the fuzzy area of increased rainfall was obtained Figure 80 Statistical score Further analysis was performed for all overpasses available for the 15 of August 2010 The ability of HO2 product to recognize the precipitation was analyzed using dichotomous statistics parameters The 0 25mm h threshold was used to discriminate rain and no rain cases In the next table the values of Probability of Detection POD False Alarm Rate FAR and Critical Success Ratio CSI are presented Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 The sarah pos p idati PVR 02 z H SAF Product Validation Report 0 an a yea and Wote Product H02 PR OBS 2 Da
83. Tops Hail Probability Base Hail Probability 24h HailProbability Overview 1 3 24 Hr Rainrate accumulation Is any quality YES map available Processing Clutter removal time Clutter suppression by RLAN wifi filter Clutter chain domain Doppler filtering Fuzzy Logic scheme using removal atttenuation and static clutter map Clutter map Velocity correction beam Product Validation Report PVR 02 Product H02 Z R a 200 b 1 6 PCAPPI 1500m Cartesian grid 600m resolution Description of instantaneous radar product used in HSAF Validation Activities 24 h accumulation with range dependent gauge adjustment Cartesian grid 600m resolution Description of accumulated radar product used in HSAF Validation Activities Doc No SAF HSAF PVR 02 1 1 PR OBS 2 Issue Revision Index 1 1 Date 30 09 2011 Page 41 177 Texture Z R a 200 b 1 VPR correction under testing Nationale composite CAPPI 2 km CAPPI 3 km CAPPI 5 km VMI SRI Projection Mercator Resolution 1 km Threshold No Acc periods 1 3 6 12 24h Projection Mercator Resolution 1 km Threshold No No rain gauge correction blocking correction gt next Year 2012 VPR gt No Z R a 200 b 1 6 National composite CMAX Projection stereographic S60 Resolution 2 km Threshold 7dBZ No rain gauge correction Acc periods 3 6 12 24h National composite CMAX Projection stereographic S60
84. a Rachimow and Peter Krahe Germany uboslav Okon Jozef Vivoda and Michal Nestiak Slovakia Rafal lwanski and Bozena Lapeta Poland Silvia Puca Italy Introduction This report presents outcomes of the initial activities performed within the INCA products working group In the first part information on the INCA or INCA like systems available in the participating countries are summarized The second part of the report presents several case studies comparing precipitation fields estimated by radars raingauges and the INCA system Results of the statistical comparison of the PR OBS 2 product with the different reference fields for selected precipitation events are also included Summary of the INCA system survey As a first step of survey experts contact persons were identified inside the INCA group community as listed in the following table Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 ryan one Waler Product H02 PR OBS 2 Date 30 09 2011 Page 151 177 Product Validation Report PVR 02 Slovakia Jozef Vivoda jozef vivoda shmu sk Bowes eachatnesick icanestskoshmusk Germany Claudia Rachimow Table 56 List of contact persons Within the participating countries there are two types of systems providing precipitation analyses usable for H SAF validation INCA developed by ZAMG Austria and RADOLAN DWD Germany The INCA system is currently under development as INCA CE Central Europe and is used in pre o
85. a detail of field studied below the original grid of field for step 2 From the field the white circles mean the data removed from the map The black squares mean the position of perfect measurement The techniques of up down scaling reproduce the field only from the perfect measurements The algorithms used in the validation group are similar to the Barnes algorithm This like Barnes algorithm creates a grid of regular step where each node contains the data calculated from all data weighted by distance from the node itself The calculation is done several times through successive iterations in order to minimize errors in the precipitation field In the following table 65 are reported the values of RMSE were sat is the sampled and captioned data and reference is the value of perfect field Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 are Product H02 PR OBS 2 Date 30 09 2011 Page 175 177 The EUMETSAT Network o a i Product Validation Report PVR 02 N 1 sat true 100 N true i RMSE N is the total number of pairs data in which the reference value is different by O Algorithms Step 2 Step 3 Step 4 Table 62 RMSE and standard deviation of interpolation algorithms for 3 different regular grids In the cases studied Barnes appears to be the algorithm with the lower mean value of RMSE and their standard deviation than the other interpolation algorithms and the error of interpolatio
86. able on the H SAF ftp server hsaf WP6000 WP6100 precipitation WG_groups WG2 radar WG 2 3_ radar quality indication_v1 doc 4 12 Ground data in Turkey 4 12 1 Rain gauge The network 193 Automated Weather Observation Station AWOS located in the western part of Turkey are used for the validation of the satellite precipitation products in the HSAF project The average distance between the AWOS sites is 27 km The locations of the AWOS sites are shown in next figure 425 N 40 0 N 37 5 N 45 0 E 27 5 E 3006 42 5 E 325E 350E 375E 400E Figure 40 Automated Weather Observation System AWOS station distribution in western part of Turkey The instruments The gauge type of the network is tipping bucket where each has a heated funnel The minimum detection capability of the gauge is 0 2mm per tip In the maximum capacity of the instrument is 720 mm h at most The operational accumulation interval is 1 minute so that alternative cumulation intervals such as 5 10 20 30 minutes are possible Data processing Quality control High quality of the ground data is critical for performing the validation of the precipitation products The validation results or statistics can provide meaningful feedbacks for the product developers and additionally the products can be used reliably only if there is a confidence present about the ground data at a certain level For this reason some predefined quality assurance QA tests are considered for
87. aches are applied separately in the validation of the satellite products As for the temporal matching the product time is taken into account and 5 minute window t 2 to t 3 is considered for estimation of the average rainrate for each site For the spatial matching the mesh grid size of 3kmX3km is constructed for each IFOV area For each grid point the rainrate is estimated by taking the 5 minute averaged rainrate amounts observed at the nearby AWOS sites within the radius distance of 45 km for convective type or 125 km for stratiform type considering the weighting of each site with respect to the grid point Equation 1 The weighting amounts are derived from the spatially varying weighting functions obtained by using the semi variogram approach Sen and Habib 1998 Finally the Gaussian filter is applied to the estimations at the mesh grid of the IFOV area to get the average rainrate Then this amount is compared with the satellite precipitation product amount for the validation purposes 4 13 Conclusions After these inventories some conclusions can be drawn The rain gauge in PPVG is composed by 3500 instruments across the 6 Countries Belgium Bulgaria Germany Italy Poland and Turkey These data are as usual irregularly distributed over ground and are generally deduced by tipping bucket type instruments Moreover most of the measurements are hourly cumulated So probably the raingauge networks used in this validation activities are sure
88. act of precipitation generally Standard deviation SD with 0 5 mmh for this class is less than for validation of HO1 The correlation coefficient CC with 0 61 0 49 is better than that for HO1 0 24 0 46 These facts are due to less samples for H02 Results for whole August are not so clearly better ME 0 64 0 19 for HO2 against 0 25 0 59 for HO1 CC 0 35 0 38 for H02 against 0 32 0 38 for HO1 7 August 2010 August 2010 7 August 2010 August 2010 i rain gauge rain gauge rain gauge 0 25 lt RR lt 1 RR gt 0 25 069 oso 186 044 036 044 116 The Jat si p d tV lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Sete Appice ak H SAF roduct Validation Report Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 90 177 Support to ee ydro 0 01 0 02 0 09 0 22 0 61 0 49 0 35 0 38 Pamse oss 083 079 138 166 276 165 151 a 1 lt RR lt 10 Me 1 26 2 07 1 28 0 19 11 22 975 11 69__ 8 08 SD 141 192 158 253 000 196 281 517 MaE 152 218 161 175 11 22 975 1169 888 MB 045 035 040 092 024 020 0 17 040 cc 0o52 042 028 022 0o04 058 014 mse 129 283 203 253 i122 994 1202 959 Table 26 Continuous statistic Conclusions The results for HO2 were worse than for H01 For rain rates greater than 1 mmh the probability of detection is equal less than the false alarm rate All the quantitative preci
89. aditional techniques Furthermore as soon as the polarimetric systems directly managed by DPC will be operational end of summer 2008 the property of the rain medium at vertical incidence are planned to be used for differential reflectivity calibration according to the procedure proposed by Gorgucci et al 1999 Redundancy of polarimetric variables will also be used for absolute calibration Gourley et al 2005 The Jes l n P d tV lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Sette appicor W z H SAF roduct Validation Report m er po wt t olo pdy ia 1 see en Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 61 177 Radar data processing chain Vertical Sounding T p e Propagation conditions Clutter removal Melting Layer Height Figure 31 Schematic representation of radar data processing chain Attenuation correction and hydrometeor classification Polarimetric radar systems enable the use of reliable algorithms for correcting rain path attenuation Based on the paradigm that specific attenuation ah dp and specific differential phase Kdp Kdp 0 5 dFdp dr are linearly related in rain ah dp g h dp Kdp cumulative attenuation effects can be corrected through the use of Fdp Carey et al 2000 the EUMETSAT oo Doc No SAF HSAF PVR 02 1 1 sotene Apt amp HSA Product Validation Report PVR 02 M gt Issue Revision Index 1 1 ai Product H02 PR OBS 2 Date 30 09 2011 Pag
90. ained by several institutes are obtained in the same way To improve the validation methodology and to develop software used by all members of the validation cluster several working groups have been composed during the last Validation Workshop held in Bratislava 20 22 October 2010 Annex 1 7 On the base of published papers and the characteristics of the ground data available inside the PPVG the main next steps are foreseen in order to improve the validation methodology e quantitative estimation of the errors introduced in the validation procedure comparing the instantaneous satellite precipitation estimation with the rain gauge precipitation cumulated on different intervals e definition of a rain gauge and radar data quality check e application of the data quality check to all radar and rain gauge data used in the PPVG e definition of the optimal and minimal spatial density of rain gauge stations to be representative of the ground precipitation in the view of satellite product comparison e development of the three software for raingauges radar and INCA products up scaling vs AMSU B grids e definition and code implementation of the technique for the temporal matching of satellite rain rate with rain gauge and radar data e selection of the appropriate methodology for spatial distribution of precipitation products errors taking into consideration spatial and temporal characteristics of each product for selected areas as test catchments
91. ains like Slovakia but less importantly in flat terrains like Hungary In Slovakia the RMSE error see Section 3 7 of radar accumulated fields is between 70 90 whereas in Hungary it is slightly lower between 60 80 Dataset for May September 2010 have been used to derive these parameters In PPVG it is under investigation INCA WG annex 5 the possibility to use ground data integrated software to produce precipitation field The results obtained by INCA WG are reported in the chapter 4 3 5 Spatial interpolation for rain gauges The partners of the Validation Group have been using a variety of different strategies to treat gauge data Some are using interpolation algorithms to get spatially continuous rainfall maps while others process directly the measurements of individual gauges Table 5 The first approach seems to be more convenient especially when the large IFOV of HO1 are concerned Type of interpolation Barnes over 5x5 km grid Co kriging Poland No Turkey NOU Table 5 Data pre processing strategies One of the next step of the Rain Gauge WG will be to harmonize the different spatial interpolation techniques among partners developing a common software for the validation collaborating with the GeoMap WG Annex 7 3 6 Techniques to make observation comparable up scaling technique for radar data From the first Validation Workshop in 2006 it has been decided that the comparison between satellite product and
92. al activities to provide satellite derived data to support specific user communities See fig 1 Fi Q R n Systems of the Data Acquisition EUM Geostationary slate EUM NOAA other data Systems Cooperation sources EUMETSAT HQ Meteorological Products Archive amp Retrieval Extraction Facility Data Centre Satellite Application Facilities SAFs CJ EUMETSAT HQ EUMETSAT HQ Figure 1 Conceptual scheme of the EUMETSAT application ground segment Next figure reminds the current composition of the EUMETSAT SAF network in order of establishment ee eee GRAS SAF ge Nowcasting amp Very Ozone amp Atmospheric PEE Numerical Weather Operational Hydrology Short Range Forecasting Ocean and Sea Ice Chemistry Monitoring Climate Monitoring Prediction GRAS Meteorology Land Surface Analysis amp Water Management Figure 2 Current composition of the EUMETSAT SAF network in order of establishment Conceptual scheme of the EUMETSAT application ground segment The H SAF was established by the EUMETSAT Council on 3 July 2005 its Development Phase started on 1 September 2005 and ended on 31 August 2010 The SAF is now in its first Continuous Development and Operations Phase CDOP which started on 28 September 2010 and will end on 28 February 2012 The list of H SAF products is shown in Table 1 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 15 177
93. and parallax shift It has been also showed a case study Poland where the ground data have been unable to catch the precipitation system while the satellite product reproduced more correctly the precipitation area In Chapter 6 the validation results of the HO2 long statistic analysis obtained for the period 1 12 2009 31 11 2010 have been presented To assess the degree of compliance of the product with user requirements Each Country Team has provided the monthly contingency tables and the statistical scores The results have been showed for radar and rain gauge land and coast area in the three precipitation classes defined in table 7 The rain rates lower than 0 25 mm h have been considered as no rain The statistical scores evaluated by the PPVG reach the thresholds stated in the User Requirements in all cases using both rain gauge and radar data as ground reference Only for precipitation lower than 1 mm h using rain gauge in coast areas the threshold is not completely reached This result might be explained by considering the difficulty of rain gauge to evaluate the precipitation in a coastal IFOV with a large part covered by sea As reported in Annex 8 the results obtained by the current validation procedure represent the convolution of at least three factors the satellite product accuracy the accuracy of the ground data used and the limitations of the comparison methodology e g errors of space and time co location representativeness c
94. and temporal high resolution quantitative precipitation data are derived from online adjusted radar measurements in real time production for Germany The data base for the radar online adjustment is the operational weather radar network of DWD with 16 C band sites on the one hand and the joined precipitation network of DWD and the federal states with automatically downloadable ombrometer data on the other hand In the course of this the precipitation scan with five minute radar precipitation data and a maximum range of 125 km radius around the respective site is used for the quantitative precipitation analyses Currently from more than 1000 ombrometer station approx 450 synoptic stations AMDA I Il and AMDA III S of DWD approx 400 automatic precipitation stations AMDA III N of DWD approx 150 stations of the densification measurement network of the federal states the hourly measured precipitation amount is used for the adjustment procedure In advance of the actual adjustment different preprocessing steps of the quantitative radar precipitation data are performed These steps partly already integrated in the offline adjustment procedure contain the orographic shading correction the refined Z R relation the quantitative composite generation for Germany the statistical suppression of clutter the gradient smoothing and the pre adjustment Further improvements of these procedures are being developed Precipitation distribution of the Precipitat
95. ar image of 4 2 2010 cumulated rainfall in the previous 24 hours raingauge adjusted Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product HO2 PR OBS 2 Date 30 09 2011 Page 166 177 Product Validation Report PVR 02 errs Figure 117 The Wideumont radar image of 4 2 2010 upscaled to the COSMO grid Adaption of the software to all the groups and delivery for testing present status After successful testing over Belgium the software has been adapted for common use by all the other groups and then delivered for testing Some feedback from Hungary and Slovakia has already been received and used for improvements The testing by all the groups is still in progress References About the COSMO model rotated grid see http www cosmo model org content model documentation core cosmoDyncsNumcs pdf pages 21 27 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 167 177 Product Validation Report PVR 02 14 Annex 7 Working Group 5 Geographical maps distribution of error PROPOSAL Validation activities performed during Development Phase for land and coast areas showed the difference in H SAF precipitation products quality depending on geographical localisation Those first achievements as well as the request from Hydrological Validation Group to provide the error characteristic of precipitation products for test catchments made Precipitation Validat
96. ate 30 09 2011 Page 5 177 Annex 5 Working Group 3 INCA Precipitation for PPV cccccccssssseccceceesseceeeceeeeeceeseeeeeseeeeeas 149 Annex 6 Working Group 4 PR ASS 1 COSMO grid Validation cccseeeesecccceeeeeeesseeeeeeeeeeees 162 Annex 7 Working Group 5 Geographical maps distribution Of EFrOr cc eeeeccceeseceeeeeeeeeees 167 Annex 8 Comments on the Validation Results for Products PR OBS 1 PR OBS 2 And PR OBS 3 171 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 6 177 Product Validation Report PVR 02 List of tables Tane THS AFF OCOC I T nena nen ann rT Tr ee nS 15 Table 2 List of the people involved in the validation of H SAF precipitation products ccccceeeeeeees 19 Table 3 Number and density of raingauges within H SAF validation Group ccccccceeseseeeseseeseeeeneeees 22 Table 4 Summary Of the raingauge CNALACLENISUCS wsssssnsasssncassnsasesssasssarcasacncasasnasaanenananenmaneeaanaananananans 22 Table 5 Data pre processing strategies ssistrsccctciscrsasccasansssaeearaneanantesaveanaceasanaaaneenaanesaanealaneanenaratenaateaatense 24 Table 6 Left Original Gaussian matrix Right Reduced matrix to dimensions M XK cccccceeeeeeeeeeeeees 26 Table 7 Classes for evaluating Precipitation Rate products eesssesssesseserrresrrrerrresrteserresrerssrrrerresereeeeeee 27 Table 8 Number and density
97. ations to be representative of the ground precipitation in the view of satellite product comparison development of the three software for raingauges radar and INCA products up scaling vs AMSU and MHS grids definition and code implementation of the technique for the temporal matching of satellite rain rate with rain gauge and radar data selection of the appropriate methodology for spatial distribution of precipitation products errors taking into consideration spatial and temporal characteristics of each product for selected areas as test catchments Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 127 177 Product Validation Report PVR 02 8 Annex 1 Status of working group Working Group 1 Rain gauge data Coordinator Federico Porcu University of Ferrara supported by Silvia Puca DPC Italy Proposal completed first report available Participants Emmanuel Roulin and Angelo Rinollo Belgium Gergana Kozinarova Bulgaria Claudia Rachimow and Peter Krahe Germany Emanuela Campione Italy Rafal lwanski and Bozena Lapeta Poland Ibrahim Sonmez and Ahmet Oztopal Turkey Working Group 2 Radar data Coordinators Gianfranco Vulpiani DPC Italy and Eszter Labo HMS Hungary Proposal completed first report available Participants Rafal lwanski Poland Emmanuel Roulin and Angelo Rinollo Belgium Marian Jurasek Luboslav Okon Jan Kanak Ladislav
98. ations within the limits of resolution Between the stations the weight of the radar information becomes larger the better the radar captures the precipitation climatologically Important factor affecting the final precipitation analysis is accuracy and reliability of the raingauge stations In order to eliminate the influence of raingauge stations providing evidently erroneous data the SHM is developing the blacklisting technique which temporarily excludes such stations from the analysis Currently the stations can be put into the blacklist only manually but development of the automated blacklisting is expected in near future Brief description of the RADOLAN system RADOLAN is a routine method for the online adjustment of radar precipitation data by means of automatic surface precipitation stations ombrometers which has started on a project base at DWD in Doc No SAF HSAF PVR 02 1 1 Product Validation Report PVR 02 iseue Revision Index ia Product H02 PR OBS 2 Date 30 09 2011 Page 153 177 1997 Since June 2005 areal spatial and temporal high resolution quantitative precipitation data are derived from online adjusted radar measurements in real time production for Germany The data base for the radar online adjustment is the operational weather radar network of DWD with 16 C band sites on the one hand and the joined precipitation network of DWD and the federal states with automatically downloadable ombrometer data on the o
99. average but also for seasons averages The seasons are reported in table 39 Winter Spring Summer Autumn Dec 2009 Jan and Feb March April and June July and August Sept Oct and Nov 2010 May 2010 2010 2010 Table 39 split in four sections one for each season reports the Country Team results side to side Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 hydrology ona Weer Product H02 PR OBS 2 Date 30 09 2011 Page 117 177 en HSAF Product Validation Report PVR 02 itie Nen Satellite Appl Fe 6 2 1The winter period e eee fw ot Po fu foe Version 2 2 radar radar_ radar radar radar_ gauge gauge gauge gauge gauge gauge _ lt Imm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt Imm h lt 1mm h lt 1mm h lt 1mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h Table 40 The main statistical scores evaluated by PPVG for H02 during the winter period The rain rates lower than 0 25 mm h have been considered as no rain In Table 40 it can be seen that the scores obtained by radar data are very similar to the scores obtained by rain gauge data for all the precipitation classes The RMSE evaluated for light precipitation rain rate lower than 1mm h has the highest value The difficulty to estimate small precipitation intensities is not only of the satellite prod
100. ble statistic of Rain Rate mm h for H02 vs radar data Left for 3rd June 2010 Right for whole June 2010 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 93 177 Product Validation Report PVR 02 class 1 class 2 class 3 class 4 class 1 class 2 class 3 class 4 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Rain Gauge RR Rain Gauge Figure 66 Contingency table statistic of rain rate mm h for H02 vs rain gauge data Left for 3rd June 2010 Right for whole June 2010 Results of the continuous statistic next table show negative Mean Error ME in both periods with both kind of ground data in the first class which means that H SAF product underestimates all kind of precipitation amounts For SD of 0 48 for radar data is the best analogue to the results for POD see above For detection of precipitation RR gt 0 25 mmh there are nearly the same results for both kind of ground data and for both periods which means the chosen period is representative for June 2010 Standard deviation SD with 1 51 mmh for this class is the highest for validation with RADOLAN for 3 June nevertheless the correlation coefficient CC with 0 62 is the best analogue to the results for POD see above The better results for CC and RMSE in comparison with that for HO1 validation are due
101. bles the result of the categorical statistic of the validation with both RADOLAN and rain gauge data are listed The results for validation with radar data for 3 June are worse than for the whole month June Probability Of Detection of precipitation RR gt 0 25 mmh was 0 30 with less False Alarm Rate of 0 13 and Critical Success Index is 0 29 Compared with results of HO1 validation the results are much worse for 3 June Results for H02 3 June were better than for the whole June The matter may be the fact that the rain events were particularly in small point areas which were not scanned by satellite 3 June 2010 H02 vs radar HO2 vs rain gauge RR gt 10 RR gt 0 25 RR gt 1 0 RR gt 10 poo 8 3 3 e p 014 018 100 p 026 023 000 HO2 vs rain gauge RR gt 0 25 Table 28 Results of the categorical statistic of the validation for whole June 2010 The contingency tables next two figures for both kinds of validation data show that only in the lowest class except for validation with radar data for whole June more than 50 of H02 data fall in the same class The results are worse than for H01 RRsatinclass 1 WRRsatinclass2 RRsatinclass3 Wm RRsat in class 4 class 1 class 2 class 3 class 4 class 1 class 2 class 3 class 4 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Radar RR Radar Figure 65 Contingency ta
102. btained for all HO2 data on the 27 of PEMPE ZO TO ene ne ee ee ee eee ere re 108 Figure 86 Percentage distribution of PR OBS 1 precipitation classes in the rain classes defined using rain gauges RG data on the 27th of September 2010 0 0 0 ceeseccccceesseecceeeeeseecceeeeeeseccesseeeneeeeeeeas 109 Figure 87 Synoptic situation on 15 August 2010 at 0 00 UTC eccceccseccseeeeceeeeeeeceseceeceseeneees 109 Figure 88 Instantaneous precipitation fields from 15 August 2010 observed by HO2 product left column and SHMU radar network right column corresponding to NOAA18 passage at 12 07 UTC top row and NOAA19 passage at 12 08 UTC second LOW cccccceeeeccceeeecccceesecsseeececeseeueessseeueseseeuaeseseeees 111 Figure 89 Atmospheric condition 20 10 2010 06 00 GMT ccccccccccssssecceeeeessecceseeeeseceeeeeeeeeeeeeeeas 112 Figure 90 Atmospheric condition 20 10 2010 12 00 GM ccccccccccsssseccceeeessecceeseeeseeceeeeeeeeeeeeeeas 113 Figure 91 Comparison of HO2 product and rain gauge ccccccesseccccessececeesecccceeseceeeeeeceeceeneceeeeeecesseees 113 Figure 92 Scatter diagram of rain gauge and HO2 product Red line is 45 degree line cecce 114 Figure 96 Rain gauge networks in PPVG cecccessccceesccceesceceesceceenceesenceeseueeceeceseeueeeseeesseneesseeeeseeeees 129 Figure 97 Correlation coefficient between raingauge pairs as function of the distances between the gauges Colours refer to the months of t
103. central Italy it is below 5 km while in Emilia Romagna Po Valley it is more than 20 km A study of the decorrelation distance between stations as function of the mutual distance has been carried out for the 2009 dataset The decorrelation distance is defined as the minimum distance between two observations that makes the Pearson correlation coefficient between the two measures decrease below e Results are shown in next figure where the decorrelation distance is plotted as function of the distance between stations It appears that there is a large variability of this parameter from higher values around 60 km for cold months when large precipitating systems dominate and reduces to roughly 10 km when small scale convection is more likely to occur warm months This points out that the distribution of gauges could be able to describe the spatial structures of precipitation fields in case of wintertime rainfall while may be inadequate for spring summer convective events 200812 200901 200902 200903 200904 200905 200906 200907 200908 200909 200910 200911 Figure 26 Correlation between rainrates detected by two close by stations as function of the distance between the two stations Colors refer to the month along 2009 In next figure the distribution of working stations over Italy is shown for a given day Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 57 177 Product Va
104. chments e analysis of the possible solutions for operational creation of the error maps and selection of the best one e creation the software if necessary Start Time End time March 2011 November 2011 Second Report 30 of November 2011 Coordinator Bozena Lapeta IMGW Poland Members Ibrahim Somnez ITU Turkey Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 168 177 Product Validation Report PVR 02 H SAF project Validation Programme WP 6100 Working Group 5 Geographical maps distribution of error Bozena Lapeta IMGW Poland First report March 31 2011 Introduction The Working Group 5 aims at creating geographical maps of H SAF products error and analyzing its usefulness for H SAF validation The idea of this work stemmed from hydrological validation community that is interested in distribution of the error over the catchments In this report the results obtained during the first step of WG5 activities aiming at selection of the best method for mean error specialization are presented Selection of spatialisation algorithm first results The most important issue in creating geographical distribution of any parameter is the algorithm for spatial interpolation As there is no universal spatial interpolation method that can be applied for any parameters the first step in the creation of maps of H SAF precipitation products error was
105. cipitation data integration product formats data coverage products availability and quality e collect and study INCA methods and products and to consider how these methods meet requirements of H SAF precipitation products validation Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 150 177 Product Validation Report PVR 02 e compare precipitation field reconstructed using radar data raingauges data and INCA products for some case studies Start Time End time December 2010 March 2011 First Report 31 of March 2011 Second step e develop common upscaling software tools for proper upscaling of identified INCA products into native H SAF product s grids e select extreme weather events and make case studies on comparison the INCA and H SAF relevant precipitation products e in case of positive case studies to perform batch validation of H SAF products and provide standard validation statistical outputs continuous and multicategorical statistics Start Time End time April 2011 November 2011 Second Report 31 of July 2011 Final Report 30 of November 2011 Composition of the working group Coordinator Jan Kanak SHMU Participants members of H SAF consortium which are in parallel involved in development of INCA products Belgium Germany Italy Hungary Slovakia Turkey FIRST REPORT Coordinator Jan Ka k Slovakia Participants Claudi
106. correction for 1 hourly data It is important to note that techniques used for accumulation are numerous even within the same country the can differ from one accumulation period to another E g in Hungary the 3 6h accumulations are derived from summing up the interpolation of the 15minute frequent measurements into 1 minute intervals whereas the 12 and 24 h accumulations are summed up from 15 minute measurements but corrected with rain gauge data All above implies that more probably the quality and error of rainfall and rain rate accumulations is differing from one country to another and cannot be homogeneously characterized Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 40 177 Product Validation Report PVR 02 4 3 4 Some conclusions Maintenance All the contributors declared the system are kept in a relatively good status Correction factors for error elimination These correction factors are diverse in the countries not homogeneous distribution of correction methods gt all contributors compensate for non meteorological echoes Clutter gt RLAN interferences implemented in Hungary Slovakia in development gt Poland and Slovakia correct attenuation In other countries it is not accounted for gt Some of the countries are testing new procedures for dealing with VPR Italy and Partial Beam Blockage PBB effects VPR Vertical Profile of Reflectivity used i
107. cscscacacacacacacacasacacacacacacscacacacacacacacacacacacacacetecececececeees 108 Table 35 Scores for continuous statistics for precipitation threshold of 0 25 MM h sssccrcrreeeeeeees 111 Table 36 Scores for dichotomous statistics for precipitation threshold of 0 25 MM h scese 112 Table 37 Statistic scores for HO2 ssssssssssssnssrrnssrrssrrnssrresrrrnsrtrustresrtesstresrtessrresstesstersstensreesrtresrereereesns 114 Table 38 Accuracy requirements for product PR OBS 1 RMSE cccccccsssessseeseeseseeesssseeeseseeees 115 Table 39 split in four sections one for each season reports the Country Team results side to side 116 Table 40 The main statistical scores evaluated by PPVG for HO2 during the winter period The rain rates lower than 0 25 mm h have been considered as NO rain eeeeeeccceecccesccccccceccececensecectecenseeeaaecs 117 Table 41 The main statistical scores evaluated by PPVG for HO2 during the spring period The rain rates lower than 0 25 mm h have been considered as NO rain ceeceeeeeccccsecccceecececceccuccccceececaececueeeensneeens 118 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 7 177 Product Validation Report PVR 02 Table 42 The main statistical scores evaluated by PPVG for H02 during the summer period The rain rates lower than 0 25 mm h have been considered AS NO rain eceeeeccceecccecccecccsecccccenecececenseceasecs 119 Tab
108. d only re calculated into instantaneous precipitation Values of errors in case of instantaneous precipitation can be significantly higher because of short time spacing Therefore it is planned in the future to calculate errors of radar measurements separately for instantaneous and for cumulated precipitation Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 149 177 Product Validation Report PVR 02 12 Annex 5 Working Group 3 INCA Precipitation for PPV PROPOSAL The precipitation ground reference can be only based on certain conceptual models The validation activity inside H SAF project is composed by hydrological and product validations Precipitation captured by river basin is transformed by set of processes into the river discharge This set of processes is described by hydrological discharge models and by river discharges measured by hydrological equipments Moreover validation of precipitation products cannot be overcasted by only an evaluation of methods describing transformation of precipitation into river discharge For this reason a common validation methodology to compare satellite precipitation estimations with ground data radar and rain gauge inside the H SAF project has been defined The validation of precipitation field is a difficult task and a continuous study of possible validation methodology improvement is necessary The Precipitation Product Validation
109. d that is not possible to consider radar and raingauge fields like the reference and the accuracy indicated in the table 63 RMSD is the degree of closeness of measurements of a quantity to its actual reference value The reference value of precipitation fields is not available and the measurement available are a limited picture of the reference Then it is important to evaluate which are the limits of available reference and then to understand the sources of errors of data used to evaluate the satellite outputs Taking in account this consideration a direct comparison of the requirements with the result of validation is not correct since they have different meanings the requirements indicate what error is allowed by the user to the satellite product to be significantly useful threshold or to produce a step improvement in the application target or to produce the maximum improvement before entering saturation optimal it_is the RMSE of satellite v s reference the result of validation activities indicate the difference between the satellite measurement and the ground measurement utilized as a reference it is the RMSD of satellite v s reference PR OBS1 Requirements Result of E threshold validation Accuracy RMS gt 10 mm h Accuracy RMS 1 10 mm h Accuracy RMS lt 1 mm h Table I Simplified compliance analysis for product PR OBS I PR OBS Requirements MSUN a validation Accuracy RMS gt 10 mm h Accuracy RMS
110. e 62 177 a Observed Distance North km Distance East km b Corrected Z ABZ 150 100 StS Sa a ES Distance North km Oo BE aaa S22 RS ens SEE EES SITE ORR ere es 100 F A 1 50 O ee CO Cl ne Senn ee neene eee ene 1 0 150 100 50 0 50 100 150 Distance East km Figure 32 Measured upper panel and attenuation corrected lower panel PPI 1 0 deg of reflectivity observed on 09 14 08 at 0500 U T C by the polarimetric radar operated by Piemonte and Liguria regions Although several approaches with different degree of sophistication have been proposed in the last years the procedure named APDP proposed in Vulpiani et al 2007 has been chosen to be implemented for its physical adaptability and operationally oriented architecture APDP Adaptive PhiDP method is an iterative correction of attenuation based on the use of Fdp that taking advantage from the classification of hydrometeors Marzano et al 2006 2007 adapt the coefficients g h dp to the observed physical conditions Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 yaa ons nol Product HO2 PR OBS 2 Date 30 09 2011 Page 63 177 The EUMETSAT PREP er Product Validation Report PVR 02 2 usa p As an example previous figure shows the 1 0 degree PPI of measured upper panel and attenuation corrected lower panel reflectivity observed on 09 14 08 at 0500 U T C by the polarimetric radar
111. e HO2 make the gaussian filter overlapping radar data so that the central pixel C C corresponds to HO2j HO2 and the y axis has the same direction of the scanline Multiply each element of G for the closest radar measurements RRpigh lat lon and sum the products M K RRiw gt G m k RRrie m lk I1 Following this procedure it is obtained for each FOV and SCANLINE a value RRoy RRiow FOV SCANLINE which represents the matrix of validation used versus AMSU B estimates This scheme has been suggested by the precipitation developers of CNR ISAC and it has been adopted by the PPVG One of the Radar WG and Rain Gauge WG next steps is to develop a common code for the up scaling of radar data versus AMSU B grids following this technique The code will be an evolution and optimization of the code already available by Belgium Van de Vyver H and E Roulin 2008 and Italy A Rinollo All participants of validation task will use not only the same technique but the same software Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 27 177 Product Validation Report PVR 02 3 7 Temporal comparison of precipitation intensity Taking to account the revisiting time of the HO2 3 4 hours it was decided during the first validation workshop in 2006 to perform a direct comparison between the satellite and radar precipitation intensity maps The revisiting time of the
112. e Nationale de la Recherche Scientifique of France DMSP Defense Meteorological Satellite Program DPC Dipartimento Protezione Civile of Italy EARS EUMETSAT Advanced Retransmission Service ECMWF European Centre for Medium range Weather Forecasts EDC EUMETSAT Data Centre previously known as U MARF EUM Short for EUMETSAT EUMETCast EUMETSAT s Broadcast System for Environmental Data EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FMI Finnish Meteorological Institute FTP File Transfer Protocol GEO Geostationary Earth Orbit GRAS SAF SAF on GRAS Meteorology HDF Hierarchical Data Format HRV High Resolution Visible one SEVIRI channel H SAF SAF on Support to Operational Hydrology and Water Management IDL Interactive Data Language IFOV Instantaneous Field Of View IMWM Institute of Meteorology and Water Management in Poland IPF Institut fur Photogrammetrie und Fernerkundung of TU Wien in Austria IPWG International Precipitation Working Group IR Infra Red IRM Institut Royal M t orologique of Belgium alternative of RMI ISAC Istituto di Scienze dell Atmosfera e del Clima of CNR Italy ITU istanbul Technical University in Turkey LATMOS Laboratoire Atmospheres Milieux Observations Spatiales of CNRS in France LEO Low Earth Orbit LSA SAF SAF on Land Surface Analysis M t o France National Meteorological Service of France METU Middle East Technical University in Turkey MHS Microwave Humidity Sounder
113. e Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 16 177 Product Validation Report PVR 02 f m ON ae ot gt f afa f F E oS AA F a g ri y Direction A of Flight paas Figure 3 Geometry of cross track scanning for AMSU Since the incidence angle changes moving cross track the effect of polarisation also changes thus the information stemming from dual polarisation would be very difficult to be used and in effect the various frequencies are observed under a single polarisation V or H The resolution is constant for all frequencies in AMSU A 48 km at s s p and AMSU B MHS 16 km at s s p The NOAA satellites are managed by NOAA MetOp by EUMETSAT Both NOAA and MetOp provide direct read out the real time transmitter of MetOp suffered of a failure but now transmission of data over Europe has been resumed For more information please refer to the Products User Manual specifically volume PUM 02 2 2 Algorithm principle The baseline algorithm for PR OBS 2 processing is described in ATDD 02 Only essential elements are highlighted here Next figure illustrates the flow chart of the AMSU MHS processing chain The EUMETSAT Doc No SAF HSAF PVR 02 1 1 Sotelte Amica Product Validation Report PVR 02 Foci Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 17 177 PRECIPITATION RATE Figure 4 Flow chart of the AMSU MHS precipitat
114. e confirmed by evaluating the size of the error associated with the limits of the comparison technique 2 It must be considered that the total RMSD is affected by other than satellite terms one of which RMSEground very difficult to be reduced and the other one RMSEcomparison possibly dominant and also very difficult to be reduced This tells us that the validation figures have a large component which is independent from the structure of the algorithm 3 However the case for continuing algorithm improvement is very strong Data are produced for being used and the better the quality the higher the impact The fact that the current validation Doc No SAF HSAF PVR 02 1 1 Product Validation Report PVR 02 iseue Revision Index ia Product H02 PR OBS 2 Date 30 09 2011 Page 177 177 methodology cannot completely evaluate the intrinsic error of satellite data is regrettable but should not prevent a better representation of the physics in the retrieval model 4 The case for continuing the validation activity essentially as it is now or improving it if considered cost effective is also very strong since it is necessary to continuously watch that the product generation chain works correctly
115. e generated products are then disseminated to all institutions composing the national network Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 The EUMETSAT ee Product Validation Report PVR 02 E usa p Support to Operational a Product H02 PR OBS 2 Date 30 09 2011 Page 60 177 Volume Data Site State Figure 30 Architecture of the Italian radar network Data processing Data processing and product generation are here briefly described In particular attenuation correction hydrometeor classification vertical profile of reflectivity correction and rainfall estimation will be treated in the following sections Radar data quality As known any fruitful usage of radar data either for quantitative precipitation estimation or just for operational monitoring must deal with a careful check of data quality Figure 31 schematically shows the operational processing chain that is applied within the system DATAMET software system for radar remote control product generation visualization system maintenance and data archive developed by DATAMAT S P A Ground clutter anomalous propagation beam blockage effects are routinely mitigated through the application of the decision tree method proposed by Lee et al 1995 for single polarized systems Dual polarized systems provide additional observables such as differential reflectivity correlation coefficient and their texture that can be used to further reinforce the tr
116. e monthly contingency tables and the Statistical scores The results are showed for radar and rain gauge land and coast area in the three precipitation classes defined in table 7 The rain rates lower than 0 25 mm h have been considered as no rain The precipitation ground networks instruments and data used for the validation of HO2 have been described in Chapter 4 To assess the degree of compliance of the product with user requirements all the PPVG members provided the long statistic results following the validation methodology reported in Chapter 3 For product H02 the User requirements are recorded in Table 38 Precipitation range threshold target optimal gt 10mmh J 5 1 10mmh 12 105 ooo lt Iimmh w w wo Table 38 Accuracy requirements for product PR OBS 1 RMSE Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 116 177 Product Validation Report PVR 02 This implies that the main score to be evaluated has been the RMSE However in order to give a more complete idea of the product error structure several statistical scores have been evaluated as reported Mean Error Standard Deviation SD and Correlation Coefficient CC Probability Of Detection POD False Alarm Rate FAR and Critical Success Index CSI These scores have been defined in Section 3 7 The long statistic results obtained in Belgium Hungary Germany Italy
117. e satellite transmission facilities the availability of acquisition stations the processing time required to generate the product and the reference dissemination means Direct read out is provided by all NOAA satellites and after partial recovery from the AHRPT transmitter failure also by MetOp A After adding the processing time we have e timeliness 6 0 5 h The accuracy RMS is the convolution of several measurement features random error bias sensitivity precision To simplify matters it is generally agreed to quote the root mean square difference observed reference values The accuracy of a satellite derived product descends from the strength of the physical principle linking the satellite observation to the natural process determining the parameter It is difficult to be estimated a priori it is generally evaluated a posteriori by means of the validation activity 3 Validation strategy methods and tools 3 1 Validation team and work plan To evaluate the satellite precipitation product accuracy a Validation Group has been established by the beginning of the Validation Phase in the H SAF project The Precipitation Product Validation team is composed of experts from the National Meteorological and Hydrological Institutes of Belgium Bulgaria Germany Hungary Italy Poland Slovakia and Turkey fig 5 Hydrologists meteorologists and precipitation ground data experts coming from these countries are involved in the prod
118. eam extent of 200 km 0 10 E 20 E 30 E 40 E Figure 13 Radar networks in PPVG The first step was to collect characteristics polarization beam width maximum range range resolution scan frequency geographical coordinates scan strategy elevations of the radar networks which composes the PPVG adopted processing chain and the generated products including Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 38 177 Product Validation Report PVR 02 the quality map if any The results of the overview of different radar capacities and instruments in each of the participating countries are here reported 4 3 2 The instruments In the PPVG group there are 54 C band radars used or in the plan to be used Their distribution in the countries is Belgium 1 radar Germany 16 radars not BfG products Hungary 3 radars Italy 18 radars Slovakia 2 radars Poland 8 radars Turkey 6 radars These radars cover wide range of geographical area from the longitude 5 50562 in Wideumont Belgium to the most Eastern area with longitude 32 58 15 in Ankara Turkey and from the Northern latitude of 54 23 03 17 in Gda sk Poland to the latitude of 36 53 24 in Mugla Turkey and lat 37 462 in Catania Italy The Radars are built at different elevations above the sea level In mountainous countries they are placed at elevations more than 1000m above sea le
119. ed for case analysis SEVIRI images courtesy of University of Dundee NEODAAS Weather charts courtesy of Wetterzentrale NCEP and METOFFICE Comparison This deep convective case has been observed by two different AMSU sensors on board NOAA NP and NOAA NN with a time lag of only 10 minutes In the figure below the two estimates are presented top panel with the hourly cumulated raingauge map at 13 00 UTC please note zero rainrate gauges are not shown The rain rate patterns in the two estimates are rather similar but some significant variations in the shape of rain areas and in the rain rate values can be due to the time lag between the images 10 minutes and to a different viewing angle of the two sensors The NOAA NP estimate shows higher precipitation rates close to the end of the scale 20 mm h_ while the raingauge cumulated maximum value is 34 mm h The considered statistical parameters indicate a best matching for the NOAA NN Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 102 177 Product Validation Report PVR 02 estimate with the following values ETS 31 27 FAR 43 44 POD 49 42 HSS 37 29 where values within brackets refer to the NOAA NP overpass Overestimation occurs for both estimates around the large convective cells while underestimation takes place mainly in case of very small structure not detected by h02 because the rather large IFOV
120. el by the simple adding of a constant quantity in dBZ units The original algorithm is modified and integrated with a VPR diagnosis and analysis phase to handle different operative problems Fornasiero et al 2008 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 ryan nator Product H02 PR OBS 2 Date 30 09 2011 Page 64 177 The EUMETSAT Network of Satellite Application Facilities Product Validation Report PVR 02 As an example next figure shows the measured upper panel and VPR corrected lower panel PPI of reflectivity observed on 03 25 07 at 0930 U T C by the polarimetric radar located in Gattatico Emilia Romagna Italy a 25 03 2007 09 30 GMT gat Servizio Idro Meteo Figure 34 Measured upper panel and VPR corrected lower panel PPI of reflectivity observed on 03 25 07 at 0930 U T C by the polarimetric radar located in Gattatico Emilia Romagna Italy Rainfall estimation Quantitative rainfall estimation is one of the first application of the radar network The estimation of rainfall at the ground takes advantage of the dense network of raingauges spread all over Italy This Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 65 177 Product Validation Report PVR 02 network is one of the most dense in the world with more than 1700 gages and it is used for tuning and correcting the operational Z R algorithms of non po
121. eneeessenecessegaesetsenes 89 Figure 62 Synopsis for Central Europe for O3rd June 2010 FU Berlin http wkserv met fu berlin de a N A EA E ke cuna Suu abe bane bue suvsune E E E A S A 90 Figure 63 12h totals of precipitation ending FU at 3rd June 2010 7 UTC esssssssssesesessrresessrerrsesrerere 91 Figure 64 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 06 03 01 50 UTC station Athens and for RADOLAN RW left filled raster 2010 06 03 01 50 UTC and station data AA No Ove 20 a TS E E E E se ea ee a 91 Figure 65 Contingency table statistic of Rain Rate mm h for HO2 vs radar data Left for 3rd June 200 RENCA WOE JUNG Z 0 oere EE 92 Figure 66 Contingency table statistic of rain rate mm h for HO2 vs rain gauge data Left for 3rd June 2010 RENG TOT Wile JUNG 20 10 czas cecacansacsccaanasugenancesesnancnegsansoecas E EET 93 Figure 67 Synopsis for Central Europe for 05th December 2010 FU Berlin http wkserv met fu FS POU IN gS D EN NE EEE EE E N E etc serene ede deste esto vce E N N te 94 Fee o ONTO a Or precip all Old p ai 94 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 10 177 Product Validation Report PVR 02 Figure 69 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 12 05 02 29 UTC and for RADOLAN RW left filled raster 2010 12 05 02 50 UTC and station data right dots 2010 12 05 0 00 UTE
122. entary in assessing the accuracy of the implemented algorithms Large statistics helps in identifying existence of pathological behaviour selected case studies are useful in identifying the roots of such behaviour when present The main steps of the validation procedure are e ground data error analysis radar and rain gauge e point measurements rain gauge spatial interpolation ee Product Validation R PYRO Doc No SAF HSAF PVR 02 1 1 Sotellite seelcaion roquc alidation Report Facilities a H S AF p Issue Revision Index 1 1 anony a Ware Product H02 PR OBS 2 Date 30 09 2011 Page 21 177 e up scaling of radar data versus SSMI grid e temporal comparison of precipitation products satellite and ground e statistical scores continuous and multi categorical evaluation e case study analysis 3 4 Ground data and tools used for validation Both rain gauge and radar data have been used until now for H01 validation As said in the previous section during the last Precipitation Product Validation Workshop held in Bratislava 20 22 October 2010 it has been decided to set up Working Groups to solve specific items of the validation procedure and to develop software used by all members of the validation cluster A complete knowledge of the ground data characteristics used inside the PPVG has been the first item of the working groups this is necessary to understand the validation results and to define the procedure to select t
123. er Regione Friuli Venezia Giulia Polarization single Linate and 12 Fiumicino Owner ENAV Polarization single Brindisi Owner Italian Air Force Polarization single Grazzanise Owner Italian Air Force Polarization single Pisa Owner Italian Air Force Polarization single Istrana Owner Italian Air Force Polarization single The first C band radar of new generation directly managed by DPC located in Tuscany Italy is operational since the beginning of 2008 whereas six C band radars including two dual polarized systems will be operational by the end of 2008 see Figure 29 As an example the national mosaic CAPPI at 2000 m is shown in next figure relatively to the event of 04 18 08 at 0015 U T C 18 04 2008 ore 00 15 Figure 29 Graphical mosaic of reflectivity CAPPI at 2000 m for the event of 04 18 08 at 0015 U T C As depicted before each Doppler Radar System either dual or single polarized PDRS or DRS are connected by satellite links to the two National Radar Primary Centres RPC located in Roma DPC and Savona CIMA Research Foundation in order to mainly ensure the remote control through the RRC server and products generation through the RPG server The RPC located in Savona works as backup centre in order to continuously ensure the system functioning The subsystem RAC Radar Archive Centre is devoted to archive and manage radar data and products by means of a relational database Th
124. ero A P P Alberoni G Vulpiani and F S Marzano Reconstruction of reflectivity vertical profiles and data quality control for C band radar rainfall estimation Adv in Geosci vol 2 p 209 215 2005 http www adv geosci net 2 index html R Bechini L Baldini R Cremonini E Gorgucci Differential Reflectivity Calibration for Operational Radars Journal of Atmospheric and Oceanic Technology Volume 25 pp 1542 1555 2008 http journals ametsoc org doi pdf 10 1175 2008JTECHA1037 1 Silvestro F N Rebora and L Ferraris 2009 An algorithm for real time rainfall rate estimation using polarimetric radar Rime J Hydrom 10 227 240 Vulpiani G P Pagliara M Negri L Rossi A Gioia P Giordano P P Alberoni Roberto Cremonini L Ferraris and F S Marzano 2008 The Italian radar network within the national early warning system for multi risks management Proceed of 5th European Radar Conference ERAD Helsinki Finland http erad2008 fmi fi proceedings extended erad2008 0184 extended pdf Vulpiani G M Montopoli L Delli Passeri A Gioia P Giordano and F S Marzano 2010 On the use of dual polarized C band radar for operational rainfall retrieval in mountainous areas submitted to J Appl Meteor and Clim http www erad2010 org pdf oral tuesday radpol2 5 ERAD2010 0050 pdf Hungary P ter N meth Complex method for quantitative precipitation estimation using polarimetric relationships for C band radars
125. errors can be mitigated and reduced but in general not eliminated by a careful maintenance of the instrument A number of a posteriori correction strategies have been developed in order to correct precipitation data measured by raingauges but mainly apply at longer accumulation intervals daily to monthly Wagner 2009 Country a iii detectable MASU CELEC TADIE Heating system cumulation eee ametan S el _ a eo Germany 0 05 mmh 30000 Italy 02mm NAH N BO Table 9 Summary of the raingauge characteristics only 300 out of 1800 gauges are heated information not available at the moment a value about 300 mmh can be assumed for tipping bucket raingauges Most of these shortcomings could be avoided by using instruments based on different principle or mechanisms The German network and a part of the Bulgarian network as an example are equipped by precipitation weighting gauges that allow continuous precipitation both solid and liquid Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 35 177 Product Validation Report PVR 02 measurements with higher accuracy Other option could be the use of disdrometers which give more information about the precipitation structure and a more accurate rainrate measure In previous table relevant characteristics of the raingauges used in the different countries are reported 4 2 3 Data processing The partners of the Validation
126. eseeenaes 55 4 9 2 Radar datd Ree ee 58 4 10 Ground data in Poland IMWDM cccccccsssccccseecccesecesceesecsseeeesseeeseceseeueeseseuueeseeeusesseeeneeeeeas 65 AMO TRAN E N AAA SRA 65 41l GrounddatainSlovakia SHMU scsssssaissssscseeisseisesiaieaeesiaiesnisaieesrasiseveiaaissssiaasanesisiseenaaieiernneess 67 4 11 1 Rain gauge s ssessessssessessrssessssesrrssosesressrsrosessessresoseosrorrssosessesressesesseoereseoseosesressoseeseereesee 67 A RON A A E E ne ee eee 68 4 12 Ground adatain TUKEY ee Se ee ee ee ne ene ene EE 71 7 ee 2 RN o 2 02d lt n E ee 71 AlS Conclusio gee ee ee 75 DS Va One aE Case stiidy alldly SIS oeae eae 76 oi AACO a 76 5 2 Case study analysis in Belgium IRM cccccccccssseccccccesecccceeeeeseccceeeeeseecceeeueeseceeseeeeeeeeeeeas 77 5 2 1 Case study August 14 17 POLO EI T EEE IAI I E T T T 77 5 2 2 Case study August 22 24 POIO E E E 80 5 2 3 Case study November D is PON ea ach aah chain de A taahaaahanahiaahahapaaenen 83 5 3 6 Case Study analysis In Germany BIG seccsssscscsreessansernderssanededadosanneaboevsainbabosesaasnatsbatsiabadebetsiatsies 86 5 3 1 Case study August 7 2010 River Nei e Oder Spree and Elbe catchments 86 5 3 2 Case study June 3 2010 River Danube catchment c ccccccsssssecesesesesesesessesesesesesceeseees 90 5 3 3 Case study December 5 6 2010 River Rhine catchment ccccccssccescseseseeececeseseeee 94 5
127. esolution to the common radar grid the radar upscaling techniques can be applied also on the INCA or RADOLAN data In frame of the unification of the validation methodologies the same common upscaling software could be shared between both radar WG2 and INCA WG3 working groups in the future References T Haiden A Kann G Pistotnik K Stadlbacher C Wittmann Interated Nowcasting through Comprehensive Analysis INCA System description ZAMG Vienna Austria 4 January 2010 Andr Simon Alexander Kann Michal Ne amp tiak Ingo Meirold Mautner Akos Horvath Kalman Csirmaz Olga Ulbert Christine Gruber Nowcasting and very short range forecasting of wind gusts generated by deep convection European Geosciences Union General Assembly 2011 Vienna Austria 03 08 April 2011 Ingo Meirold Mautner Benedikt Bica Yong Wang INCA CE A Central European initiative in nowcasting applications Central Institute for Meteorology and Geodynamics Hohe Warte 38 1190 Vienna Austria Ingo Meirold Mautner Yong Wang Alexander Kann Benedikt Bica Christine Gruber Georg Pistotnik Sabine Radanovics Integrated nowcasting system f or the Central European area INCA CE Central Institute for Meteorology and Geodynamics ZAMG Hohe Warte 38 1190 Vienna Austria Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 162 177 Product Validation Report PVR 02 13 Annex 6 Working Group 4 PR A
128. gary and Gianfranco Vulpiani DPC Italy Participants Belgium Germany Hungary Italy Slovakia Turkey FIRST REPORT AND SECOND REPORT Reported by Eszter Labo Hungarian Meteorological Service Contributors Gianfranco Vulpiani DPC Italy Angelo Rinollo Belgium Jan Kanak and Luboslav Okon Slovakia Firat Bestepe Turkey Rafal lwanski Poland Claudia Rachimow Germany Description of tasks In the HSAF project satellite based precipitation estimations are compared regularly with the radar derived precipitation fields However radar rainfall products are influenced by several error sources that should be carefully analyzed and possibly characterized before using it as a reference for validation purposes However we have to emphasize that the radar data used for validation purposes is not developed by the validation groups themselves They are developed within specialized radar working teams in many of the countries Therefore it should not be the aim of the work of the Radar WG to improve the radar data used however it is specifically expected from the current activities to characterize radar data and error sources of the ground data coming from the radar networks of the Precipitation Validation Group PPVG Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 137 177 Product Validation Report PVR 02 Main error sources of radar rainfall estimations are listed in t
129. ge 157 177 Original radars 4 F 6 INCA Raingauges 201008150800 KEG he 201008150800 Ne ad 4 F D INCA Radars raingauges m 201008150800 mE a gt i a But Ni Sores J intensity mm h E Figure 109 Precipitation intensity field as in previous figure except for 8 00 UTC Statistical analysis of the PR OBS 2 product on selected precipitation events As a first step towards utilizing the INCA precipitation analyses for the H SAF validation it has been decided to perform at SHM a statistical analysis of the H SAF products using the precipitation fields from INCA radars and raingauges as a ground reference data for selected precipitation events Since this task required modification of the SHM software currently used for upscaling radar data until now results for the PR OBS 2 product are only available In order to eliminate interpolation artefacts in the areas outside the raingauge network occurring in the INCA analyses only the PR OBS 2 data falling inside the Slovakia territory were taken into account in the statistical analysis Overall five precipitation events with different prevailing type of precipitation have been selected for the statistical analysis as listed in next table Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 158 177 Product Validation Report PVR 02 Period UTC Precipitation type 15 August 2010 00 00 2
130. ge of November 15th 2010 at 1 29 left compared with upscaled radar at 1 30 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 ee ecccceeeseecceeeeeeeeeeeeeeeeees 84 Figure 56 Time evolution of fraction area with rain average rain rate over this area threshold 0 25 mm h RMSE and ETS during the present case StUGY cccccssssssseeeeeeececccecsccseeeeseuueeeeeeeseeeceeeeeeeeeeess 85 Figure 57 Synopsis for Central Europe for 07th August 2010 FU Berlin http wkserv met fu berlin de ee entree ha eee cel iced teers tected tad DEEA S AEE EA AAE AENA 86 Figure 58 two day totals ending at 9th August O UTC interpolated on a 1 x1 evaluation grid as derived from SYNOP messages Global Precipitation Climatology Centre GPCC operated by DWD 86 Figure 59 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 08 07 11 58 UTC station Rome and for RADOLAN RW left filled raster 2010 08 07 12 50 UTC and station data men dote 201008307 ec OOUT irri 87 Figure 60 Contingency table statistic of rain rate mmh h for HO2 vs radar data Left for 7th August 2010 Right for whole August 201 Orca ecseeescsansacecctesevncsesecunesesacautenacsanaevecaueinansasiteanenetieesarsatsauaeeeenieae 89 Figure 61 Contingency table statistic of rain Rate mm h for H02 vs rain gauge data Left for 7th August 2010 Right for whole August 2010 ccccccessscccceeseccceeesececeeeeeceeeeseceeseeseceseu
131. ges 37 Figure 14 Radar SCAN PrOCOCUle ssscccssscccessccceescccsscecsscccaescecsencecseneeseenceesencesseeeseeseesseeeeeeeeeseneeteges 38 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product Validation Report PVR 02 Product HO2 PR OBS 2 Date 30 09 2011 Page 8 177 Figure 15 Coverage of Europe by the INCA and RADOLAN SyYStEMS ccccccessscceeeesececeeesececeeeeceesenaeees 43 Figure 16 Procedure of the RADOLAN online adjustment hourly precipitation amount on 7 August 710 7 Gs le OUTO Ieee eRe en a nT aD ne EA IN TIT EAEE N E SERENE AH OUTER ER CRAION CEN ENENTEN NORE S NRO NERT N 45 Figure 17 Meteorological radar in BelSiuim cccccsscccesscccessceceesececesceceenceceeseeseeeeeeeaeesseeeseeeeeseeessees 47 Figure 18 Distribution of the raingauge stations of Iskar River BaSIN cccccsssccccesseccecesececeeeeceeseeneces 48 Figure 19 Distribution of the raingauge stations of Chepelarska River BaSin cscccccssseceeeeseeeeeeeseees 49 Figure 20 Distribution of the raingauge stations of Varbica River Basin cccccccssseceeceesececeeeceeeeeseees 49 Figure 21 Networkof rain gauges in Germany sessiersiersisicisieiiii a a e 51 Figure 22 Pluvio with Remote Monitoring MOdule cccccsecccsseccceseecenceceeneeceeneeeeeeeeseeeeseaeeeseaeeeeees 51 Figure 23 Left radar compound in Germany March 2011 Right location of ombrometers for online calibration in RADOLAN squa
132. ght panel H02 product at 11 UTC left panel Comparison HO2 well detected the precipitation area over the country HO2 derives lower values than the radar measured Conclusions The HO2 well detects the precipitation area but it underestimates the precipitation values 5 5 Case study analysis in Italy Uni Fe 5 5 1 Case study July 06 2010 Description On July 06 the Azores anticyclone avyected very warm and moist air on the Tyrrhenian coasts where a weak trough induced cyclonic circulation and instability in the early morning After 10 00 UTC deep convections initiated in the Po Valley and in central Italy along the Apennines chain causing waterspouts along the northern Adriatic coasts hail falls and supercells storms in Central Italy The EUMETSAT Produa Wahcaenk t PVR 02 Doc No SAF HSAF PVR 02 1 1 HSAF P Facilities M gt Issue Revision Index 1 1 Product HO2 PR OBS 2 Date 30 09 2011 Page 101 177 O6JUL2010 00Z 500 hPa Geopotential gpdm und Bodendruck hPa gt E Daten Reanalysis des NCEP C Wetterzentrale www wetterzentrale de SEVIRI HR VIS image at 12 00 on July 06 shows a well developed convective cluster over central Italy while small scale scattered convection is present along the Apennines chain Some of these small systems are expected to grow in the following hours Data used Reference data Italian hourly raingauges network provided by DPC Ancillary data us
133. ground data has to be on satellite native grid Generally one or two rain gauges are ina SSMI pixel but radar instruments provide many measurements within a single SSMI pixel For this reason an up scaling technique is necessary to compare radar data with the HO1 precipitation estimations on the satellite native grid The precipitation data in the retrieval product HO2 is based on the instruments AMSU A and AMSU B or MHS flown on NOAA and MetOp satellites These cross track scanners provide images with constant angular sampling across track that implies that the IFOV elongates as the beam moves from nadir toward the edge of the scan The elongation is such that e for AMSU A the IFOV at nadir is 48 x 48 km at the edge of the 2250 km swath 80 x 150 km e for AMSU B and MHS the IFOV at nadir is 16 x 16 km at the edge 27 x 50 km Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 jyacgyons wale Product HO2 PR OBS 2 Date 30 09 2011 Page 25 177 The EUMETSAT OS a ar e Product Validation Report PVR 02 E HSA p HO2 follows the scanning geometry and IFOV resolution of AMSU B scan so that each pixel along the scan has a precipitation value representative for an elliptical region next figure y scan direction x direction of flight y z vertical on the FOV n FOV 2 FOV 1 Figure 8 Geometry Geometry of cross track scanning for AMSU 3 6 1 Average of hi res ground validation data Radar instrument
134. gs descriptions modified from Shafer et al 1999 oo ceccccceesecceeeeseseseeeeesseeeeess 72 Table 21 Scores obtained with the comparison with radar data in MM h assesseer 79 Table 22 Scores obtained with the comparison with radar data in MM h assesses 82 Table 23 Scores obtained with the comparison with radar data in MM h sser 85 Table 24 Results of the categorical statistic of the validation for 7th August 2010 eeen 88 Table 25 Results of the categorical statistic of the validation for whole August 2010 ccccceeeeees 88 Tapie 26 COMINUOUS STALS STIC Rare ari nna re eo 90 Table 27 Results of the categorical statistic of the validation for 3rd June 2010 ec ecccceeeeeeeeeeeees 92 Table 28 Results of the categorical statistic of the validation for whole June 2010 eee eeeeeeeeeeeees 92 Tape FC OU S STA SUN neoion EEEE EE AEAEE AE ENEE A AEEA EAA aioe A AE 93 Table 30 Results of the categorical statistic of the validation for 5 6th December 2010 sanese 96 Table 31 Results of the categorical statistic of the validation for whole December 2010 cscs 96 Wale 2 COMLIMUOUS SAIU E EEEE 97 Table 33 Results of the categorical statistics obtained for PR OBS 1 on the base of all data available on the 15 AugustI0lO ceee E a Ea ea AE EE 105 Table 34 Results of the categorical statistics obtained for HO2 on the base of all data available on the 27 September 2010 ccccccceccccseccscccscscscscscscscscscscsce
135. h the weighted mean of two standard procedures Table 15 Precipitation data used at BfG for validation of H SAF products 4 7 1 Rain gauge The network The data used are compiled from 1300 rain gauges About 1000 are operated by DWD while about 300 are operated by other German authorities The average minimum distance between stations is 17 km The instruments The measurement instruments are precipitation sensors OTT PLUVIO of Company Ott 3 They continually and precisely measure quantity and intensity of precipitation in any weather based on balance principle with temperature compensation heated funnel and by an electronic weighing cell The absolute measuring error is less than 0 04 mm for a 10 mm precipitation amount and the long term 12months stability is better than 0 06 mm The operating temperature ranges from 30 C to 45 C The minimum detected quantity sensitivity is 0 05 mmh The maximum possible measured rain rate is 3000 mmh The operational accumulation interval theoretically is one minute The data processing Continuous automatic measurement of liquid and solid precipitation data are collected accumulated intervals from 1hour until 1day and provided as SYNOP tables by DWD These data are error http www ott com web ott de nsf id pa ottpluvio2 vorteile html OpenDocument amp Click Precipitation amount and intensity measurements with the Ott Pluvio Wiel Wauben Instrumental Department INSA IO
136. hanging with scale etc Therefore the results currently found are by far pessimistic in respect of what is the real product performance 7 2 Next steps On the base of the development phase it is possible to say that the ground data error characterization is necessary and that a validation of a common protocol is not enough Only the use of the same software can guarantee that the results obtained by several institutes are obtained in the same way To improve the validation methodology and to develop software used by all members of the validation cluster several working groups have been composed during the last Validation Workshop held in Bratislava 20 22 October 2010 see annex 1 2 3 4 5 6 7 On the base of published papers and the characteristics of the ground data available inside the PPVG the main next steps are foreseen in order to improve the validation methodology Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 126 177 Product Validation Report PVR 02 quantitative estimation of the errors introduced in the validation procedure comparing the instantaneous satellite precipitation estimation with the rain gauge precipitation cumulated on different intervals definition of a rain gauge and radar data quality check application of the data quality check to all radar and rain gauge data used in the PPVG definition of the optimal and minimal spatial density of rain gauge st
137. he Radar Working Group description document 1 system calibration 2 contamination by non meteorological echoes i e ground clutter sea clutter clear air echoes birds insects W LAN interferences partial or total beam shielding rain path attenuation wet radome attenuation range dependent errors beam broadening interception of melting snow contamination by dry or melting hail hot spots variability of the Raindrop Size Distribution RSD and its impact on the adopted inversion techniques St er ae Moreover several studies have been on radar quality assessments like S alek M Cheze J L Handwerker J Delobbe L Uijlenhoet R 2004 Radar techniques for identifying precipitation type and estimating quantity of precipitation COST Action 717 Working Group 1 A review Luxembourg Germany or Holleman l D Michelson G Galli U Germann and M Peura Quality information for radars and radar data Technical rapport 2005 EUMETNET OPERA OPERA_2005_19 77p Our main purpose for the first step was to collect characteristics polarization beam width maximum range range resolution scan frequency geographical coordinates scan strategy elevations of the radar networks which composes the PPVG adopted processing chain and the generated products including the quality map if any This report is intended to present the results of the overview of different radar capacities and instruments in each of the part
138. he Wallonia Region only in 3 river basins only covering the western part of Anatolia Data processing The partners of the Validation Group have been using a variety of different strategies to treat gauge data and to compare them with satellite estimates Some are using interpolation algorithms to get spatially continuous rainfall maps while others process directly the measurements of individual gauges All the data in the network except for cold months in Poland are quality controlled there is no information about the techniques used but usually quality control rejects data larger than a given threshold and in case of too high rainrate difference exceeding given thresholds among neighbouring gauges and between subsequent measures of the same instrument Table 52 summarizes the data pre processing performed in different Countries while Table 53 and Table 54 report the different matching approaches for HO1 HO2 and H03 H05 respectively As for the temporal matching the used approaches are rather homogeneous within the Groups instantaneous measurements are matched with next ground cumulated values over the different available intervals ranging from 1 minute Turkey to 1 hour Italy Germany Cumulated estimates obviously are compared to ground measured rain amounts over the same cumulation intervals As for spatial matching different approaches are considered also taking into account the different Spatial structure of the satel
139. he eumesa Product Validati R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Soelite Applicat ee H SAF rOauct Validation Report Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 46 177 Group of information tem GERMANY POLAND SLOVAKIA domain1 SLOVAKIA domain2 Availability of documentation for INCA or _ If possible please attach link or Dokumentaion receIved Documentation available Documentation available Documentation should be ae during Helsinki validation j similar German system Yes No documentation meeting from ZAMG from ZAMG issued in future Definition of geographical area covered by INCA or similar in Germany system Grid size in pixels 900x900 741x651 501x301 1193x951 Min longitude 3 5943 E 13 82 E 15 99231 E 8 9953784943 E Max longitude 15 71245 E 25 334 E 23 09630 E 25 9996967316 E Min latitude 46 95719 N 48 728 N 47 13585 N 45 0027313232 N Max latitude 54 73662 N 55 029 N 50 14841 N 53 000579834 N Space resolution 1km 1km 1km 1km Composite of 16 national Composite of 8 national Composite of 2 national Composite of 5 radars radars radars international radars 397 SHMU CHMI ZAMG IMWM Blacklist for precipitation stations Yes No i Map of density of precipitation stations Density of raingauge stations y gaug Yes No Instantaneous precipitation based only Output data on raingauge network time resolution Yes 15 min Yes 15 minute timelines Instantaneous precipita
140. he most reliable data to represent a ground reference A complete report on the results obtained by the Working Group on rain gauge radar and ground data integration are reported in the Chapter 4 with a complete inventory of the ground data used within the PPVG 30 W 20 W 10 W 0 10 E 20 E 30 E 40 E 50 E 60 E 70 E 0 500 1 000 km a oe MMM PPV Countries gt Rain gauges 55 N 50 N 45 N 40 N 35 N 9 zg 0 10 E 20 E 30 E 40 E Figure 6 The network of 3500 rain gauges used for H SAF precipitation products validation The rain gauge networks of PPVG is composed of approximately 3500 stations across 6 Countries Figure 6 A key characteristic of such networks is the distance between each raingauge and the closest one averaged over all the instruments considered in the network and it is a measure of the raingauge density Instruments number and density are summarized in Table 3 Country Total number of gauges Average minimum distance km Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 22 177 Product Validation Report PVR 02 Table 3 Number and density of raingauges within H SAF validation Group the number of raingauges could vary from day to day due to operational efficiency within a maximum range of 10 15 only in the Wallonia Region only in 3 river basins only covering the western part of Anat
141. he year 2009 cecccccsseccccessecceeesececeesecceeeeseceeeeeneceseeeeeeeees 131 Figure 98 Volume SCAN procedure cccssecccessceceesccceececeuncecseucecsencecsenceeseneeeseueeeseaeeeseeesseaeesseseeeseneeees 138 Figure 99 Distribution of rain gauges according their altitude above the sea level eese 143 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 11 177 Product Validation Report PVR 02 Figure 100 Radar horizon model output for Maly Javorn k left and Koj ovsk hola right radar sites Soba ainsi aati eed AA AAAA AA AE 144 Figure 101 Composite picture of minimum visible height above the surface over the whole radar network Compositing algorithm selects the minimum value from both radar SiteS cccecceeeeeeees 145 Figure 102 Distribution of rain gauges according to the minimum visible height of radar beam 145 Figure 103 Scatterplot of log R G versus station altitude shows general underestimation of precipitation DY radal seeriana EA AA E EEEREN 146 Figure 104 Scatterplot of log R G versus radar beam altitude shows increased underestimation of radar for high and close to zero radar beam Elevations c ccsesccccesseccecesececceeseceeseeceeseeneceeeeesecetseees 146 Figure 105 Relative RMSE left and Mean Error right computed independently for each rain gauge Station in radar range and corresponding trend lines extrapolated for beam
142. hting only nearest 8 stations are taken into account to reduce bull eyes effect ii Climatological scaling of radar data by means of monthly precipitation totals of raingauge to radar ratio partial elimination of the range dependence and orographical shielding iii Re scaling of radar data using the latest rain gauge observations iv Final combination of re scaled radar and interpolated rain gauge data V Elevation dependence and orographic seeding precipitation In the final precipitation field the raingauge observations are reproduced at the raingauge station locations within the limits of resolution Between the stations the weight of the radar information becomes larger the better the radar captures the precipitation climatologically Important factor affecting the final precipitation analysis is accuracy and reliability of the raingauge stations In order to eliminate the influence of raingauge stations providing evidently erroneous data the SHM is developing the blacklisting technique which temporarily excludes such stations from the analysis Currently the stations can be put into the blacklist only manually but development of the automated blacklisting is expected in near future 4 4 2 RADOLAN system RADOLAN is a routine method for the online adjustment of radar precipitation data by means of automatic surface precipitation stations ombrometers which has started on a project base at DWD in 1997 Since June 2005 areal spatial
143. i 10 1002 met 230 BELGIUM ITALY HUNGARY List of Available Products Rain rate 240 Km rain rate 120 Km velocity 120 Km MAX 240 Km VVP2 Windprofiles Hail Probability Hail Probability 24h Overview 1 3 24 Hr Rainrate accumulation CMAX PPI CAPPI 2 5 km VIL ETops Base HailProbability Is any quality YES map available Processing chain Description of instantaneous radar product used in HSAF Validation Activities Description of accumulated radar product used in HSAF Validation Clutter removal time domain Doppler filtering and static clutter map Z R a 200 b 1 6 PCAPPI 1500m Cartesian grid 600m resolution 24 h accumulation with range dependent gauge adjustment Cartesian grid 600m resolution Clutter suppression by Fuzzy Logic scheme using Clutter map Velocity Texture Z R a 200 b 1 VPR correction testing under Nationale composite CAPPI 2 km CAPPI 3 km CAPPI 5 km VMI SRI Projection Mercator Resolution 1 km Threshold No Acc periods 1 3 6 12 24h Projection Mercator Resolution 1 km Threshold No RLAN wifi filter Clutter removal attenuation correction beam blocking correction gt next Year 2012 VPR gt No Z R a 200 b 1 6 National composite CMAX Projection stereographic S60 Resolution 2 km Threshold 7dBZ No rain correction Acc periods 3 6 12 24h National CMAX Projection gauge
144. ical radars There are three C band dual polarized Doppler weather radars operated routinely by the OMSZ Hungarian Meteorological Service The location and coverage of the three Hungarian radars are shown in next figure the measurement characteristics are listed in Table 18 All three radars are calibrated periodically with an external calibrated TSG the periodicity is kept every 3 months Poganyvar Napkor he mE F p GX easy mis A red parr st r c Fonte z s dape st i E Figure 25 The location and coverage of the three meteorological Doppler radars in Hungary Product Validation Report PVR 02 Product H02 PR OBS 2 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 54 177 Year of Location Radar type Parameters installation measured 1999 Budapest Dual polarimetric Z ZDR Doppler radar 2004 Dual polarimetric Z ZDR KDP DP Doppler radar Dual polarimetric Doppler radar Table 17 Main characteristics of the Hungarian radar network Poganyvar The instruments The Hungarian radar network is composed by three Doppler radars which are measuring in the C band mainly at same frequencies The scan strategy is the same for all the radars the Budapest radar has a resolution lower than the two other radars which are newer types The parameters of the instruments and the measurement campaigns are listed in next table Budapest Napkor Poganyvar C Ba
145. icipating countries Radar sites and radars In the PPVG group we have all together 54 radars used or in the plan to be used Their distribution in the countries is Belgium 1 radar Germany 16 radars not BfG products Hungary 3 radars Italy 18 radars Slovakia 2 radars Poland 8 radars Turkey 6 radars These radars cover wide range of geographical area from the longitude 5 50562 in Wideumont Belgium to the most Eastern area with longitude 32 58 15 in Ankara Turkey and from the Northern latitude of 54 23 03 17 in Gdansk Poland to the latitude of 36 53 24 in Mugla Turkey and lat 37 462 in Catania Italy Radars are built at different elevations above the sea level In mountainous countries they are placed at elevations more than 1000m above sea level whereas in flat countries like Hungary or Belgium their height position is not exceeding 400m This information collected will be useful in the future steps of the Working Group to assess the partial or total beam shielding by mountains in the propagation way of the radar signals VVVVVV WV All radars are C band radars working at frequency in C band at 5 6 GHz This is important to know that our radar system is comparable Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 138 177 Product Validation Report PVR 02 All radars are equipped by Doppler capacity which means that ground clutters
146. igure 37 Map of SHM rain gauge stations green automatic 98 blue climatological 586 red hydrological Stations in H SAF selected test basins 37 The instruments Type of all the automatic rain gauges is tipping bucket without heating of the funnel The gauges are able to measure precipitation rates ranging from 0 1 to 200 mm h at 10 min operational accumulation Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Paaa na we Product H02 PR OBS 2 Date 30 09 2011 Page 68 177 The EUMETSAT E ert a Product Validation Report PVR 02 H SAF p interval Shorter accumulation interval of 1 min is also possible which makes the instruments suitable for case studies in the H SAF project The data processing The rain gauge data are not used at SHM directly for the H SAF precipitation validation but they are utilized as the input to the INCA precipitation analysis system which is supposed to become a new validation tool Prior the INCA analysis the rain gauge data are interpolated onto the regular 1x1 km grid using the inverse distance squared IDS interpolation method Only the 8 nearest rain gauge stations are taken into account in the interpolation in order to reduce occurrence of precipitation bull eyes artifact SHM performs the offline automatic and manual quality check of the rain gauge data In frame of the INCA system a quality control technique called blacklisting has been developed which avoids the da
147. il of field studied below the original grid of field for step 2 From the field the white circles mean the data removed from the map The black squares mean the position of perfect measurement The techniques of up down scaling reproduce the field only from the perfect measurements 00 174 Figure 124 randomly distribution of perfect measurement to remap the field on a regular grid 175 Figure 125 STD vs RMSE for interpolations by step 2 cc cc eescccccssseccccesecceceeseceeeeeseeeseeneceeseeaecetseens 176 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 12 177 The EUMETSAT Natwork e Yetwork of Sotellite Application Facilities Product Validation Report PVR 02 Support to Operationa ydrolo bene Product H02 PR OBS 2 Acronyms AMSU Advanced Microwave Sounding Unit on NOAA and MetOp AMSU A Advanced Microwave Sounding Unit A on NOAA and MetOp AMSU B Advanced Microwave Sounding Unit B on NOAA up to 17 ATDD Algorithms Theoretical Definition Document AU Anadolu University in Turkey BfG Bundesanstalt f r Gewasserkunde in Germany CAF Central Application Facility of EUMETSAT CDOP Continuous Development Operations Phase CESBIO Centre d Etudes Spatiales de la BIOsphere of CNRS in France CM SAF SAF on Climate Monitoring CNMCA Centro Nazionale di Meteorologia e Climatologia Aeronautica in Italy CNR Consiglio Nazionale delle Ricerche of Italy CNRS Centr
148. image of August 16th 2010 at 2 07 left compared with upscaled radar at 2 05 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 We can see that in the noon case the matching is rather good and the images from two satellites are consistent in particular in detecting the precipitation cells in the North of the validation area There is just a slight underestimation In the morning case it is quite good Scores evaluation The score evaluation results Table 21 are quite good if compared to long period statistics especially for what concerns correlation POD and FAR A slight underestimation is reported over all the case study consistently with the long period statistics Sample 32 Mean error 0 43 Standard deviation 1 37 Mean absolute error 0 98 Multiplicative bias 0 72 Correlation coefficient 0 49 Root mean square error 1 45 URD RMSE 1 56 POD 0 62 FAR 0 22 CSI 0 52 Table 21 Scores obtained with the comparison with radar data in mm h Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 80 177 Product Validation Report PVR 02 The time evolution of the fraction area with rain the average rain rate over this area the Equitable Threat Score ETS and the root mean square error RMSE is reported in the following figure Fraction area gt 0 25 mm h RMSE Figure 47 Time evolution of fraction area with rain average rain rate over
149. in rate lower than 0 25 mm h is considered no precipitation The main steps to evaluate the statistical scores are e all the institutes up scale the national radar and rain gauge data on the satellite native grid using the up scaling techniques before described e all the institutes compare HO2 with the radar precipitation intensity and the rain gauge cumulated precipitation e all the institutes evaluate the monthly and seasonal continuous scores below reported and contingency tables for the precipitation classes producing numerical files called CS and MC files e all the institutes evaluate PDF producing numerical files called DIST files and plots e the precipitation product validation leader collects all the validation files MC CS and DIST files verifies the consistency of the results and evaluates the monthly and seasonal common statistical results The EUMETSAT Doc No SAF HSAF PVR 02 1 1 Sotelte e Product Validation Report PVR 02 Foci Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 28 177 GERMAN Y Figure 10 Main steps of the validation procedure in the PPVG Statistical scores The statistical scores evaluated in PPVG for continuous statistics are Mean Error ME N ME sat true Range to ce Perfect score 0 k 1 Mean Absolute Error MAE N MAE N X sat true Range 0 to ee Perfect score 0 k 1 Standard Deviation
150. ing bucket type instruments Minimum detected quantity that can be measured by those rain gauges is 0 1 mm h which means that each tilt of rain gauge bucket adds 0 1mm to the total sum of the measured precipitation During very heavy precipitation events MetOne rain gauges tend to underestimate real precipitation by factor of 10 Maximum measured rainrate mmh by MetOne instruments in Poland was recorded in 5 06 2007 at ATSO Koscielisko Kiry at the foot of Tatra Mountains The recorded values reached 65 mm h Operational cumulation interval min of ATS network rain gauges is set for 10 minutes and can be adjusted according to given needs There is possibility to have very short cumulation intervals for case studies theoretically 1 minute but not on every given precipitation post It depends on local DCS settings The data processing As stated above the data quality control can be achieved by comparison on two rainfall datasets collected by two independent rain gauges at the same ATS post It is done operationally during summertime There is no such possibility during the winter because of lack of non heated rain gauge wie gt roduct Validation Report _PVR 02 COC NO SAF HSAF PVR 02 1 1 Satelite Application roauct VallQation Report ze HSAF p Issue Revision Index 1 1 hyoy ond waler Product H02 PR OBS 2 Date 30 09 2011 Page 67 177 dataset In case that one pair of rain gauges at the same ATS post provide two different
151. ion Group to set up a Working Group for creating geographical maps of error distribution The main goals of this working group are e to investigate the opportunity to create geographical maps of error distribution for H SAF validation e to define if necessary the methodology for spatial representation of precipitation products errors e to produce a well referenced documentation on the methodology defined e to produce two short reports on the results obtained first by 31 of March 2011 and second by 30 of November 2011 e to develop if necessary the code to be used in the PPVG for a correct generation of the defined geographical maps of error distribution Activities First step to define the methodology e selection of the appropriate methodology for spatial distribution of precipitation products errors taking into consideration spatial and temporal characteristics of each product e first study performed for selected Polish test catchments as well as Polish territory Start Time End time December 2010 March 2011 First Report 31 of March 2011 Second step To define the precipitation products errors maps for country members of PPVG e collection of collocated ground data and satellite products for selected period possibly through 6300 e creation of the error maps for territory of PPVG country members for selected period e analysis of the achieved results emphasizing the errors distribution obtained for test cat
152. ion distribution of the RADOLAN precipitation rain gauge point areal original radar product measurements measurements z Doc No SAF HSAF PVR 02 1 1 The EUMET siwer cttte Apoaren Product Validation Report PVR 02 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 45 177 y MANNHEIM WST JIANNHEIM WST y ld aA R TaT EKHEMBAD yN a RKHEIM BAD pn a a N C an UTERN ca C a k S000 0 00 a i HEIDELRERG 0 00 P HEDDELAERG R 40 00 a a 0 00 ve I HEIDELBERG a l o a san 5 10 00 A 5 00 p G 7 Tha o je r A 1 00 afr 0 i 1 00 B a WAGHAEUSELKIRRIACH E a50 WAGHAEUSEL KIRRIACH i WAGHAEUSEL KIRRIACH g 080 SBEN m ace oo 010 amp 0 10 n f j d Pi a j j j j j j PERGZABERN BAD es d SU SARLSRUHE WSI el amp ARLSRUHE WST ae m KARLSRUHE WSD J Figure 16 Procedure of the RADOLAN online adjustment hourly precipitation amount on 7 August 2004 13 50 UTC In order to collect more detailed information about both types of systems a questionnaire was elaborated and completed by Slovakia Poland and Germany The questionnaire provided details such as geographical coverage see Fig 17 input data inventory or availability of different instantaneous and cumulated precipitation products The final version of the questionnaire is shown in the next table and is also available as annex 5 r
153. ion product cal val activities occurs in the dimension disagreement between the product space area and the ground observation space point To be able to compare both cases either area to point product to site or point to area site to product procedure has to be defined However the first alternative seems easier The basic assumption in such an approach is that the product value is homogenous within the product footprint Next figure presents satellite foot print FOV centers of the HO1 and H02 products an elliptical footprint for the corresponding center area within the yellow dots and Awos ground observation sites The comparison statistic can be performed by considering just the sites in the footprint area Although this approach is reasonable on the average but it is less useful in spatial precipitation variability representation The comparison is not possible when no site is available within the footprint area Awos sites T FOV center g ii f 4 r i A 4 Pia i chatty a P t lt i ri 4 Figure 41 H01 and H02 products footprint centers with a sample footprint area as well as the Awos ground observation sites Alternatively the point to area approach is more appealing for the realistic comparison of the precipitation product and the ground observation This approach is simply based on the determination of the reference precipitation field underneath the product f
154. ion rate processing chain The first step is to resample AMSU A brightness temperature TB to AMSU B MHS grid using bilinear interpolation Then AMSU A and AMUS B MHS radiometers are corrected for limb and surface effects to report the viewing geometry changing across the image to vertical viewing This is obtained by applying procedures based on specific Neural Networks one Net for each channel The instantaneous rain field is finally retrieved by using a Neural Network on the corrected TBs In the initial product release the Neural Network had been trained by selected radars of the NEXRAD network In the current release the Neural Network is trained by a Cloud Radiation Database CRD built by applying a Radiative Transfer Model RTM to simulated cloud systems derived by a Cloud Resolving Model CRM 2 3 Main operational characteristics The operational characteristics of PR OBS 2 are discussed in PUM 02 Here are the main highlights The horizontal resolution Ax descends from the instrument Instantaneous Field of View FOV AMSU A and AMSU B MHS have constant resolution with frequency different for AMSU A 48 km at nadir and AMSU B MHS 16 km at nadir degrading across scan 80 x 150 and 27 x 50 km respectively at the very edge of scan Lower resolution AMSU A data are resampled over the AMSU B MHS grid by means of bilinear interpolation As a whole a representative value for the final product could be 40 km Sampling is made a
155. k will include twenty five C band radars including seven polarimetric systems and five transportable dual polarized X band radars next figure The Italian Department of Civil Protection is developing the radar network in Southern Italy and thanks also to the fruitful collaborations with Regional Authorities ENAV and AMI integrated all the existing radars in one national network with a clear advantage for both severe weather monitoring and civil protection purposes National Weather Radar Coverage 125km Figure 28 Italian radar network coverage The existing sixteen C band weather radars that belong to Regional Authorities ENAV and AMI are listed below Bric della Croce Owner Regione Piemonte Polarization on going upgrade to polarimetry Settepani Owner Regione Piemonte and Regione Liguria Polarization dual San Pietro Capofiume and Gattatico Owner Regione Emilia Romagna Polarization dual Monte Macaion Owner Regione Trentino Alto Adige and Provincia autonoma Trento Polarization single Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 jyacogyons wale Product HO2 PR OBS 2 Date 30 09 2011 Page 59 177 The EUMETSAT RS i PRO reat e Product Validation Report PVR 02 2 usa Teolo and Loncon Owner Regione Veneto Polarization single Monte Midia Owner Regione Abruzzo Polarization single Monte Rasu Owner Regione Sardegna Polarization single Fossolon di Grado Own
156. key Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 120 177 The EUMETSAT work icc acil on amp H SAF Product Validation Report PVR 02 Nen Satellite Appl Fe itie Support to Operot erarono Hydrology and Water Management 6 2 4 The autumn period Ce ee autumn 2010 Version 2 2 radar__ radar__ radar__ radar__ radar__ gauge gauge gauge gauge _ gauge gauge _ 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 1 10mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h 210mm h Table 43 The main statistical scores evaluated by PPVG for H02 during the autumn period The rain rates lower than 0 25 mm h have been considered as no rain A general precipitation underestimation by HO2 is reported in table 43 using both rain gauge and radar data for all the precipitation classes The statistical scores obtained during this season with both radar data RMSE Cl1 153 Cl2 77 Cl3 97 and rain gauge RMSE Cl1 181 Cl2 100 Cl3 85 are the best ones of all the year The EUMET ESAT Product Validation R PVR 0 Doc No SAF HSAF PVR 02 1 1 ication a H SAF roquc alldation Report itie Nen Satellite Appl Fe Issue Revision Index 1 1 a Product H02 PR OBS 2 Date 30 09 2011 Page 121 177 6 2 5 The annual average DIC 09 NOV 10 lt 1mm h lt 1mm h lt 1mm h lt 1mm h
157. king Groups are here reported 3 2 Validation objects and problems The products validation activity has to serve multiple purposes e to provide input to the product developers for improving calibration for better quality of baseline products and for guidance in the development of more advanced products e to characterise the product error structure in order to enable the Hydrological validation programme to appropriately use the data e to provide information on product error to accompany the product distribution in an open environment after the initial phase of distribution limited to the so called beta users Validation is obviously a hard work in the case of precipitation both because the sensing principle from space is very much indirect and because of the natural space time variability of the precipitation field sharing certain aspects with fractal fields that places severe sampling problems It is known that an absolute ground reference does not exist In the H saf project the validation is based on comparisons of satellite products with ground data radar rain gauge and radar integrated with rain gauge During the Development phase some main problems have been pointed out First of all the importance to characterize the error associated to the ground data used by PPVG Secondly to develop software for all steps of the Validation Procedure a software available to all the members of the PPVG Three months ago the rada
158. larimetric radars In order to evaluate the benefits of upgrading the new radar installations to full polarimetric radars and for considering the benefit of existing polarimetric radars many studies have been carried on by Research Centres and Regional Authorities belonging to the network e g Silvestro et al 2008 As an example in next figure is shown the cumulated rainfall estimates versus gage measurements obtained for the event observed on 06 01 2006 by the dualpolarized C Band radar of Mt Settepani The figure shows the comparison between a multi parameter algorithm that uses polarimetric data Silvesto et al 2008 and a simple ZR relationship Marshall Palmer bis o ZR200 MultiParametric 200 E S150 J T 4 t L D f D w 100 p l r 5 O r am ae i ra e n E K p d O 0 s J z O 5 i Doo Bo we om j a 100 150 200 250 Figure 35 Cumulated radar rainfall estimates versus gage measurements for the event observed on 06 01 2006 by the dualpolarized radar located in Settepani Liguria Italy 4 10 Ground data in Poland IMWM 4 10 1 Rain gauge The network The maximum number of rain gauges in the Polish ATS Automatic Telemetric Station national network is 950 Each ATS post is equipped with two independent rain gauges of the same sort One of them is heated during the winter period and the other one is not Therefore precipitation information i
159. lassification algorithm starting from the radar variables observed on 09 14 08 at 0500 U T C by the polarimetric radar operated by Piemonte and Liguria regions sssssssessessssssessessrsresessesrrsresessesrreresessessesreseosrssesreseoseosreresesseoresreseoseoseereseoseoseereseosesseereneo 63 Figure 34 Measured upper panel and VPR corrected lower panel PPI of reflectivity observed on 03 25 07 at 0930 U T C by the polarimetric radar located in Gattatico Emilia Romagna Italy 64 Figure 35 Cumulated radar rainfall estimates versus gage measurements for the event observed on 06 01 2006 by the dualpolarized radar located in Settepani Liguria Italy 65 Figure 36 ATS national network in Poland ssccccssscccssscccsssecessseceseecessececssecueusececusccueusscneusseneussenens 66 Figure 37 Map of SHMU rain gauge stations green automatic 98 blue climatological 586 red hydrological stations in H SAF selected test basins 37 ccccccccsssseccceeeeesecceeeeeeeccceeeeeeeeceeeseeeeceeeseuenes 67 Figure 38 Map of SHMU radar network the rings represent maximum operational range 240 km for radar at Maly Javornik left 200 km for radar at Kojsovska hola right cccccceeecsceeseeseeeeeeseeeeeeeees 68 Figure 39 Map of relative RMSE left and Mean Error right over the SHM radar composite 70 Figure 40 Automated Weather Observation System AWOS station distribution in western part
160. lay down the Validation plan that was finalised as first draft early as 30 September 2006 After the first Workshop other ones followed at least one per year to exchange experiences problem solutions and to discuss possible improvement of the validation methodologies Often the Precipitation Product Validation workshop are joined with the Hydrological validation group The results of the Product Validation Programme are reported in this Product Validation Report PVR and are published in the validation section of the H SAF web page A new structure and visualization of the validation section of H SAF web page is in progress to take into account the user needs This validation web section is continuously updated with the last validation results and studies coming from the Precipitation Product Validation Group PPVG In the last Validation Workshop hosted by Slovensk Hydrometeorologick stav in Bratislava 20 22 October 2010 it has been decided to introduce several Working Groups to solve specific items of validation procedure and to develop software used by all members of the validation cluster The coordinators and the participants of the working groups are members of the PPVG or external experts Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 20 177 Product Validation Report PVR 02 of the institutes involved in the validation activities The first results obtained by the Wor
161. le 43 The main statistical scores evaluated by PPVG for HO2 during the autumn period The rain rates lower than 0 25 mm h have been considered AS NO rain eceeeeccceeccceecececccececececsecccecenseceauecs 120 Table 44 The main statistical scores evaluated by PPVG for H02 during one year of data 1 December 2009 30 November 2010 The rain rates lower than 0 25 mm h have been considered as no rain 121 Table 45 The averages POD FAR and CSI deduced comparing HO2 with radar data neeese 122 Table 46 The contingency table for the three precipitation classes defined in table 7 evaluated by TAA tI MINTZ with radar data csc cpap tect ionann eiO Ee EEEREN EEEE EEEE 122 Table 47 The averages POD FAR and CSI deduced comparing HO2 with rain gauge data 066 123 Table 48 The contingency table for the three precipitation classes defined in table 7 evaluated by compare OZ Wien hall 2 u e a eai E 123 Table 49 User requirement and compliance analysis for product HO2 sssessssssserreserrrerrrserrrerrrrerrresns 124 Table 53 Summary of the raingauge characteristics seesssssesersnssrrssrrrssrresrrrssrrrssrrrsrrrssrrrertresrererrresns 130 Table 54 Number and density of raingauges within H SAF validation Group cccccceeccceeseeeeenseeeenees 131 Table 55 Data pre processing Strategies ccrnren iriran rine En ETA EE EEEE E 132 Table 56 Matching strategies for comparison with HO1 and HO2 sseesssssesersesersserrrssrrrsrrrserrresrererr
162. lidation Report PVR 02 Figure 27 Distribution of the raingauge stations of the Italian network collected by DPC The instruments The following information should be provided in this section All the available raingauge are of tipping bucket type Most of the raingauge have a minimum detected quantity of 0 2 mm others have 0 1 mm The maximum rainrate that can be measured by the gauges ranges between 300 and 500 mm over one minute depending on the manufacturer The rainrate is measured over different cumulation intervals by the different local administrations managing the network but the data disseminated are all integrated over 60 minutes At the moment the National network made available by DPC provides only hourly data Shorter cumulation times could be available for case studies after specific agreements with local management authorities Only a small subset about 300 stations of gauges have heated funnel especially in alpine regions such as Valle d Aosta and Piedmont and this is a clear source of errors in both summer due to hailfall and in autumn winter due to snowfall The data processing No quality control is performed on the data right now In this Project the point like gauges data are interpolated by using the Barnes method Barnes 1964 Koch et al 1983 widely used to interpolate station data It works by defining a regular output grid 5x5 km in our case and a radius of influence of each
163. lidation Report PVR 02 the estimation of their errors cannot be homogenized The first step in PPVG Radar WG will be to define a quality index on the base of the study performed by the Slovakian team Annex 4 and the scheme published by J Szturc et all 2008 The main difficulty consists on the definition of a quality index computable for every radar networks of PPVG The evaluation of this quality index will allow to evaluate the rain gauge error in the same way and to select the more reliable radar data in the PPVG In this chapter the first example of precipitation fields integration has been provided Section 4 4 3 INCA and RADOLAN products The INCA system a tool for the precipitation products validation is available in Slovakia and Poland in both countries being run in pre operational mode In Germany similar precipitation analysis system called RADOLAN is being run operationally This tool is already used for validation of the H SAF precipitation products in Germany The study performed in the PPVG INCA WG showed that the accuracy and reliability of the raingauge stations significantly affect final precipitation analysis of the INCA or INCA like systems In order to solve this problem an automated blacklisting technique is going to be developed at SHMU currently blacklisting is used in manual mode The next step will be to develop the software for up scaling the INCA precipitation field into the satellite product grid The grids
164. lite IFOVs Two basic ideas are pursued pixel by pixel matching or ground measure averaging inside satellite IFOV The second approach seems to be more convenient especially when the large IFOV of HO1 and HO2 are concerned Probably it is mandatory for H02 also take into account that the size of the IFOV changes across the track and could become very large The first approach e g nearest neighbour can be more effective for H03 and HO5 products Type of interpolation Quality control Y N Barnes over 5x5 km grid Co kriging Barnes over 5x5 km grid Poland No Y except cold months Turkey No Table 52 Data pre processing strategies Spatial matching Temporal matching Spatial matching Temporal matching Germany matching gauges are each overpass is matching gauges are each overpass is searched on a radius compared to the searched on a radius compared to the of 2 5 km from the next hourly rain of 2 5 km from the next hourly rain IFOV centre amount IFOV centre amount Italy mean gauges value each overpass is Gaussian weighted each overpass is over 15x15 km area compared to the mean gauges value compared to the Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Date 30 09 2011 Page 133 177 centred on satellite next hourly rain centred on satellite next hourly rain IFOV amount IFOV amount mean gauges value each overpass is mean gauges value each overpass is over the IFOV area compared to the over the
165. ly appropriated for the validation of cumulated products 1 hour and higher while for the validation of instantaneous estimates the use of hourly cumulated ground measurements could introduce a large error Moreover the revisiting time 3 4 hours of HO2 makes impossible or not reasonable to validate the product for 1 24 hours cumulated interval The first object of PPVG Rain Gauge WG in the next future it will be to quantitatively estimate the errors introduced in the validation procedure comparing the instantaneous satellite precipitation estimation with the rain gauge precipitation cumulated on different intervals the Polish and Turkish data will be used for this purpose The rain gauge inventory has also pointed out that different approaches for the estimates matching are considered in the PPVG The second steps in the next future will be to define the rain gauge spatial interpolation technique and to develop the related software The radar data in the PPVG is composed by 54 C band radars across the 7 countries Belgium Germany Hungary Italy Slovakia Poland Turkey The rain gauge network responsible declared that the systems are kept in a relatively good status The rain gauge inventory pointed out that different correction factors are applied This means that the corresponding rainfall estimates are diverse and Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 76 177 Product Va
166. ly when the large IFOV of HO1 and HO2 are concerned Probably it is mandatory for H02 also take into account that the size of the IFOV changes across the track and could become very large The first approach e g nearest neighbour can be more effective for HO3 and HO5 products Barnes over 5x5 km grid Barnes over 5xSkmerid NO Poland No Y except cold months Turkey No Table 10 Data pre processing strategies Spatial matching Temporal matching Spatial matching Temporal matching Germany matching gauges are each overpass is matching gauges are each overpass is searched on a radius compared to the searched on aradius compared to the of 2 5 km from the next hourly rain of 2 5 km from the next hourly rain IFOV centre amount IFOV centre amount Italy mean gauges value each overpass is Gaussian weighted each overpass is over 15x15 km area compared to the mean gauges value compared to the centred on satellite next hourly rain centred on satellite next hourly rain Poland Product Validation Report PVR 02 mean gauges value over the IFOV area rectangular weighted mean of the gauge values estimated at the 3kmX3km grid structure within satellite IFOV by using semi variogram Product H02 PR OBS 2 each overpass is compared to the next 10 minutes rain amount each overpass 1s compared to 5 minute averaged rain for Temporal matching Doc No SAF HSAF PVR 02 1 1 Issue Revisio
167. m broadening interception of melting snow contamination by dry or melting hail hot spots variability of the Raindrop Size Distribution RSD and its impact on the adopted inversion techniques oo et eS Some of them are typically handled by resorting to standard procedures some others requires the availability of dual polarized observations Generally speaking there are not correction methodologies applicable worldwide The knowledge of the radar system and the environmental conditions makes the difference when approaching such problems During the Precipitation Product and Hydrological Validation workshop held in Bratislava the 20 22 of October 2010 the Precipitation Product Validation Group PPVG has decided to set up a working group on the radar data use in the validation procedures This WG is not aimed at promoting the acceptance of shared data processing chain What really matter for us is the characterization of the error sources through the construction of appropriate quality maps As requested by the hydro meteorological community many operational institutions already provide such information others are currently working on this task The main aims of this WG are to describe the characteristics and generated products of PPVG radar networks to produce a referenced documentation on minimal requirements for certifying the radar products quality radar rainfall products testing and the procedure for satellite prod
168. ment of a common procedure for validation of PR ASS 1 in the native COSMO model grid A Rinollo RMI Belgium F Porcu Universita di Ferrara Italy L Torrisi CNMCA Italy Validation technique depends on data resolution The task of the present group is to develop a common validation procedure for the PR ASS 1 product characterized by the COSMO model native grid which is built up in a rotated coordinate system Depending on the resolution of the ground data we decided to suggest two different approaches in case of ground data with a spatial sampling similar to the one of COSMO that is the typical case of raingauge networks the nearest neighbor approach is suggested In this case no upscaling is needed in case of a resolution of the ground data much finer than the one of COSMO that is the case of many radar products then the upscaling to the native COSMO grid is recommended For this case we are currently working on a common upscaling procedure Methodology The main issue in this task is the fact that PR ASS 1 is based on the rotated coordinate system of the source model COSMO while the ground observations are normally based on geographical coordinates For this reason in case upscaling is needed a regular portion i e a fixed number of rows and columns is extracted from the COSMO grid Then all the coordinates of the ground data are converted in the rotated system and associated to the grid cell in which they fall in
169. mi variogram gauges value over centred on satellite IFOV Table 54 Matching strategies for comparison with H03 and H05 rain amounts in the same number of hours are compared 24 hours rain amounts in the same number of hours are compared 3 and 24 hours rain amounts in the same number of hours are compared 3 6 12 and 24 hours rain amounts in the same number of hours are compared 3 6 12 and 24 hours rain amounts in the same number of hours are compared 3 6 12 and 24 hours rain amounts in the same number of hours are compared 3 6 12 and 24 hours Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 134 177 Product Validation Report PVR 02 Conclusions After this inventory some conclusion can be drawn First it seems the raingauge networks used in this validation activities are surely appropriated for the validation of cumulated products 1 hour and higher while for instantaneous estimates the use of hourly cumulated ground measurements surely introduces intrinsic errors in the matching scores that can be estimated as very large The validation of instantaneous estimates should be carried on only when gauges cumulation interval is 10 to 15 minutes as in Poland Values cumulated over shorter intervals 5 or even one minute as it is done in Turkey are affected by large relative errors in cases of low moderate rainrates Different approache
170. mospheric fields Among the input data sources belong NWP model outputs in general P T H clouds Surface station observations T precipitation Radar measurements reflectivity currently 2 d 3 d in development Satellite data CLM Cloud type in development for use in precipitation analysis Elevation data high resolution DTM indication of flat and mountainous terrain slopes ridges peaks The INCA system provides High resolution analyses interest of validation WG 3 Nowcasts Improved forecasts of the following variables Temperature 3 d field Humidity 3 d Wind 3 d Precipitation 2 d interest of validation WG 3 Cloudiness 2 d Global radiation 2 d The INCA precipitation analysis is a combination of station data interpolation including elevation effects of the precipit and radar data It is designed to combine the strengths of both observation types the accuracy point measurements and the spatial structure of the radar field The radar can detect ating cells that do not hit a station Station interpolation can provide a precipitation analysis in areas not accessible to the radar beam Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 44 177 Product Validation Report PVR 02 The precipitation analysis consists of the following steps i Interpolation of station data into regular INCA grid 1x1 km based on distance weig
171. n Figure 17 Meteorological radar in Belgium The instruments These are Doppler C band single polarization radars with beam width of 1 and a radial resolution of 250 m Data are available at 0 6 0 66 and 1 km horizontal resolution for the Wideumont Zaventem and Avesnois radars respectively In this report only the Wideumont radar has been used The data of this radar are controlled in three steps Data processing First a long term verification is performed as the mean ratio between 1 month radar and gauge accumulation for all gauge stations at less than 120 km from the radar The second method consists in fitting a second order polynomial to the mean 24 h 8 to 8 h local time radar gauge ratio in dB and the range only the stations within 120 km and where both radar and gauge values exceed 1 mm are taken into account The third method is the same as the second but is performed on line using the 90 telemetric stations of the SETHY Ministry of the Walloon Region Corrected 24 h images are then Nemo ol Product Validation Report PVR 02 e Satellite Apg Doc No SAF HSAF PVR 02 1 1 picaran H SAF Issue Revision Index 1 1 yaaa ara Waler Product H02 PR OBS 2 Date 30 09 2011 Page 48 177 calculated New methods for the merging of radar and raingauge data have been recently evaluated Goudenhoofdt and Delobbe 2009 In this report only instantaneous radar images are used 4 6 Ground data in Bulgaria NIMH 4 6
172. n HSAF Product Validation Report PVR O of the countries It is not the aim of the PPVG to improve the radar data used however it is specifically expected from the current activities to characterize radar data and error sources of the ground data coming from the radar networks of the PPVG Main error sources of radar rainfall estimations are listed in the Radar Working Group description document Annex 3 1 system calibration 2 contamination by non meteorological echoes i e ground clutter sea clutter clear air echoes birds insects W LAN interferences partial or total beam shielding rain path attenuation wet radome attenuation range dependent errors beam broadening interception of melting snow contamination by dry or melting hail hot spots variability of the Raindrop Size Distribution RSD and its impact on the adopted inversion techniques a a ee Moreover several studies have been on radar quality assessments like S alek M Cheze J L Handwerker J Delobbe L Uijlenhoet R 2004 Radar techniques for identifying precipitation type and estimating quantity of precipitation COST Action 717 Working Group 1 A review Luxembourg Germany or Holleman l D Michelson G Galli U Germann and M Peura Quality information for radars and radar data Technical rapport 2005 EUMETNET OPERA OPERA_2005_19 77p PPV Countries Weather radar units O Horizontal beam extent of 100 km O Horizontal b
173. n can be evaluated in the 30 for the step 2 that means an ideal condition were the rain gauge are disposed long a regular grid with a distance that the half of phenomenon length The structure of precipitation depends from precipitation typology time and spatial resolution therefore phenomenon length cannot be considered absolute An irregular distribution of perfect measurements has been considered also For each step the number of perfect measurement has been redistribuited randomly to simulate the raingauge network In the figure 124 below the white circles mean the position of perfect measurement points In the figure 124 below the white circle mean the position of perfect measurement points in the best case step 2 The results shown again the Barnes tecnique the best choice to reproduce the field Sampled irregular data 10 0 w wa a 40 50 60 70 48 90 4100 110 Figure 121 randomly distribution of perfect measurement to remap the field on a regular grid Assuming the best condition step 2 for the regular grid an evaluation of spread of RMSE respect the structure of precipitation field has been done In the figure 125 below the Barnes and Kriging Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 176 177 Product Validation Report PVR 02 tecniques show a low dependence from the standard deviation of field ie the level of inomogeneity of field The performance of
174. n Index 1 1 Date 30 09 2011 Page 36 177 mean gauges value over the IFOV area rectangular weighted mean of the gauge values estimated at the 3kmX3km grid structure within satellite IFOV by using semi variogram each overpass is compared to the next 10 minutes rain amount each overpass is compared to 5 minute averaged rain for Temporal matching Table 11 Matching strategies for comparison with H0O1 and H02 Belgium and Bulgaria use raingauges only for cumulated precipitation validation 4 2 4 Some conclusions After this inventory some conclusion can be drawn First it seems the raingauge networks used in this validation activities are surely appropriated for the validation of cumulated products 1 hour and higher while for instantaneous estimates the use of hourly cumulated ground measurements surely introduces intrinsic errors in the matching scores that can be estimated as very large The validation of instantaneous estimates should be carried on only when gauges cumulation interval is 10 to 15 minutes as in Poland Values cumulated over shorter intervals 5 or even one minute as it is done in Turkey are affected by large relative errors in cases of low moderate rainrates Different approaches for the estimates matching are considered and probably could be a good idea to harmonize them among partners The ground data up scaling procedure indicated in Section 3 5 has been already developed by E Roulin
175. n Turkey This means that the corresponding rainfall estimates are diverse and the estimation of their errors cannot be homogenized However each county can provide useful information of the error structure of its rainfall products based on its own resources e g if they have already defined Quality Indicators or estimations of errors based on studies of comparison of radar and rain gauge data in the country itself The study performed by the Slovakian team Annex 4 and the scheme published by J Szturc on the quality index evaluation are under consideration by the Radar WG In the future possible separation of reliable and quasi reliable radar fields would be possible Separation would be based on radar site geographical areas event type radar products Selected cases will be suitable enough to be used as a reference for the H SAF products validation A study on evaluation of radar measurements quality indicator with regards to terrain visibility has been conducted by the Slovakian team see Annex 4 Satellite product testing will be carried out in areas with higher reliability Statistical results will be evaluated and compared to previous data As such the accuracy of statistical results of PPVG with radar data as ground reference will be able to be established BELGIUM ITALY HUNGARY List of Rain rate 240 Km CMAX Available rain rate 120 Km velocity PPI Products 120 Km CAPPI 2 5 km MAX 240 Km VIL VVP2 Windprofiles E
176. n visibility by radar network using 90m digital terrain model e statistical comparison of radar data against independent rain gauge data measurements e derivation of dependence regression equation describing the errors distribution in radar range with regard to terrain visibility based on rain gauge and radar data statistical evaluation e computation of error distribution maps using regression equation and terrain visibility 24 hour cumulated precipitation measurements from 68 automatic precipitation stations from the period 1 May 2010 30 September 2010 were coupled with radar based data Distribution of gauges according their elevation above the sea level is shown in next figure m Rain gauge altitude m 8 8 R D 8 8 Sa a CAKLOV am SKALICA ml KUKOVA pm OKRUHLE m ZEMBEROVCE m CASTA pma MALCOV pm REMETSKE_HAMRE pmm MALACKY pum gt gt Se Sw STARA_BYSTRICA a kh N ff DQ ow S N o8 86 8 8 OLKA PAPIN a BANSKE p ZBOJ BZOVIK pmm HABURA pm SKYCOV p MYJAVA JASENIE pam RUDNANY pamm CIGELKA Mm SKALITE po NALEPKOVO RUNINA SMOLNIK m MAKOY ELE AD Mu ZLIECHOV LIPOVCE mmm POLOMKA VRATNA p KLAK p KRALIKY ni HENCLOYVA mm RE _BANE Pamm BEREC mammum NOVOT Emm ORAVSKA_LESNA Mmm SUCHA_HORA a HUTY STARA_LESNA pamm TORYSKY Pamm MUTNE mumm VYSNA_BOCA e ea TE PUKANEC pman RADOSINA pmm MOTESICE mum SMOLENICE pma BYSTRICANY mma KOLBASOV pmm
177. nd 5625MHz C Band 5610MHz C Band 5610MHz Polarization ingl ingl ingl a scan freq 15 min scan freq 15 min scan freq 15 min Elevaions deg 00 5 Elevaions deg 00 5 Elevaions deg 00 5 pio 1 1 1 8 2 7 38 5 1 1 1 1 8 2 7 3 8 5 1 1 1 1 8 2 7 3 8 51 istance 6 6 8 5 6 6 8 5 6 6 8 5 Range 240 Km Range 240 Km Range 240 Km Frequency band Doppler Yes No Scan Strategy elevations maximum range range resolution Resolution 500m Resolution 250m Resolution 250m Table 18 Characteristics of the three radar instruments in Hungary The data processing Radar measurements are influenced by many error sources that should be minimized as much as possible As such in case of the Hungarian radar data many correction methods are applied or planned to be applied int he near future to filter out false radar reflectivity measurements Clutter removal and WLAN filter is already implemented int he processing chain of all three radar data anda filter to disregard signals below 7dBz is also applied because in general these data is not coming from real rain drops but false targets According to experiences beam blockage can result in serious underestimation of precipitation amounts e g behind the B rzs ny mountains at the north of Budapest So the bleam blockage Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 55 177 Product Validation Report PVR 02 correc
178. near the High Tatras mountain in the northern part of Slovakia where only low precipitation rates were observed by radars next figure part a The resulting INCA analysis is shown in next figure part c The corresponding PR OBS 1 field next figure part d shows overestimation even when compared with the rain gauge adjusted field of the INCA analysis Original radars FINCA Raingauges 201008151500 Aki 201008151500 te lt 4g i s a aA Co moh INCA Radars raingauges HO1 201008151500 ga f i 201008151500 d Rain intensity mm h 30 j Figure 107 Precipitation intensity field from 15 August 2010 15 00 UTC obtained by a radars b interpolated raingauge data c INCA analysis and d PR OBS 1 product Visual comparison of the precipitation fields In this section two case studies from 15 August 2010 focused on performance of the INCA analyses are presented 15 August 2010 06 00 UTC This case illustrates potential of the INCA system to correct errors in radar precipitation measurements due to radar beam attenuation in heavy precipitation As can be seen in Fig 3a the radar measured precipitation near centre of the circled area was relatively weak However as Fig 3c suggests the precipitation was probably underestimated by radars because an intense convective cell occurred directly in path of the radar beam dashed line in Figure 108 part c The raingauge network Figure 108 part b captured the intense
179. nfra Red Imager on Meteosat from 8 onwards Slovak Hydro Meteorological Institute Special Sensor Microwave Imager on DMSP up to F 15 Special Sensor Microwave Imager Sounder on DMSP starting with S 16 Suomen ymp rist keskus Finnish Environment Institute Equivalent Blackbody Temperature used for IR Teknillinen korkeakoulu Helsinki University of Technology TRMM Microwave Imager on TRMM Tropical Rainfall Measuring Mission UKMO Turkish State Meteorological Service Technische Universitat Wien in Austria Unified Meteorological Archive and Retrieval Facility University of Ferrara in Italy User Requirements Document Universal Coordinated Time Visible Zentralanstalt fur Meteorologie und Geodynamik of Austria Doc No SAF HSAF PVR 02 1 1 The EUMETSAT is Product Validation Report PVR 02 Yerwo Sotellite Applicc ac ie Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 14 177 1 The EUMETSAT Satellite Application Facilities and H SAF The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management H SAF is part of the distributed application ground segment of the European Organisation for the Exploitation of Meteorological Satellites EUMETSAT The application ground segment consists of a Central Application Facility CAF and a network of eight Satellite Application Facilities SAFs dedicated to development and operation
180. ng to synoptic charts this low pressure centre will tend to deepen and build up due to feeding with cold air masses from over Barents Sea flowing between low over Scandinavia and above mentioned low pressure centre This cold air will force up warm air masses Condensing water vapor will stimulate clouds development and also emitting condensation energy supporting vorticity of the low pressure centre ERTER Product Validati R t PVR 02 Doc No SAF HSAF PVR 02 1 1 Schellle ere roauc alldation Report HSAF p acilities Issue Revision Index 1 1 yao end Waler Product H02 PR OBS 2 Date 30 09 2011 Page 107 177 A A a x 11015 1010 1005 AT Ki k 995 2 e g 4 019 990 a 35 45 VY 9 lt 8 y mdd neat POA89 QPOAB9_EDZW ees ee oaa na_p 000_000 P k l Boden gt 2 Mo 27 09 10 12 UTC http www wetter3 de Figure 83 Synoptic chart at 1200 UTC on 27th of September 2010 Comparison On the next figure the HO2 product is visualized for the noon overpass For comparison the distributions of 10 minute precipitation obtained from RG data measured at closest to the given time slots are presented The RG derived precipitation maps were prepared using Near Neighbor method RG rain rate 27 09 2010 1250 UTC H02 rain rate 27 09 2010 1251 UTC Figure 84 PR OBS 1 at 1251 UTC on the 27 of September 2010 right panel and 10 minute precipitation i
181. nt 120 km rain effective range of the radars inside which data are included in the statistical scores computation Despite the HO2 overestimation a good spatial consistency between the HO2 and radar precipitation fields can be observed Even the patterns of light precipitation were localized by HO2 quite well Scores evaluation Statistical scores have been computed separately for each of the two satellite passages but also for common dataset from both passages Totally 249 radar satellite pairs from the 12 07 UTC passage and 190 pairs from the 12 08 UTC passage have been included in the computation Results of the scores for continuous and dichotomous statistics for precipitation threshold of 0 25 mm h are presented in the next two tables respectively Satellite passage sg Gai 1A Oa G Common poanaag NOAA18 NOAA19 Number of satellite values Number of radar values Mean error mm h Standard deviation mm h Mean absolute error mm h Multiplicative bias Correlation coefficient Root mead square error mm h RMSE Table 35 Scores for continuous statistics for edea ion threshold of 0 25 iah In agreement with visual comparison of precipitation fields the scores of continuous statistics Table 1 as Mean Error and Multiplicative bias exhibit overestimation of the H02 product in this case The Multiplicative bias and URD RMSE obtained for the 12 07 UTC passage are better than for the 12 08 Doc No SAF HSAF PVR 02 1 1 Issue Revision Inde
182. nt the correct retrieval of small sub IFOV precipitation structures It has also to be mentioned that this case study does not show differences between land and sea algorithms as it was remarked in the 2009 case study 5 6 Case study analysis in Poland IMWM 5 6 1 Case study August 15 2010 Description Significant cloud layer reaching over Lower Silesia region with its upper constituent belongs to developing low pressure centre That structure is a part of bigger low pressure centre over France and tends to move over Germany to Poland Stripe of clouds extending from Tunis through central Italy Adriatic Sea to Austria is a cold front of Atlantic air which is going to reach Poland on Monday 16 of August when bay of low pressure over Germany moves over Poland Mentioned above bay of low pressure extends further over Balkans with significant wind convergence stimulating convection updrafts with large scale moves Moreover the forecast dated on 0000 UTC shows very turbulent night because of development of low pressure centre over Poland e J 5 PEORES LA tee a a bad p 000 QONPPOASOOPOA9 EDZW N S pow ie N 2 ins Boden it ai Agii aed wth fotlerdienst ne M http www wetter3 de Figure 78 Synoptic chart at 1200 UTC on 15th of August 2010 Convective storms where observed over the country on that day The precipitation was accompanied by lightning activity On the next figure the lightning activity ma
183. nterpolated from RG data from 1250 UTC left panel On the both maps the precipitating area is located along with South West and North Polish borderlines however the its spatial distribution is underestimated by HO2 product Moreover some rainfall observed in the Central Poland was not recognized by satellite product at all The rainfall maxima measured by ground stations in western and northern Poland were missed by H02 what resulted in more homogenous precipitation distribution obtained for satellite product Statistical score Further analysis was performed for all overpasses available for the 27 of September 2010 The ability of PR OBS 01 product to recognize the precipitation was analyzed using dichotomous statistics parameters The 0 25mm h threshold was used to discriminate rain and no rain cases In the next The EUMETSAT Doc No SAF HSAF PVR 02 1 1 ste Aoi HSA Product Validation Report PVR 02 M gt Issue Revision Index 1 1 a Product HO2 PR OBS 2 Date 30 09 2011 Page 108 177 table the values of Probability of Detection POD False Alarm Rate FAR and Critical Success Ratio CSI are presented Parameter Scores POD 0 52 FAR 0 18 CSI 0 47 Table 34 Results of the categorical statistics obtained for H02 on the base of all data available on the 27 September 2010 Reasonably high value of POD and low value of FAR indicate that the product ability to recognize the stratiform precipitation is rather go
184. observed that HO2 reproduces the rainfall patterns and amounts with quite good confidence It has some difficulties with proper recognition of rainfall maximum About the convective systems it has been observed that HO2 did not well detect the small medium size thunderstorms This effect is due to a typical size of these convective cells which does not exceed the HO2 AMSU B and HMS IFOV Capturing of convective Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 115 177 Product Validation Report PVR 02 cores by satellite IFOV or in upscaled radar image is strongly dependent on the mutual position of convective core and satellite IFOV centres Other cases of medium large size convective cells have showed a general correct qualitative location and estimation of the precipitation by H02 especially moderate and heavy ones rain rate gt 10mm h while the light precipitation is either overestimated or missed The dichotomous statistical scores evaluated for the summer cases have quite different values mean values POD 0 60 FAR 0 40 and CSI 0 40 but there are case studies with POD ranges between 0 8 0 9 During winter period when more stratiform events occur the HO2 product well detects the precipitation area but it underestimates the precipitation apart from few cases see Turkish and Polish cases The satellite product misses or strongly underestimates the rainfall In general for the
185. od The quality of HO2 in estimating the convective precipitation is presented on the next figure Most of the points on the scatter plot are located under and along with the diagonal what indicates that that HO2 tends to underestimate the rain rate rarely exceeding the 4 mm h value Rain rate H 02 mm h Rain rate RG mm h Figure 85 Scatter plot for measured RG and satellite derived H 02 rain rate obtained for all HO2 data on the 27 of September 2010 Finally the analysis of rain classes was performed The categories were selected in accordance with the common validation method Next figure shows the percentage distribution of satellite derived precipitation categories within each precipitation class defined using ground measurements One can easily notice very good ability of HO2 to recognize no rain situations more than 90 of ground cases was properly allocated by satellite product The light precipitation is strongly underestimated 68 of cases is allocated in the no rain class Better results were obtained for moderate precipitation 40 of pixels in this class was properly recognized by H02 however 34 of cases was overestimated and 27 was underestimated The EUMETSAT Product Validation R ETT Doc No SAF HSAF PVR 02 1 1 GE Hsaf Product Validation Report PVR 02 Revision Index 1 1 akn ii Product H02 PR OBS 2 Date 30 09 2011 Page 109 177 mm h m 1 10 E 0 25 1 0 m 0 0 25 c
186. od indicator of the continuous supervision of quality of radar data and the important element to sustain radar data quality Overview of radar products used for validation in the HSAF project The Table at the end of the report is provided to summarize the available products generated from radar measurements and the processing chain used to generate them Finally the list of the radar products used for the validation work is included in the last row Radar rainfall products are obtained after processing the measured radar reflectivity at different elevations of the radar scan strategy After each elevation the PPI Plan Position Indicator products and the CAPPI Constant Altitude PPI products are calculated PPI is the measurement of the radar antenna rotating 360 degrees around the radar site at a fixed elevation angle CAPPI products are derived from this by taking into account the radar displays which give a horizontal cross section of data at constant altitude The CAPPI is composed of data from several different angles that have measured reflectivity at the requested height of CAPPI product The PPVG group uses mostly CAPPI products for calculation of rainfall intensities except for Hungary which uses the CMAX data maximum radar reflectivity in each pixel column among all of the radar Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 139 177 Product Validation Report PVR 02
187. of LG CY serve saat AAA AAAA AA OAA AOA ENAA E AEAT O NAAA ATEA E 71 Figure 41 HO1 and HO2 products footprint centers with a sample footprint area as well as the Awos Proun We eka gcd OTSTES oaaae aE E EE A AE A EAA EE E AAAA A re acer enters 73 Figure 42 Meshed structure of the sample HO1 and HO2 products footprint esesesessesseesesrerserrrrsesrersss 74 Figure 43 Synoptic situation on 15 August 2010 at 6 UTC zoom in the surface Map cseeeeeeeeees 77 Figure 44 HO2 image of August 15 2010 at 12 08 left compared with upscaled radar at 12 10 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 cece eeececeeeceeeeeseeeeeeseeeeeseeeeees 78 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 9 177 Product Validation Report PVR 02 Figure 45 H02 image of August 15th 2010 at 12 09 left compared with upscaled radar at 12 10 right the same radar image as above but upscaled on a different grid The scale corresponds to thresholds Of 0 1 1 ANG 10mm Dad siceiisedicesoicesaccvaaiissiineldnnicsinnic Erinn NEEE dinnl nie sient lamb veisirulesinalsnintavatsivaein ee 78 Figure 46 HO2 image of August 16th 2010 at 2 07 left compared with upscaled radar at 2 05 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 ee eeccceeececeeeceeeeeseseeeeeseeeeseeneeees 79 Figure 47 Time evolution of fraction area with rain average
188. of INCA and RADOLAN have similar horizontal resolution to the common radar grid The up scaling software will allow to provide case study analysis and statistical score evaluation for future considerations on the opportunity to use these precipitation integration products in the H saf validation programme 5 Validation results case study analysis 5 1 Introduction As reported in the Chapter 3 the common validation methodology is composed of large statistic multi categorical and continuous and case study analysis Both components large statistic and case study analysis are considered complementary in assessing the accuracy of the implemented algorithms Large statistics helps in identifying existence of pathological behaviour selected case studies are useful in identifying the roots of such behaviour when present This Chapter collects the case study analysis performed by PPVG on HO2 for the year 2010 The Chapter is structured by Country Team one section each The analysis has been conducted to provide information to the User of the product on the variability of the performances with climatological and morphological conditions as well as with seasonal effects Each section presents the case studies analysed giving the following information e description of the meteorological event e comparison of ground data and satellite products e visualization of ancillary data deduced by nowcasting products or lightning network e discussion
189. oint m to the point and is a power parameter that reflects the curvature of the weighting function Another form of geometrical weighting function was proposed by Barnes 1964 as a Fi m W r exp 4 4 13 3 L SRy Unfortunately none of these functions are observation dependent but suggested on the basis of the logical and geometrical conceptualizations only They are based only on the configuration i e geometry of the measurement stations and do not take into consideration the natural variability of the Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 75 177 Product Validation Report PVR 02 meteorological phenomenon concerned In addition the weighting functions are always the same from site to site and time to time However in reality it is expected that the weights should reflect to a certain extent the regional and temporal dependence behavior of the phenomenon concerned For the validation activities the point cumulative semi variogram technique proposed by Sen and Habib 1998 is used to determine the spatially varying weighting functions In this approach the weightings not only vary from site to site but also from time to time since the observed data is used In this way the spatial and temporal variability of the parameter is introduced more realistically to the validation activity Matching approach The temporal and spatial matching appro
190. olia Most of the gauges used in the National networks by the PPVG Partners are of the tipping bucket type and hourly cumulated see Table 4 The rain gauge inventory Chapter 4 proposed by rain gauge WG annex 2 on the instruments the operational network and the approach to match gauge data with the satellite estimates in the PPVG has pointed out that the rain gauge networks available in the PPVG are surely appropriated for the validation of cumulated products 1 hour and higher but probably not for instantaneous estimates The comparison of satellite rain rate with hourly cumulated ground measurements surely introduces intrinsic errors in the matching scores that can be estimated as very large The validation of instantaneous estimates should be carried on only when gauges cumulation interval is 10 to 15 minutes as in Poland Values cumulated over shorter intervals 5 or even one minute as it is done in Turkey are affected by large relative errors in cases of low moderate rain rates Studies are undertaken in order to quantitatively estimate the errors introduced in the validation procedure comparing the instantaneous satellite precipitation estimation with the rain gauge precipitation cumulated on different intervals Moreover the revisiting time 3 4 hours of HO1 makes impossible or not reasonable to validate the product for 1 24 hours cumulated interval The WG has also pointed out that different approaches for the estimates matching a
191. om its development in frame of the ongoing INCA CE project In Germany similar precipitation analysis system called RADOLAN is being run operationally This tool is already used for validation of the H SAF precipitation products in this country The accuracy and reliability of the raingauge stations significantly affect final precipitation analysis of the INCA or INCA like systems and therefore need to be checked In order to solve this problem an automated blacklisting technique is going to be developed at SHMU currently blacklisting is used in manual mode The case studies presented in the report comparing the INCA analyses with corresponding input precipitation fields from radars and raingauges pointed out the benefits of the INCA system It has been shown that the system has potential to compensate errors due to effects like radar beam orographical blocking but also to correct instantaneous factors affecting radar measurement quality like radar beam attenuation in heavy precipitation what cannot be achieved by standard methods of climatological radar data adjustment First attempts to utilize the INCA analyses as a ground reference data for the H SAF products validation have been done by statistical analysis of the PR OBS 2 product during selected precipitation events The software for upscaling the INCA precipitation field into the H SAF products grid will have to be developed Since the grids of INCA and RADOLAN have similar horizontal r
192. on Index 1 1 ilo and Wot Product H02 PR OBS 2 Date 30 09 2011 Page 52 177 The combination of hourly point measurements at the precipitation stations with the five minute interval radar signals of the 16 weather radars C Band Doppler provides gauge adjusted hourly precipitation sums for a 1km x 1km raster for Germany in a polar stereographic projection Radar site Latitude N Longitude E WMO No Radar site Latitude N Longitude E WMO No 48 20 14 54 10 35 12 03 33 50 01 25 08 33 34 52 09 39 53 37 19 09 59 52 47 52 28 08 00 18 Berlin 52 28 43 13 2317 10384 Eisberg 49 32 29 12 24 15 10780 Tempelhof 51 24 22 06 58 05 10410 Flechtdorf 51 18 43 08 48 12 10440 52 27 47 09 41 54 10338 Neuheilenbach 50 06 38 06 32 59 10605 53 20 22 07 01 30 10204 48 35 10 09 47 02 10832 50 30 03 11 08 10 10557 51 07 31 13 4611 10488 Table 16 Location of the 16 meteorological radar sites of the DWD Radarverbund des DWD mit 150 km Radien a n raphie Orem ens a E R n ea on 21002556M 2700 amp garner lad geplante Radarstandorte O Radarradius 150 km Figure 23 Left radar compound in Germany March 2011 Right location of ombromete
193. on results long statistic ANALYSIS ccccccssscccesscccesececeesceceeececsencecseucesseceesecesseeeeseaeeesees 115 aL WIEFOGUIGTION oon 115 G2 TAE COM UIMUOUS STA E aaraa EEE EAE ENEA E T 116 63 The MUII CaleCOliCal SLQUSUCccscsccesescteseintesesatesarntesasateicsateiatateiaintoissatesaieteisrateicintoicasieioisieioas 122 6 4 User requirement compliance sssseesssessseessserssrrrssrersrerssrerssrresrerssresserrssreeserrsseeessrreseeessereses 124 E LORU ON e E ee eee eee ee ee ee eee eee ee 124 7 1 Summary conclusions on the status Of Product Validation ccccceseccccessececeeeeeeeeesecereeees 124 7 2 IN SOE G29 arreo ne is eG setts pial nic als lat O alanis islet lable E OENE bis tonic se siecle E EE 125 8 Annex 1 Status of working group cceeccccesseccecessccecceeccceeeececseeeececeeeceeseuecessenecesseeaecetsegeeeeees 127 9 Annex 2 Working Group 1 Rain gauge data cccccceessccccessecccceseececeeseceeeeesececeeeeceeseuaecesseeeeeeas 128 10 Annex Working Group 2 Radardata seasisiesevascneesnasareaesnvarwyvasvnveueawaiees oni abeevasehvasbasetinabennabeuvast ease 135 11 Annex 4 Study on evaluation of radar measurements quality indicator with regards to terrain VI SIDIINMEY a cececucecnanceesauceqncsaeaesuaesensaessenen E N ie aaaseueeaeeaeaeaeet 143 12 13 14 15 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product Validation Report PVR 02 Product H02 PR OBS 2 D
194. ootprint area To do so the footprint area is meshed and precipitation amounts are estimated at each grid point by using the precipitation observations at the neighboring Awos sites as shown in next figure A 3x3 km grid spacing is Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 74 177 Product Validation Report PVR 02 considered for the products with elliptical geometry while 2x2 km spacing is considered for the products with rectangular geometry For any grid point Awos sites within the 45 km for the time period of April September convective type and 125km for the rest stratiform type are taken into consideration At each grid point the precipitation amount is estimated by DW m Zi i l Zn 4 13 1 Wim i l where Zm is the estimated value and W rim is the spatially varying weighting function between the i th site and the grid point m Awos sites FOV center Recto Sane WSF ae Te 8 ver eth he tS F Figure 42 Meshed structure of the sample H01 and H02 products footprint Determination of the W r m weighting function in Equation 1 is crucial In open literature various approaches are proposed for determining this function For instance Thiebaux and Pedder 1987 suggested weightings in general as x wT m for r lt R Wim R 12 4 13 2 for hm2R where R is the radius of influence rim is the distance from point i to p
195. p for half an hour time spam 1145 UTC 1215 UTC is presented The map was constructed on the base of data from Polish Lighting Detection System PERUN The EUMETSAT Doc No SAF HSAF PVR 02 1 1 Sotette Arico Product Validation Report PVR 02 Facies ri gt Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 104 177 Time zone 15 08 2010 11 45 00 15 08 2010 11 49 59 Sp Gt ATR DF ORE X l 15 08 2010 11 50 00 a a E DY ot Ce 15 08 2010 11 54 59 2 ninth Tas ose Uline aos Aa Seat Lice gt aS j 15 08 2010 11 55 00 o 15 08 2010 11 59 53 15 08 2010 12 00 00 15 08 2010 12 04 59 15 08 2010 12 05 00 15 08 2010 12 09 59 15 08 2010 12 10 00 15 08 2010 12 15 00 nt A f Center coordinate My yt spe s AS oa Lat 51 N 37 50 Lon 019 E 00 36 Figure 79 Total lighting map of Poland showing electrical activity between 1145 and 1215 UTC on 15th of August 2010 Comparison On the Figure 80 the HO2 product is visualized for the noon overpass For comparison the distribution of 10 minute precipitation obtained from RG data measured at closest to the given time slot is presented The RG derived precipitation map was prepared using Near Neighbor method RG rain rate 15 08 2010 1210 UTC H02 rain rate 15 08 2010 1208 UTC l l l l l l l l 15 16 17 18 19 20 21 22 23 24 16 17 18 19 20 21 22 23 24 Figure 80 H02 at 1208 U
196. perational Issue Revision Index 1 1 aaaea a Product H02 PR OBS 2 Date 30 09 2011 Page 145 177 Figure 98 Composite picture of minimum visible height above the surface over the whole radar network Compositing algorithm selects the minimum value from both radar sites In next step minimum visible heights above the rain gauge stations were derived from the composite picture Distribution of rain gauges according to the minimum visible height of radar beam is shown on next figure It should be noted that while radar beam elevation is reaching 3000m in northern central part of composite picture no rain gauge station was available in this region Only rain gauge stations with minimum visible heights in the interval Om 1100m were available in this study m Minimum visible height of radar beam above the rain gauge m 2000 1600 1400 1200 200 TN ee ee a E a E RUNINA m PAPIN mm RAZTOCNO mm OKRUHLE m LIPOVCE mm SKYCOV m BZOVIK pummmmm KOLBASOV pmm KLAK ZLATA_BANA m CASTA KREMNICKE_BANE CIGELKA TOR YSKY MODRA PIESOK m VALASKA_BELA m BYSTRICANY NIZNA_POLIANKA mmm PUKANEC mumm SENOHRAD mm REMETSKE_HAMRE mmm HENCLOVA mmm RUDNAN y mmm ZEMBEROVCE mumm NALEPKOVO ZLIECHOV m RADOSINA mmm DEMANOVSKA DOLINA JASNA E STOS KUPELE mummu STARA_LESNA NIZNY_KOMARNIK 1 STARA_BYSTRICA BANSKA_STIAVNICA mammum VYSOKA_NAD_UHOM b o o o
197. perational mode in Slovakia and Poland The RADOLAN system is used in Germany operationally and is already utilized for the H SAF products validation Google Figure 105 Coverage of Europe by the INCA and RADOLAN systems Here below a brief description of the INCA and RADOLAN systems follows More information on both systems can be found in the documentation which is available on the H SAF ftp server hsaf WP6000 precipitation WG_groups WG3 inca documentation Brief description of the INCA system The INCA Integrated Nowcasting through Comprehensive Analysis analysis and nowcasting system is being developed primarily as a means of providing improved numerical forecast products in the nowcasting and very short range forecasting It should integrate as far as possible all available data sources and use them to construct physically consistent analyses of atmospheric fields Among the input data sources belong e NWP model outputs in general P T H clouds e Surface station observations T precipitation e Radar measurements reflectivity currently 2 d 3 d in development e Satellite data CLM Cloud type in development for use in precipitation analysis Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 152 177 Product Validation Report PVR 02 e Elevation data high resolution DTM indication of flat and mountainous terrain slopes ridges peaks The INCA
198. pitation amounts were underestimated 5 3 2 Case study June 3 2010 River Danube catchment Description On the beginning of June 2010 the weather was determined by a low pressure area over eastern part of Central Europe Wet hot air out from Mediterranean Sea was directed around the low pressure vortex Bergthora contraclockwise out from north to Bavaria and arrived overhead the near ground cold area By this air advection on 3 of June fell long lasting rain in the catchments of the rivers Regen and Danube and caused a Danube river flood Precipitation amounts over 24 hours reached between 80 mm and 155 mm and Jahrgang 59 Nr 108 Donnerstag 03 06 2010 eg ah X I iS iy Figure 62 Synopsis for Central Europe for 03rd June 2010 ti Berlin http wkserv met fu berlin de Gewasserkundlicher Monatsbericht Juni 2010 Bayrisches Landesamt fur Umwelt Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 ryan oor Product H02 PR OBS 2 Date 30 09 2011 Page 91 177 The EUMETSAT se Saa H SAF Product Validation Report PVR 02 pusy j n Mion ih i i Arkin oN L iitaiedh a i j s ws Td uan Halgetone Ta Se er a Ban cs dace nds poesia l ee eRogtock pivantct r p Mordorngy wa to 1 a as a ae e Mn eh Hirangi j f i wE oni i aHamburp i Lecimardim O j et nf gt yiyor d AA garmiengia peau y Neurciphin ite 05 oct
199. polarization radar at Kojsovska hola are not used because the measured polarimetric data are not operational calibration would be required Software filter for the RLAN interference detected by radars is currently in development at SHMU Radar composite based on CAPPI 2 km products from both radars is used for the H SAF validation The composition algorithm used selects the higher value measured by the two radars in the overlapping area No raingauge correction of the derived instantaneous precipitation is applied Effect of elevating radar beam with increasing range and beam attenuation is reduced by limiting the validation area to rain effective range of 120 km for both radars in the composite The instantaneous precipitation products are provided in Mercator projection with approximately 1 km resolution Threshold for precipitation detection is 0 02 mm h Time resolution of the current instantaneous products is 5 minutes for the products prior to April 2010 it was 10 minutes and prior to August 2009 15 minutes Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 70 177 Product Validation Report PVR 02 Precipitation accumulation in case of 3 hourly interval is based on integration of 5 10 or 15 minutes instantaneous measurements in time period of 3 hours Accumulated precipitation for intervals of 6 12 and 24 hours is calculated as a sum of the 3 hourly accumulated precipitation A
200. ppear more aligned to the ones obtained in the long period analysis with noticeable underestimation This might be connected with the fact that in this case the fraction of area interested by the rainfall is smaller see Figure 52 and for comparison Figure 47 Sample 12 Mean error 0 72 Standard deviation 1 66 Mean absolute error 1 06 Multiplicative bias 0 52 Correlation coefficient 0 41 Root mean square error 1 73 URD RMSE 0 95 POD 0 59 FAR 0 36 CSI 0 44 Table 22 Scores obtained with the comparison with radar data in mm h The time evolution of the fraction area with rain the average rain rate over this area the Equitable Threat Score ETS and the root mean square error RMSE is reported in the following figure Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 83 177 Product Validation Report PVR 02 3 0 N an gt 0 25 mm h N oO 1 5 t D Fraction area gt 0 25 mm h ao n P N Loritiritiritiiil wa Precipitation a 22 23 24 22 23 24 25 4 l E of w J D 0 4 E 0 2 E 4 0 0 4 2 22 23 24 25 22 23 24 vl Figure 52 Time evolution of fraction area with rain average rain rate over this area threshold 0 25 mm h RMSE and ETS during the present case study Conclusions From qualitative and statistics comparison it appears that for this case study the h02 produc
201. precipitation underestimated by radars and improved the INCA analysis Figure 108 part c the EUMETSAT Proda valdaione t PVR 02 Doc No SAF HSAF PVR 02 1 1 Satellite firein tan r n r ee faciles 2 H SAF PAE eee Issue Revision Index 1 1 Support to Operational Hydrology and Water oe Product H02 PR OBS 2 Date 30 09 2011 Page 156 177 ginal rad T ae INCA Raingauges 01008150600 A a s 201008150600 INCA Radarstraingauges 01008150600 2 A AA Figure 108 Precipitation intensity field from 15 August 2010 6 00 UTC obtained by a radars b interpolated raingauge data c INCA analysis and d PR OBS 3 product 5 57 UTC supplemented with map of minimum visible height above surface level SHMU radar network 15 August 2010 08 00 UTC The case from 08 00 UTC Figure 109 gives an example of partial correction of radar beam orographical blocking by the INCA analysis The radar precipitation field in the north western part of Slovakia Figure 109a is affected by orographical blocking as indicated by relatively high minimum elevations of radar beam above this location in Figure 109e Also in this case information from raingauge network Figure 109b supplemented the radar field in the resulting INCA analysis Figure 109c re produce E E E Doc No SAF HSAF PVR 02 1 1 Satellite Pcl ee r l l n r z ari faciles 2 H SAF PAE eee Issue Revision Index 1 1 ilo and Water Product H02 PR OBS 2 Date 30 09 2011 Pa
202. product does not allow to have a sensible accumulated precipitation map on 1 24 hours In the PPVG the satellite product is compared with the closest up scaled radar and rain gauge data in time The satellite time is considered the time in the BUFR4 file provided by CNMCA when validation area is first reached 3 8 Large statistic Continuous and multi categorical The large statistic analysis allows to point out the existence of pathological behaviour in the satellite product performance It requires the application of the same validation technique step by step in all the institutes take part of the PPVG The large statistic analysis in PPVG is based on the evaluation of monthly and seasonal Continuous verification and Multi Categorical statistical scores on one year of data 2010 for three precipitation classes see next figure It was decided to evaluate both continuous and multi categorical statistic to give a complete view of the error structure associated to HO2 Since the accuracy of precipitation measurements depends on the type of precipitation or to simplify matters the intensity the verification is carried out on three classes indicated by hydrologists during the development phase see next table S lt 1mm h 1 10 mm h gt 10 mm h light precipitation medium precipitation intense precipitation Table 7 Classes for evaluating Precipitation Rate products Precipitation Classes The ra
203. r and rain gauge Working Group WG have been composed in order to solve these problems The first results obtained by the working groups are reported in the following sections and a complete documentation is available as annex 1 7 of this document In addition to the radar and rain gauge WG other WG have been composed on integrate various sets of precipitation data sources raingauge network radar network NWP models outputs and climatological standards into common precipitation product which can describe the areal instantaneous and cumulated precipitation fields INCA WG and to investigate the opportunity to create geographical maps of error distribution for providing information on test catchments to the Hydrological Validation Group GEO MAP WG 3 3 Validation methodology From the beginning of the project it was clear the importance to define a common validation procedure in order to make the results obtained by several institutes comparable and to better understand their meanings The main steps of this methodology have been identified during the development phase inside the validation group in collaboration with the product developers and with the support of ground data experts The common validation methodology is based on ground data radar and rain gauge comparisons to produce large statistic multi categorical and continuous and case study analysis Both components large statistic and case study analysis are considered complem
204. r in the radar field However observation by different satellites or time lag between the compared fields could also be the reason of this discrepancy Despite the observed H02 overestimation the overall spatial consistency of the HO2 and radar fields is satisfactory as confirmed by relatively good results of the correlation coefficient and very good scores of dichotomous statistics see above 5 8 Case study analysis in Turkey ITU 5 8 1 Case study October 20 2010 Description As it can be seen from next two figures Turkey is in low pressure area and there are respectively warm and stationary fronts rain bands and precipitation in western part of Turkey on October 20 at 06 00 GMT and 12 00 GMT http fjwww wetter3 deffax 20 10 10 06 UTC 00 Figure 89 Atmospheric condition 20 10 2010 06 00 GMT The share ons yo Sotelli ae zic at Product Validation Report PVR 02 t H SAF Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 alr a sth Operatio ogy an rd Wate Product H02 PR OBS 2 Date 30 09 2011 Page 113 177 Managemen i H lOS 1025 Ls 1 sf A e http fjwww wetter3 deffax 20 10 10 12 UTC 0O Figure 90 Atmospheric condition 20 10 2010 12 00 GMT Data used In this case study 193 rain gauges which have specifications as explained in section 4 12 in western part of Turkey has been used HO2 product on October 20 at 10 44 GMT has been compared with gauge observations
205. rain rate over this area threshold 0 25 mm h RMSE and ETS during the present case StUGY ccccsssssseseeeeeececccccccceeeeeseuueeeeeeseseeeceeeeeeeseeess 80 Figure 48 Surface map on 22 August 2010 at 06 UTC MSLP and synoptic observations ccccc00 80 Figure 49 H02 image of August 23th 2010 at 2 35 left compared with upscaled radar at the same time right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 cc eeeceseeeeeeeeeeeee 81 Figure 50 H02 image of August 23th 2010 at 12 23 left compared with upscaled radar at 12 25 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 ee eecccceesseecceeeeeeeeeeeeeeeeees 81 Figure 51 H02 image of August 23th 2010 at 12 23 left compared with upscaled radar at 12 25 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 cee eeccccceesseeceeeeeeeseeeeeeeeeees 82 Figure 52 Time evolution of fraction area with rain average rain rate over this area threshold 0 25 mm h RMSE dnd ETS during the present case StUGY ssiisicicccrcesccutsssoncseneedsvacanenecsdrsesminpsscnsadvonsdaeieenamecttes 83 Figure 53 Surface map on 13 November 2010 at 06 UTC MSLP and synoptic observations 83 Figure 54 H02 image of November 13th 2010 at 12 53 left compared with upscaled radar at 12 55 right The scale corresponds to thresholds of 0 1 1 and 10 MM A 1 cee eccccceessececeeeeeeeeeeeeeeeeees 84 Figure 55 H02 ima
206. ral Neighbour method what indicates that application of this algorithm may allow for minimizing the systematical error introduced by spatialisation method Therefore this method seems to be the best for creating the geographical distribution of H SAF products error for countries characterized by terrain geographical configuration similar to the Polish one Conclusions The analysis performed for ME of H 05 3 h cumulated product obtained using data from Polish network of rain gauges showed that Natural Neighbour interpolation method seems to be the best one for creating maps of H SAF products error However application of Natural Neighbour method does not allow for extrapolating the distribution beyond the area defined by stations what is a disadvantage of these methods As the maps are to be created for the whole H SAF domain presented above results should be verified over other countries Therefore in the next step of WG5 activities the study will be performed for other countries and for the errors calculated using radar data Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 171 177 Product Validation Report PVR 02 15 Annex 8 Comments on the Validation Results for Products PR OBS 1 PR OBS 2 And PR OBS 3 Casella F Dietrich S Levizzani V Mugnai A Laviola S Petracca M Sano P F Zauli CNR ISAC CNMCA VS EUMETSAT The results of WGs sai
207. re considered in the PPVG One of the next step of the WG will be to define in collaboration with the GeoMap WG Annex 7 the spatial interpolation technique and to develop the related software to be used in side the PPVG Country ioi eete Cape Maimun Gereta ne Heating system cumulation coe imema mme amn AUAN neti NAA ee rr O00 Italy 0200800 Table 4 Summary of the raingauge characteristics only 300 out of 1800 gauges are heated The EUMETSAT s Doc No SAF HSAF PVR 02 1 1 saaie Anca Product Validation Report PVR 02 Foci m SA Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 23 177 An inventory on radar data networks and products used in PPVG Chapter 4 has pointed out that all the institutes involved in the PPVG declared the system are kept in a relatively good status and all of them apply some correction factors in their processing chain of radar data In Figure 7 there is the map of the 54 C band radars available in their H SAF PPVG Only the radar data which pass the quality control of the owner Institute are used by the PPVG for validation activities However these correction factors are diverse in the countries depending on their capacities and main sources of error in the radar measurements This also means that the corresponding rainfall estimates are different products in nature and the estimation of their errors cannot be homogenized for all the countries of the PPVG However each
208. recipitation with increments of 0 1 mm quality index of the measurements between 1 and 10 8 9 Conventional precipitation gauges type Wild measuring 24 hourly totals of precipitation Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 50 177 Product Validation Report PVR 02 The rainrate is given only by the automatic stations for a 60 minutes interval Those stations are located in Varbica and Chepelarska river basins There are no automatic stations in Iskar river basin Data processing There is quality control on the data In this Project the point like gauges data are interpolated for using Co kriging interpolation of the ground measurements taking into account orography 4 7 Ground data in Germany BfG The H SAF products are validated for the territory of Germany by use of two observational ground data sets SYNOP precipitation data based on the network of synoptical stations provided by the German Weather Service DWD and RADOLAN RW calibrated precipitation data based on the radar network of DWD and calibrated by DWD by use of measurements at precipitation stations Data _ Number Resolution Synoptical stations 6h 12h_ Near realtime Precipitation 1100 Automatic precipitation stations Stations RADOLAN RW 16 German radar 1 hour Near real time Quantitative radar composite Sites product RADOLAN RW Radar data 1 km x 1 km after adjustment wit
209. reliable radar fields would be possible Separation would be based on radar site geographical areas event type radar products Selected cases will be suitable enough to be used as a reference for the H SAF products validation Satellite product testing will be carried out in areas with higher reliability Statistical results will be Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 140 177 Product Validation Report PVR 02 evaluated and compared to previous data As such the accuracy of statistical results of PPVG with radar data as ground reference will be able to be established References References have been collected from each country describing radar data radar data quality and radar data quality estimation techniques This list will be the baseline for further work of the Radar WG The following list of references has been set up Belgium Goudenhoofdt E and Delobbe L Evaluation of radar gauge merging methods for quantitative precipitation estimates Hydrol Earth Syst Sci 13 195 203 doi 10 5194 hess 13 195 2009 2009 http radar meteo be en 3302595 Publications html Berne A M ten Heggeler R Uijlenhoet L Delobbe Ph Dierickx and M De Wit 2005 A preliminary investigation of radar rainfall estimation in the Ardennes region Natural Hazards and Earth System Sciences 5 267 274 http radar meteo be en 3302595 Publications html Italy Fornasi
210. res hourly data provision about 500 circles event based hourly data provision ab OUlGO0 STATIONS esaiar siinnd ana en O ES 52 Figure 24 Flowchart of online calibration RADOLAN DWD 2004 ssessssesssssesrsreerrrsesrrersrrrrserrrreerreress 53 Figure 25 The location and coverage of the three meteorological Doppler radars in Hungary 53 Figure 26 Correlation between rainrates detected by two close by stations as function of the distance between the two stations Colors refer to the month along 2009 ceecccessecccceesececeeseceeeeeeeeeeeaeees 56 Figure 27 Distribution of the raingauge stations of the Italian network collected by DPC 0 57 Figure 28 Italian radar network coverage ccccssecccesscccesccceescecsesceceenceceenceceeeeseeaeeeeeeesseaeesseseetseaeeteges 58 Figure 29 Graphical mosaic of reflectivity CAPPI at 2000 m for the event of 04 18 08 at 0015 U T C 59 Figure 30 Architecture of the Italian radar network ccsssesccccccessseceeeceesseccecsaeeeeeceeeseeaseceeesaaaseeeeeeeas 60 Figure 31 Schematic representation of radar data processing chain ccccccsseccccessecccceesececeeeceeeeeseees 61 Figure 32 Measured upper panel and attenuation corrected lower panel PPI 1 0 deg of reflectivity observed on 09 14 08 at 0500 U T C by the polarimetric radar operated by Piemonte and Liguria HONS a ca pass cael eases E a ee es 62 Figure 33 Hydrometeor classes as detected by the c
211. reses 133 Table 57 Matching strategies for comparison with HO3 and HOS ccccsceseccceeececeecsceeeeeseeseeeeeness 133 Table 58 List Of products used cccccceccceseccccesecccenccecenececeneceeeecseeuececeuecseeueceseueceseueceesueceseueeeseeaeeseuness 142 Table 59 List of contact PEFSONS cccccsseccccesseccccesecccceusececcesececeuseceeceuseceseeueeceseuaecesseenecessunecesseusecetsegas 151 Table GO OUCSTIONMAING orris Or S NERS NES IRSE E SEE APOASE TO RAAE OEN ENEI AS EE sata EEN EEE SS AIE ENAT 154 Table 61 List of precipitation events selected for statistical analysis sssessssssesreesrrrerrrserrrerrererrresns 158 Table 62 Mean Residual and Mean Absolute Residual values obtained for three algorithms for spatial interpolation using cross validation approaCh s ssesssseessseessrrsssrrsseressresseressressrerssreeserreseeessereseerssreess 170 Table 63 Simplified compliance analysis for product PR OBS 1 2 3 ccecccccsssececeeesececeeseceeeaeseceeeeees 171 Table 64 Errors of the ground reference provided by all validation groups sesssseseseseseesesrresesrreserrrress 172 Table 65 RMSE and standard deviation of interpolation algorithms for 3 different regular grids 175 List of figures Figure 1 Conceptual scheme of the EUMETSAT application ground segment ccccceesececeeeeeeeeeeneees 14 Figure 2 Current composition of the EUMETSAT SAF network in order of establishment 14
212. rithms of the precipitation analysis e g assimilation of the 3 D radar data can be expected from its development in frame of the ongoing INCA CE project In Germany similar precipitation analysis system called RADOLAN is being run operationally This tool is already used for validation of the H SAF precipitation products in this country The accuracy and reliability of the raingauge stations significantly affect final precipitation analysis of the INCA or INCA like systems and therefore need to be checked In order to solve this problem an Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 47 177 Product Validation Report PVR 02 automated blacklisting technique is going to be developed at SHMU currently blacklisting is used in manual mode The software for upscaling the INCA precipitation field into the H SAF products grid will have to be developed Since the grids of INCA and RADOLAN have similar horizontal resolution to the common radar grid the radar upscaling techniques can be applied also on the INCA or RADOLAN data In frame of the unification of the validation methodologies the same common upscaling software could be shared between both radar and INCA working groups in the future 4 5 Ground data in Belgium IRM 4 5 1 Radar data The network Belgium is well covered with three radars see next figure Further radar is currently under construction in the coastal regio
213. roduct Validation Report PVR 02 pixel geolocation is retrieved by using the information made available by satellite owners and it is not perfect it is necessary to evaluate how much mislocations impact on the accuracy of the comparison The effect is clearly larger for convective precipitation This may explain why product PR OBS 3 is apparently performing better than PR OBS 1 and PR OBS 2 the high resolution minimizes mislocation errors similarly time mismatching is a source of error more effective for convective precipitation hence the advantage of PR OBS 3 and also of radar contributing to reduce the effect of intrinsic lower accuracy parallax errors introduce mislocation of satellite precipitation with associated comparison errors larger for convective precipitation because of deeper penetration in the upper troposphere These and maybe other error sources need to be analyzed in detail in order to determine their contribution to the overall RMSEcomparison WG and VS started to evaluate the sources of errors awaiting the final results is it possible to reconsider the requirements like to understand the thresholds of requirements in table 63 Then it is need to anticipate the likely size of these errors The very low POD values and very high FAR values as well as the invariably poor values of the correlation coefficient indicate that RMSEcomparison could be dominant in the error partitioning with RMSEsat and RMSEground An
214. rrain visibility by current radar network of SHMU as is shown in next two figures Figure 103 Final relative root mean square error map of radar measurements with regard to terrain visibility by current radar network of SHMU Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 148 177 Product Validation Report PVR 02 Figure 104 Final mean error map of radar measurements with regard to terrain visibility by current radar network of SHMU General underestimation of precipitation by radars is observed Conclusions Considering the fact that reference precision of rain gauges used in this study is not sufficient and they do not reflect real ground reference of precipitation fields obtained results can be considered as a ceiling guess of radar measurements quality indicator with regards to terrain visibility This result includes also the error of rain gauge network itself Also averaged mean error root mean square error and relative root mean square error values were computed for 68 rain gauge stations located in radar horizons Averaged mean error 0 184 mm h for instantaneous or 4 42 mm for 24 hours cumulated precipitation Averaged RMSE 0 395 mm h for instantaneous or 9 48 mm for 24 hours cumulated precipitation Averaged URD_RMSE 69 3 for 24 hours cumulated precipitation It should be noted that all computations in this study were based on 24 hour cumulated precipitation an
215. rs the satellite product accuracy the accuracy of the ground data used and the limitations of the comparison methodology e g errors of space and time co location representativeness changing with scale etc Therefore the results currently found are by far pessimistic in respect of what is the real product performance 7 Conclusions 7 1 Summary conclusions on the status of product validation The H02 product has been validated by the PPVG on one year of data 1 of December 2009 30 of November 2010 Each Country Team have provided case study and long statistic analysis using radar and rain gauge following the validation methodology reported in Chapter 3 The results of the Precipitation Validation Programme are reported in this Product Validation Report PVR A precipitation product validation section of the H SAF web page is under development This validation web section will be continuously updated with the last validation results and studies coming from the Precipitation Product Validation Group SPVG It is well know that radar and rain gauge rainfall estimation is influenced by several error sources that should be carefully handled and characterized before using these data as reference for ground validation of any satellite based precipitation products A complete inventory of the precipitation ground networks instruments and data available inside the PPVG has been provided in Chapter 4 in order to highlight the main error source
216. rs for online calibration in RADOLAN squares hourly data provision about 500 circles event based hourly data provision about 800 stations enna gt roduct Validation Report PVR 07 POC NO SAF HSAF PVR 02 1 1 Satelite Application roauct VallQation Report z H SAF p Issue Revision Index 1 1 tyaciogy ana Water Product H02 PR OBS 2 Date 30 09 2011 Page 53 177 The flowchart of online calibration method applied in RADOLAN is depicted in next figure Preprocessing I 5 min intervalls shading correction refined Z R relation KIE La composite production Y l i s ee j y Preprocessing II 2 xin 60 min a A summation to hourly composits a AE ene a statistical clutter suppression a WE M ay interpolation ee Gare Preprocessing III of Radar data with K PR PA VW op station data every 60 min een Lt smoothing Neuheilen iim 3 atk precalibration oh j Calibration of Radar data with station data every 60 min ee calculation of calibration params yr A TAE mia and interpolation d calibrations E 2 intersection of different calibration b l a q l ae procedures for best result 4 FE Bi ht F praa J al Frankfurt Flechtdorf im Munchen Figure 24 Flowchart of online calibration RADOLAN DWD 2004 4 8 Ground data in Hungary OMSZ 4 8 1 Radar data The network The main data used for validation in Hungary would be the data of meteorolog
217. s 7 mm h while by H02 it is 22 mm h For the passage of NOAA19 at 12 08 UTC second row in the figure the corresponding maximum value is 3 mm h and 20 mm h respectively Spatial shift between the maxima in HO2 and coincident radar field is about 65 km for the 12 07 UTC passage but only 16 km 1 satellite along track sampling distance in case of the 12 08 UTC passage Generally there are slightly smaller differences between higher precipitation rates observed by H02 and radars for the 12 08 UTC passage This could be a result of larger closer to the swath edge IFOVs that smooth more the extremes in precipitation fields However observation by different satellites and time lag between the compared fields could also be the reason SHMU Radars PR OBS 2 201008151205 201008151207 the EUMETSAT Proda veldaiione t PVR 02 Doc No SAF HSAF PVR 02 1 1 Stet Asai a H SAF roduct Validation Report ri leeds Issue Revision Index 1 1 ride Product H02 PR OBS 2 Date 30 09 2011 Page 111 177 SHMU Radars PR OBS 2 201008151210 201008151208 in intensity mm h 30 Figure 88 Instantaneous precipitation fields from 15 August 2010 observed by H02 product left column and SHMU radar network right column corresponding to NOAA18 passage at 12 07 UTC top row and NOAA19 passage at 12 08 UTC second row The precipitation values are shown as satellite IFOVs projected over the radar composite domain White contoured circles represe
218. s and to present possible methodology for selecting the Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 125 177 Product Validation Report PVR 02 ground data more reliable Annex 1 7 In the last months the first example of precipitation fields integration has been also provided Section 4 4 3 INCA and RADOLAN products The INCA system a tool for the precipitation products validation is available in Slovakia and Poland in both countries being run in pre operational mode In Germany similar precipitation analysis system called RADOLAN is being run operationally The study performed in the PPVG Annex 5 showed that the accuracy and reliability of the raingauge stations significantly affect final precipitation analysis of the INCA or INCA like systems In order to solve this problem an automated blacklisting technique is going to be developed at SHMU currently blacklisting is used in manual mode Eleven case study analysis of HO2 have been here reported in Chapter 5 Stratiform and convective precipitations during summer and winter periods have been analysed in different countries Rain gauges with 10 minutes refresh time radar data and nowcasting tools have been used to highlight different characteristics of the satellite product The case studies proposed have pointed out that different statistical score values are obtained during summer and winter period problems on coast line
219. s derived from 475 points at the time Fact that rainfall is measured by two equally sensitive instruments two meters away from each other at the same post enables to apply simple in situ data quality control during summertime During winter non heated rain gauge is covered with a cup to prevent it from being clogged by the ice and damaged Because of that the precipitation information Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 tycka a Waler Product H02 PR OBS 2 Date 30 09 2011 Page 66 177 The EUMETSAT FETE are Product Validation Report PVR 02 HSAF p derived from ATS network in winter cannot be verified using this method It can be stated that during the wintertime precipitation information might be burdened by a slightly bigger measuring error The number of rain gauges available for H SAF validation activities varies from day to day due to operational efficiency of ATS network in Poland and depends on large number of independent factors It can be stated that the number varies between 330 and 475 rain gauges for each day of operational work Mean minimum distance between precipitation measuring ATS posts between each pair of rain gauges in Polish national network is 13 3 km N A Legend ATS gauges e SYNOP gauges 0 100 200 km Main rivers Figure 36 ATS national network in Poland The instruments All rain gauges working within Polish ATS national network are MetOne tipp
220. s for the estimates matching are considered and probably could be a good idea to harmonize them among partners As an example for H02 a document was delivered by the developers where the best estimate ground reference matching strategy was indicated and also Angelo Rinollo delivered few years ago the code for the Gaussian weight of the antenna pattern in the AMSU MHS IFOV Anyway different approaches over different Countries are leading to very similar values in the considered skill scores indicating probably two things 1 none of the considered approaches can be considered as inadequate and more important 2 the differences between ground fields and satellite estimates are so large that different views in the data processing do not results in different numbers References Duchon C E and G R Essenberg G R 2001 Comparative rainfall observations from pit and aboveground gauges with and without wind shields Water Resour Res 37 3253 3263 Duchon C E and C J Biddle 2010 Undercatch of tipping bucket gauges in high rain rate events Adv Geosci 25 11 15 Leitinger G N Obojes and U Tappeiner 2010 Accuracy of winter precipitation measurements in alpine areas snow pillow versus heated tipping bucket rain gauge versus accumulative rain gauge EGU General Assembly 2010 held 2 7 May 2010 in Vienna Austria p 5076 Sevruk M Ondras B Chvila 2009 The WMO precipitation measurement intercomparisons Atmos Res 92 376 3
221. s international radars 397 SHMU CHMI ZAMG IMWM Input data Number of radars in network TBD Number of precipitation stations 1300 475 Poland only Blacklist for precipitation stations Yes No l l Map of density of precipitation stations Density of raingauge stations y gaug Yes No Instantaneous precipitation based only Output data on raingauge network time resolution Yes 15 min Yes 15 minute timelines Instantaneous precipitation based only on radar network time resolution i Yes 5 minute Yes 5 minute timelines Instantaneous precipitation based on combined raingauge and radar i 3 i Yes 5 minutes Yes 5 minutes network time resolution timelines Cumulative precipitation based only on raingauge network time intervals 5 min 1 3 6 12 18 24 hours timelines Yes min 5 min available Yes min 5 min available 1 3 6 12 24 hours 1 3 6 12 24 hours Cumulative precipitation based only on 5 min 1 3 6 12 18 24 hour Yes min 5 min available Yes min 5 min available radar network time intervals timelines E tt ay SHES 1 3 6 12 24 hours 1 3 6 12 24 hours Cumulative precipitation based on Y in 1 i Y i i ilabl Y i i ilabl gambined raligauge and radar 5 min 1 3 6 12 18 24 hours es min 10 minutes es min 5 min available es min 5 min available available in future 1 3 6 12 24 hours 1 3 6 12 24 hours Dates for selected case studies 29 3 2009 1 3 6 2010 20 6 2010 15 16 8 2010 Availabilit
222. s provide many measurements within a single AMSU pixel Those measurements should be averaged following the AMSU B antenna pattern Establish the size in km of the axis for each elliptic FOV You will have N 90 couples of values FXn Fyn Define a 2 dimensional Gaussian surface matrix G NXxN having resolution R pixel size Rsradar resolution and elliptical section at half high having axis Ex Eyn equal to the correspondent FOV i e Ex Fx and Ey Fyn see figures below note that if the Radar resolution is 1km 1px 1km 100 Yipx Xipx Figure 9 Left Gaussian filter Right section of gaussian filter If the matrix NxN is too large it can be reduced to a MxK matrix until the pixels 1 C C 1 N C C N are less than C C 100 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 26 177 Product Validation Report PVR 02 e S a S S a oy fs Sonae O OO O S a O O O 0 23 0 25 25 0 25 023 Eunana O O O O Sonone O O O S pf ft S S Sy y os S NA Si PONG FT PL NN pet T TT a Table 6 Left Original Gaussian matrix Right Reduced matrix to dimensions M xK Normalize the matrix G MxK obtaining the matrix G having the sum of all elements equal to 1 G m k wo ua gt gt G m k m l1k 1 3 6 2 Smoothing of radar precipitation For each FOV and for each SCANLINE in the fil
223. se events the FAR has an higher value than POD and the CSI is average 0 15 Some general satellite product characteristics have been highlighted by the case studies here proposed as that parallax shift is particularly effective in case of small convective structures and that ground data hourly cumulated use is incorrect for HO2 validation see Italian case study This is due to the variability of the precipitation field which cannot be caught by hourly integrals provided by the raingauges especially in case of convective precipitation It has also to be mentioned that differences between land and sea algorithms have not been observed It has been showed a case study Poland where the ground data have been unable to catch the precipitation system while the satellite product reproduced more correctly the precipitation area It is also interesting to stress the German case study where ground data of different sources rain gauge and radar have been used for validation exercise over the same region The statistical scores obtained by rain gauge and radar data validation have very similar values 6 Validation results long statistic analysis 6 1 Introduction In this Chapter the validation results of the H02 long statistic analysis are reported for the period 1 12 2009 31 11 2010 The validation has been performed on the product release currently in force at the time of writing Each Country Team contributes to this Chapter by providing th
224. seoseosesreseosesresseseoseeseeresee 39 4 3 4 SOME ON UO aaar EEEE 40 4 4 Rain gauge and radar data integrated products in PPVG sesesseseesssessserssrerssrerssrresrerssrrsseerssees 42 4 4 1 NCA SYE I errr T EEE E E le veteaeeesea ate 43 4 4 2 RADOLAN system ss ssnssnssssessesresrssessesrrsrrsessrssrsrssessesresreseosrosrereseesessesreseosesseseeseesessessesee 44 4 4 3 SOME ON U O EE 46 AS Ground data im Benim I RM asmenine E ANAAO EAA 47 4 5 1 Raar A a arses eee 47 4 6 Ground data in Bulgaria NIMH ssessssesssssessssssrsesesrrnsssrrressrrressrerusrtrrssrtressreresrreresrrereerrreerrrreeens 48 4 6 1 EERE Ee E nee en E E ee E E ee E rr rr eT ere 48 A7 Grounddataimn Germany BIG ararsan anuna as aana hana haaa aea ah maa alaaa a Eaa a anaa ENANA Na aA aA ATAA 50 4 7 1 RN AGS e E A A EA O S 50 4 7 2 Rad r 21 gt e eR eo er Cen CECE 51 4 8 Ground data in Hungary OMSZ csscccccccssseeccccceessecccseeeesececeseeeecceeseueeecceeeeeueeeeeeeaeeneeess 53 4 8 1 Radar Ee oS Oe vv vv TT ve eee ve Tee TOTS Tee tt 53 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product Validation Report PVR 02 Product H02 PR OBS 2 Date 30 09 2011 Page 4 177 49 Ground datain italy DPC Umi Fe wccsisiscesscacesesscacwnassivescsaaieenssaacnenanaceaaessoneassanaeeareneenaauaneanancacs 55 4 9 1 RAIN gauge 20 cceccceccsscceeccscceeecseceseceeeeseceeeeeeesseeeeeeeeesseeeeeeeeeeaeeeeeeeecsaeeeeseeesaeeeaesaectaeene
225. ses When moderate is considered the H02 quality is better almost 60 of the observed precipitation in this class is properly recognized newer ol Product Validation Report PVR 02 e Satellite Apg Doc No SAF HSAF PVR 02 1 1 pon HSA Issue Revision Index 1 1 yaaa and water Product H02 PR OBS 2 Date 30 09 2011 Page 106 177 mm h E gt 10 m 1 10 E 0 25 1 0 m 0 0 25 c 2 gt 2 lt f a e oO VO eTe c VO 3 pa cy a N lt 00 25 0 251 0 110 gt 10 Rain rate RG mm h Figure 82 Percentage distribution of H02 precipitation classes in the rain classes defined using rain gauges RG data on the 15 of August 2010 Conclusions The analysis performed for situation with convective precipitation showed very good ability of H02 product in recognition of precipitation especially moderate and heavy ones rain rate gt 10mm h while the light precipitation is either overestimated or missed The product tends to overestimate the precipitation areas and has some difficulties with proper recognition of rainfall maximum 5 6 2 Case study September 27 2010 Description Low pressure centre left Hungary and heading through Slovak Republic entered Poland territory form South and is building up in the centre of the country However the most rain productive clouds of that low will remain in the SW Poland even when the centre of the low pressure leave NE Poland Accordi
226. solution 1 Resolution 1 km Threshold Rain Gauge Validation km Threshold 5 dBZ No Threshold 31 5 dBZ Correction with limited Activities rain gauge correction No rain gauge number of gauges correction Description of Acc Periods 1 6 24h Acc periods 3 6 12 Acc periods 1 3 6 12 24h accumulated National composite PAC 24h Projection Azimuthal radar product Projection azimuthal National composite Equidistant used in HSAF equidistant standard CAPPI 2 km Resolution 250 m Validation elipsoid Resolution 1 Projection Mercator Threshold Activities km Threshold 0 1 mm Resolution 1 km Rain gauge correction No rain gauge correction Threshold 31 5 dBZ applied for 1h Rain Acc No rain gauge correction Table 13 Inventory of the main radar data and products characteristics in Poland Slovakia Turkey 4 4 Rain gauge and radar data integrated products in PPVG In order to investigate the possible improvement of the ground precipitation field estimation a WG INCA WG has been introduced in the validation activities of PPVG In this section the first results with some considerations of the INCA WG Annex 5 are reported Within the WG participating countries Slovakia Poland and Germany there are two types of systems providing precipitation analyses usable for H SAF validation INCA developed by ZAMG Austria and RADOLAN DWD Germany The INCA system is currently under development as INC
227. sses In opposite to results of HO1 on validation only for the first class over 50 of the HO2 data are in the same class In higher classes most of HO2 data belong to lower classes That means we have a strong underestimation of all precipitation amounts higher than 1 mmh Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 89 177 Product Validation Report PVR 02 RRsatinclass3 OH RRsatin class 4 RRsatinclass1 m RRsatin class 2 class 1 class 2 class 3 class 4 class 1 class 2 class 4 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Radar RR Radar Figure 60 Contingency table statistic of rain rate mmh h for HO2 vs radar data Left for 7th August 2010 Right for whole August 2010 RRsatinclass3 OH RRsatin class 4 B RRsat in class 2 E RRsat in class 1 class 1 class 2 class 4 class 1 class 2 class 4 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Rain Gauge RR Rain Gauge Figure 61 Contingency table statistic of rain Rate mm h for H02 vs rain gauge data Left for 7th August 2010 Right for whole August 2010 Results of the continuous statistic next table show negative mean error ME for detection of precipitation RR gt 0 25 mmh which means that H SAF product underestimates the f
228. sually underestimated due to the loss of water during the tips of the buckets Duchon and Biddle 2010 Drifting wind can also greatly reduce the size of the effective catching area if rain does not fall vertically resulting in a rainrate underestimation quantitatively assessed in about 15 for an average event Duchon and Essenberg 2001 Further errors occur in case of solid precipitation snow or hail when frozen particles are collected by the funnel but not measured by the buckets resulting in a temporal shift of the measurements since the melting and the measure can take place several hours or days depending on the environmental conditions after the precipitation event Leitinger et al 2010 Sugiura et al 2003 This error can be mitigated by an heating system that melts the particles as soon as are collected by the funnel All these errors can be mitigated and reduced but in general not eliminated by a careful maintenance of the instrument A number of a posteriori correction strategies have been developed in order to correct precipitation data measured by raingauges but mainly apply at longer accumulation intervals daily to monthly Wagner 2009 Country Aiii detectable Maximum Gcterianle Heating system cumulation eee etm LSA ey at a a o Germany 0 05 mmh 30000 Italy 0 2mm NAMEN Table 50 Summary of the raingauge characteristics only 300 out of 1800 gauges are heated information not available at the moment a
229. t 16 km intervals the AMSU B MHS resolution at nadir Thus e resolution Ax 40km sampling distance 16 km The observing cycle At is defined as the average time interval between two measurements over the same area In the case of LEO the observing cycle depends on the instrument swath and the number of satellites carrying the addressed instrument For PR OBS 2 there are one MetOp orbit 9 30 LST and up to 5 NOAA satellites However AMSU MHS on NOAA is in a good status only for NOAA 18 and NOAA 19 that follow approximately the same orbit close to 14 00 LST Therefore the total service is equivalent to that one of two satellites around 9 30 and 14 00 LST In average the observing cycle over Europe is At 6 h with actual interval ranging from 4 5 to 7 5 hours Gaps are filled by product PR OBS 1 that also has observing cycle At 6 h but LST around 7 00 and 18 00 with actual intervals ranging from 2 to 10 hours The conclusion is Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 18 177 Product Validation Report PVR 02 e for PR OBS 2 as stand alone from NOAA amp MetOp satellites cycle At 6 h sampling 4 5 7 5 h e forthe composite PR OBS 1 PR OBS 2 system cycle At 3 h sampling 2 4 5 h The timeliness 0 is defined as the time between observation taking and product available at the user site assuming a defined dissemination mean The timeliness depends on th
230. t 0 004 mm h Figure 74 H02 odid left panel Precipitation rate from the Hungarian radar network at its original resolution right panel at 00 30 UTC at 2 15 UTC and at 12 UTC Comparison In this cold front weather situation during the whole day H02 did not detected the middle size thunderstorms Conclusions The H02 in most cases well detects the precipitation area but the middle size thunderstorms were not detected Improvement of the H02 spatial resolution would help the detection 5 4 2 Case study September 2010 Description A cyclone over Mediterranean causes precipitation in Central and South Europe Lot of precipitation was measured mainly in the central part in Hungary Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 100 177 The shar eae eo Aopice JE H SAF Product Validation Report PVR 02 ppor a Operatic bape Vat IDOJARASI HELYZET 2010 09 10 00 UIC SZTEREOGRAFIRUS VETULET r A 60 SZ LESS GI FOKON HOSSZTART f Hea 019 Io Szolg lat D http www met hu Figure 75 Synoptic chart at 00 UTC on 10 a 2010 Data used H SAF HO2 2010 09 10 p 10 58 A al Magyar kompozit Esii Kompozit mm h 2010 09 10 p 11 00 us q Ww No Pi g fr a as L AS A c P Fi 48 75 La 21 g H SAF HO2 0 Figure 76 Precipitation rate from the Hungarian radar network at its original resolution at 11 UTC TC ri
231. t could reproduce the rainfall patterns but regularly underestimating rainfall amounts and areas The results aligned with the ones of long period statistics are sensibly worse than the ones obtained for the other summer case study occurred just one week before 5 2 3 Case study November 12 15 2010 Description of the event A wide area with low pressure extended from Scandinavia to Great Britain and made a very active precipitating perturbation stay over the country during several days next figure and result in high 7 i GE 3 Figure 53 Surface map on 13 November 2010 at 06 UTC MSLP and synoptic observations Data used Products H02 from November 12 at 0 00 UTC to November 15 at 18 00 UTC have been considered in this study The total is 58 satellite passages distributed as follows the EUMETSAT Proda veldaione t PVR 02 Doc No SAF HSAF PVR 02 1 1 Satellite Pace r z e faciles a H SAF i a Issue Revision Index 1 1 Hydrology and Water sib Product H02 PR OBS 2 Date 30 09 2011 Page 84 177 14 in the morning of November 12 2 in the early afternoon of November 12 9 in the morning of November 13 6 in the early afternoon of November 13 9 in the morning of November 14 6 in the early afternoon of November 14 7 in the early morning of November 15 5 in the early afternoon of November 15 The ground data used for validation are the Wideumont radar instantaneo
232. t least 92 of instantaneous measurements must exist in relevant time period for the 3 hourly accumulated product to be produced No rain gauge correction of the accumulated precipitation is applied but the same limitation of validation area is used as for the instantaneous product Threshold for precipitation detection of the 3 hourly accumulated product is 0 5 mm Geographical projection and space resolution of the accumulated products are the same as those of instantaneous product see above For validation of H SAF precipitation products it is necessary to know errors distribution of used ground reference data in case of SHMU it is precipitation intensity and accumulated precipitation measured by Slovak radar network For this purpose a study called SHMU study on evaluation of radar measurements quality indicator with regards to terrain visibility has been elaborated To find distribution of errors in radar range next steps had to be done e simulations of terrain visibility by radar network using 90m digital terrain model e statistical comparison of radar data against independent rain gauge data measurements e derivation of dependence regression equation describing the errors distribution in radar range with regard to terrain visibility based on rain gauge and radar data statistical evaluation computation of error distribution maps using regression equation and terrain visibility Main results of this study are shown in next figure It
233. ta from systematically erroneous rain gauges to enter the analysis Currently the blacklisting is used in manual mode only 4 11 2 Radar data The network The Slovak meteorological radar network consists of 2 radars see next figure One is situated at the top of Maly Javornik hill near city Bratislava and second one is on the top of Kojsovska hola hill close to the city Kosice Both are Doppler C band radars the newer one at Kojsovska hola is able to measure also the dual polarization variables non operational os KojSovska hofa MalyJavornik Figure 38 Map of SHMU radar network the rings represent maximum operational range 240 km for radar at Maly Javornik left 200 km for radar at Kojsovska hola right The instruments The radars are operated and technically maintained by SHMU Receivers of radars are calibrated regularly by means of internal test signal generator TSG In case of radar at Maly Javornik calibration is performed every 3 months and in case of radar at Kojsovska hola every 1 month Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 69 177 Product Validation Report PVR 02 The basic parameters of both SHMU radars are summarized in next table a Maly Javornik Kojsovska hola Frequency band C Band 5600 MHz C Band 5617 MHz Polarization Double but so far only single pol Single Single Double products generated Doppler capability
234. tation in the view of satellite product comparison e optimal time resolution of rain gauge network 15 min 30 min 1 h for a correct comparison with rain rate and cumulated precipitation satellite products e raingauges up scaling techniques vs AMSU B SSMI SEVIRI grids e technique for the temporal matching of rain rate and cumulated precipitation satellite products with rain gauge data Start Time End time January 2011 July 2011 Second Report 31 of March 2011 Final Report 31 of July 2011 Third step code possible Matlab realization for e ground data quality check e comparison between rain gauge and satellite products ee Product Validation R PYRO Doc No SAF HSAF PVR 02 1 1 Sotellite seelcaion roquc alidation Report Facilities a H S AF p Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 129 177 Support to Operational Start Time End time June 2011 November 2011 Codes delivery and related documentation 30 of November 2011 Composition of the working group Coordinator Federico Porcu University of Ferrara supported by Silvia Puca DPC Italy Participants from Belgium Bulgaria Germany Poland Italy Turkey FIRST REPORT Coordinator Federico Porcu University of Ferrara supported by Silvia Puca DPC Italy Participants Emmanuel Roulin and Angelo Rinollo Belgium Gergana Kozinarova Bulgaria Claudia Rachimow and Peter Krahe Germany Rafal lwanski and
235. te 30 09 2011 Page 105 177 Parameter Scores POD 0 83 FAR 0 52 CSI 0 44 Table 33 Results of the categorical statistics obtained for PR OBS 1 on the base of all data available on the 15 August 2010 Higher value of POD than the value of FAR indicate that the product ability to recognize the convective precipitation is quite good The quality of H02 in estimating the convective precipitation is presented on the next figure The points on the scatter plot are mostly arranged above and along the diagonal what indicates that H02 tends to overestimate the precipitation except for the very heavy ones 40 30 20 H 02 mm h 0 10 20 30 40 RG mm h Figure 81 Scatter plot for measured RG and satellite derived H 02 rain rate obtained for all H02 data on the 15 of August 2010 Finally the analysis of rain classes was performed The categories were selected in accordance with the common validation method Next figure shows the percentage distribution of satellite derived precipitation categories within each precipitation class defined using ground measurements One can easily notice very good ability of HO2 to recognize both no rain and heavy precipitation situations respectively more than 90 and 60 of ground cases was properly allocated by satellite product The light precipitation is not properly recognized in most cases it is either overestimated 26 of cases or missed 36 of ca
236. te passages over the SHMU validation area on 15 August 2010 have been selected the NOAA18 observation at 12 07 UTC average observation time of the SHMU validation area and the NOAA19 observation at 12 08 UTC As ground data the instantaneous precipitation field derived by the SHMU radar network is used The closest coincident fields 5 min time frequency to the satellite passages have been selected from 12 05 UTC and 12 10 UTC The radar composites used consist of data from two radars one is situated at Maly Javornik and the second at Kojsovska hola The rule of maximum value selection is applied in the composition The original spatial resolution of the radar field is about 1 km but values upscaled into the satellite grid using the IFOV Gaussian filter are presented For statistical scores computation only the data lying inside the 120 km rain effective range of both radars are considered Comparison The HO2 and upscaled radar precipitation fields for both satellite passages are presented in next figure In both cases an overestimation of the precipitation by HO2 compared to radars can be seen This is obvious especially in case of higher precipitation intensities It should be noted that lower radar intensities especially near the Slovakia Poland border could be also caused by the radar beam blockage and or attenuation in the precipitation For the passage of NOAA18 at 12 07 UTC top row in the figure the maximum value observed by radars i
237. ted by Slovakia Poland and Germany The questionnaire provided details such as geographical coverage see Figure 105 input data inventory or availability of different instantaneous and cumulated precipitation products The final version of the questionnaire is shown in the next table and is also available on the H SAF ftp server hsaf WP6000 precipitation WG_groups WG3 inca questionnaire Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product HO2 PR OBS 2 Date 30 09 2011 Page 154 177 Group of information tem GERMANY POLAND SLOVAKIA domain1 SLOVAKIA domain2 Availability of documentation for INCA or _ If possible please attach link or penis dle ener Documentation available Documentation available Documentation should be similar German system Yes No documentation uung meant ang from ZAMG from ZAMG issued in future Definition of geographical area covered by INCA or similar in Germany system The EUMETSAT ste Asp amp H SAF Product Validation Report PVR 02 Fo yciliti 5 Grid size in pixels 900x900 741x651 501x301 1193x951 Min longitude 3 5943 E 13 82 E 15 99231 E 8 9953784943 E Max longitude 15 71245 E 25 334 E 23 09630 E 25 9996967316 E Min latitude 46 95719 N 48 728 N 47 13585 N 45 0027313232 N Max latitude 54 73662 N 55 029 N 50 14841 N 53 000579834 N Space resolution 1 km 1km 1km 1km Composite of 16 national Composite of 8 national Composite of 2 national Composite of 5 radars radars radar
238. the selection of the interpolation algorithm Commonly used Ordinary Kriging Inverse Distance Weighted and Natural Neighbour methods were tested firstly The analysis was performed for monthly average mean error of H 05 3 h cumulated precipitation for selected months In the analysis data from Polish rain gauges were used In the next figure the example mean error maps for July 2010 obtained using three mentioned above algorithms are presented b H 05 3h July 2010 ME Ordinary Kriging A a E 0 3 H 05 3h July 2010 ME Natural Neighbour E Ee e e 0 3 54 LaL f 4 0 1 pena i D wi aa 0 1 oq Bho y h 0 1 53 H i 0 3 53 S a 05 ns J r 0 3 5 i mE 4 0 5 _ 0 7 U fio A Ni 0 9 51 k R a 0 7 A Sf Ss r ya a r i 0 9 7 1 1 50 Se 7 E 50 4 i y L 1 1 pS N k 1 3 a A 3 s E H 053h July 2010 ME IDW 2 03 0 1 0 4 0 3 05 07 0 9 B 6 7 B8 en an 2 y Figure 118 Distribution of the monthly average H 05 3 h cumulated precipitation Mean Error calculated for July 2010 using three methods a Ordinary Kriging b Natural Neighbour and c IDW 2 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 yaa one Product H02 PR OBS 2 Date 30 09 2011 Page 169 177 The EUMETSAT PEON her a Product Validation Report PVR 02 sit H SAF P One can see that the obtained maps do not differ significan
239. the precipitation data in order to define the confidence level First of all a flagging procedure is defined as described in next table QA Flag Value QA Status Brief Description Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 72 177 Product Validation Report PVR 02 0 Good Datum has passed all QA Test Suspect There is concern about accuracy of datum 2 Failure Datum is unstable Table 20 QA flags descriptions modified from Shafer et al 1999 The precipitation data QA tests are summarized as follows Range Test This test is used to see if any individual precipitation observation falls within the climatological lower and upper limits The test procedures applied in the study are as follows IF LimMpower lt Obser lt Limupper THEN Obser flag is Good IF Obser gt Limupper OR Obser lt LiM ower THEN Obser flag is Failure LiMLower and LimMupper thresholds are separately determined for each station on a monthly basis At any specific site all the observed monthly data is considered for determination of the upper and lower limits By applying this test each observation is flagged either by Good or Failure label depending on the comparison tests mentioned above Step Test It is used to see if increment decrement between sequential observations in time domain is in acceptable range or not The applied test procedure is IF Obser Obser
240. ther hand In the course of this the precipitation scan with five minute radar precipitation data and a maximum range of 125 km radius around the respective site is used for the quantitative precipitation analyses Currently from more than 1000 ombrometer station approx 450 synoptic stations AMDA I Il and AMDA III S of DWD approx 400 automatic precipitation stations AMDA III N of DWD approx 150 stations of the densification measurement network of the federal states the hourly measured precipitation amount is used for the adjustment procedure In advance of the actual adjustment different preprocessing steps of the quantitative radar precipitation data are performed These steps partly already integrated in the offline adjustment procedure contain the orographic shading correction the refined Z R relation the quantitative composite generation for Germany the statistical suppression of clutter the gradient smoothing and the pre adjustment Further improvements of these procedures are being developed Precipitation distribution of Precipitation distribution of RADOLAN precipitation the rain gauge point the areal original radar product measurements measurements 10 00 0 10 PERGZABERN PAD J Fo x VA Figure 106 Procedure of the RADOLAN online adjustment hourly precipitation amount on 7 August 2004 13 50 UTC In order to collect more detailed information about both types of systems a questionnaire was elaborated and comple
241. tion Facilities and H SAF cccccssscccesececessceceesceeeesceeeeneeeeeaees 14 Introduction to product PR OBS 1 ec ceccccesscccesceceeececeescecaesceesesceeeeaceeeeceeseneeeseeeeseeeeseneesseaeeees 15 M ae 011 4 244 2 02 8 ae en nee eee eee ee 15 22 Algorithm principle Seen nen ere ee no CO Ee 16 ZS Wait OP era tlonal eal aC le 1S 11S aacecanaccocsucesanccasescnceanocegenntesaacsessaueeaaaneuasanteeataceasantasaantsasanenaanene 17 Validation strategy methods and TOONS escssacsscsccssanneansnneeniatennnnosnneteunnaneeaunteuannneeenatennnneneauneceuananeaans 18 3 1 Validation team and work plan sssesssseesseesssenssrerssressrersseesssrrsseesssressrerssrerserreseeessereseersseeeseeree 18 3 2 Validation objects and Problem ccccccssscccesscceesscccescecaesceceesceeeescessesceeseeesseneeeseaeesseneeees 20 3 gt Validaton MENOAOIOE Verres rren ar E ETE EE TEE 20 3 4 Ground data and tools used for validation cccccccsssececcsecceceesececceesecceeeeecceseuseceeseeeceeseees 21 3 5 Spatial interpolation for rain gauges sessssssssesressrerssrreresererssrrressrersserereseeressreresereresereeeseerese 24 3 6 Techniques to make observation comparable up scaling technique for radar data 24 3 6 1 Average of hi res ground validation data esesessseessssresssrrrsssrrressrrrsssreresrreresrrresserresees 25 3 6 2 Smoothing Of radar preciphallON anonsai i inai in aE ANa i Aa kN N A Nai
242. tion based only on radar network time resolution i Yes 5 minute Yes 5 minute timelines Instantaneous precipitation based on combined raingauge and radar i Yes 10 minutes Yes 5 minutes Yes 5 minutes network time resolution timelines Cumulative precipitation based only on raingauge network time intervals 5 min 1 3 6 12 18 24 hours timelines Input data Number of radars in network Number of precipitation stations 1300 475 Poland only TBD Yes min 5 min available Yes min 5 min available 1 3 6 12 24 hours 1 3 6 12 24 hours Cumulative precipitation based only on 5 min 1 3 6 12 18 24 hours Yes min 5 min available Yes min 5 min available radar network time intervals timelines ie 1 3 6 12 24 hours 1 3 6 12 24 hours Cumulative precipitation based on Yes min 10 minutes Yes min 5 min available Yes min 5 min available combined raingauge and radar 5 min 1 3 6 12 18 24 hours available in future 1 3 6 12 24 hours 1 3 6 12 24 hours Dates for selected case studies 29 3 2009 1 3 6 2010 20 6 2010 15 16 8 2010 Availability of own software for upscaling INCA data into native satellite grid Table 14 INCA Questionnaire 4 4 3 Some conclusions The INCA system as a potential tool for the precipitation products validation is available in Slovakia and Poland in both countries being run in pre operational mode It is still relatively new system undergoing continuous development More sophisticated algo
243. tion is planned to be implemented during year 2012 Also the attenuation correction the attenuation of electromagnetic waves in water environment water drops is planned for 2012 Hungary does not apply VPR Vertical Profile Reflectivity correction Precipitation intensity is derived from radar reflectivity with the help of an empirical formula the Marshall Palmer equation R a Z4b where a 200 b 1 6 From the three radar images a composite image over the territory of Hungary is derived every 15 minutes applying the maximum reflectivity in one column method in order to make adjustments in overlapping regions Description of instantaneous and accumulated radar product used in HSAF Validation Activities Rain gauge correction The non corrected precipitation field can be corrected by rain gauge measurements In Hungary we do not make corrections to instantaneous 15 minutes radar data In our institute we only use a correction for the total precipitation for 12 and 24 hour periods For the 3h and 6h accumulated products we use a special method to accumulate rainfalls we interpolate the 15 minutes measurements for 1 minute grid by the help of displacement vectors also measured by the radar and then sum up the images which we got after the interpolation It is more precise especially when we have storm cells on the radar picture because a storm cell moves a lot during 15 minutes and thus we do not get continuous precipitation fields when we sum up
244. tly however for the map created with the use of Natural Neighbour method the maximum and minimum values are less pronounced that on the other two maps Moreover application of Natural Neighbour method does not allow for extrapolating the distribution beyond the area defined by stations In order to evaluate the quality of the error distribution the cross validation was performed and the results are presented on the next figure Kriging Natural Neighbour Real mm Real mm Figure 119 Cross validation results obtained for three different methods for spatial interpolation For all methods the results are similarly scattered around the perfect estimation however for IDW 2 some underestimation was found for negative ME values The values of Mean Residual and Mean Absolute Residual defined as mean and mean absolute difference between Estimated and Real values of ME are presented in the next table The EUMETSAT Doc No SAF HSAF PVR 02 1 1 sotone Assatn HSA Product Validation Report PVR 02 Support to Operational yGrology and W Issue Revision Index 1 1 Y atacs kair aA Product H02 PR OBS 2 Date 30 09 2011 Page 170 177 Mean Residual Mean Absolute Residual Kriging Natural Neighbour IDW 2 Table 59 Mean Residual and Mean Absolute Residual values obtained for three algorithms for spatial interpolation using cross validation approach The lowest value of Mean Absolute Residual was found for Natu
245. tudy Comparison A first look to the results previous figures shows that rain rates detected by satellite product are in the same two areas of Germany as those indicated by the ground data Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 96 177 The EUMETSAT sotene Acai Product Validation Report PVR 02 Statistical score In the next two tables the results of the categorical statistic of the validation with both radar and rain gauge data are listed The results for validation with radar data for 5 6 December are worse than for the whole month December Probability Of Detection of precipitation RR gt 0 25 mmh was 0 16 with higher False Alarm Rate of 0 71 and Critical Success Index is 0 11 more worse than summer results 13082 610 311 1 Table 31 Results of the categorical statistic of the validation for whole December 2010 RRsat in class 1 MRRsatinclass2 RRsatinclass3 M RRsatin class 4 class 1 class 2 class 3 class 4 class 1 class 2 class 3 class 4 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 0 lt RR lt 0 25 0 25 lt RR lt 1 1 lt RR lt 10 RR gt 10 RR Radar RR Radar Figure 71 Contingency table statistic of Rain Rate mm h for H02 vs radar data Left for 5 6th December Right for whole December 2010 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product HO2 PR OBS 2 Date 30 09 2011 Page 97 177
246. uct but also of rain gauge and radar instruments This aspect has been highlighted also in Section 4 2 and 4 3 on ground data description Germany is the only country which has performed the validation using both radar and rain gauge data The results reported in tab 40 appear quite similar in particular for precipitation with rain rate lower than 10 mm h The difference for the heavy precipitation rain rate greater than 10 mm h is probably due to the small number of samples of this class A general precipitation underestimation by HO2 is reported in table 40 using both rain gauge and radar data for all the precipitation classes The winter average RMSE evaluated using radar data have been RMSE Cl1 131 CI2 92 Cl3 91 and using rain gauge RMSE Cl1 144 Cl2 110 Cl3 91 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 sac an Product HO2 PR OBS 2 Date 30 09 2011 Page 118 177 The i ee eo E H SAF Product Validation Report PVR 02 A small Mean Error and Mean Absolute errors have been obtained for medium precipitation rain rate between 1 10 mm h with radar ME 1 37 mm h MAE 1 5 mm h and rain gauge ME 1 73 mm h MAE 2 02 with a standard deviation respectively of 0 97 mm h and 1 83 mm h 6 2 2 The Spring period Cs 6 ee spring 2010 Version 2 2 radar radar radar radar radar_ gauge gauge gauge gauge gauge gauge _ 2 lt 1mm h lt 1mm h lt 1mm h lt 1mm h lt 1mm h
247. uct validation activities Table 2 HO1 has been submitted to validation in all these countries except Bulgaria Until now the Bulgarian data are used only for HO5 validation activity according to the Project Plan Their use in the next months is under consideration Figure 5 Structure of the Precipitation products validation team Validation team for precipitation products Siva Puca Leader Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 19 177 Product Validation Report PVR 02 National Institute of Meteorology and Hydrology Bulgarian Academy of Gergana Kozinarova Sciences NIMH BAS Bulgaria gkozinarova gmail com National Institute of Meteorology and Hydrology Bulgarian Academy of Georgy Koshinchanov Sciences NIMH BAS Bulgaria georgy koshinchanov meteo bg imatas E REN oa entanak stmusk J n Ka k SHM Slovakia jan kanak shmu sk tuvodavoton _ igumay EN oaia uvosiavotongshmuskt uboslav Okon SHM Slovakia luboslav okon shmu sk arin ursek E aE veka marian juraseashmust Mari n Jurasek SHM Slovakia marian jurasek shmu sk psig aney azeruroomisosr Table 2 List of the people involved in the validation of H SAF precipitation products The Precipitation products validation programme started with a first workshop in Rome 20 21 June 2006 soon after the H SAF Requirements Review 26 27 April 2006 The first activity was to
248. ucts validation to develop the code to be used in the PPVG for satellite products validation Activities First step collect e characteristics polarization beam width maximum range range resolution scan frequency geographical coordinates scan strategy elevations number of integrated samples etc of the radar networks which composes the PPVG e adopted processing chain Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 136 177 Product Validation Report PVR 02 e generated products including the quality map if any Start Time End time December 2010 February 2011 First Report 10 of Febrary 2011 Second step define on the base of published papers and studies of the characteristics of the radar data available inside the PPVG e minimal requirements for certifying the radar products quality e radar rainfall products testing e identification of the test bed scenario for satellite products validation Start Time End time January 2011 July 2011 Second Report 31 of March 2011 Final Report 31 of July 2011 Third step code possible Matlab realization for e comparison between radar data and satellite products on SSMI AMSU B and SEVIRI satellite grid Start Time End time June 2011 November 2011 Codes delivery and related documentation 30 of November 2011 Composition of the working group Coordinators Estezr Labo HMS Hun
249. ude The CAPPI is composed of data from several different angles that have measured reflectivity at the requested height of CAPPI product The PPVG group uses mostly CAPPI products for calculation of rainfall intensities except for Hungary which uses the CMAX data maximum radar reflectivity in each pixel column among all of the radar elevations for deriving rainfall intensities However the rest of the countries have also chosen different elevation angles for the CAPPI product which provides the basis for rain rate estimations Additionally we have to say that the countries apply different techniques of composition of radar data that were not specified in this questionnaire The composition technique is important in areas which are covered by more than one radar measurements Also the projection applied is varying from one country to the other To sum up the radar products used are not harmonized different techniques are applied However each of them is capable to grasp rainfall and to estimate rainfall intensity As for the accumulated products we see that Belgium uses 24 hourly accumulations with rain gauge correction Italy uses 3 6 12 24h accumulations without gauge correction in Hungary 3 6 12 24h data is used but only the 12h and 24 hourly accumulations are corrected by rain gauges in Poland and Slovakia no rain gauge correction is applied Poland has only 6 and 24 hourly data Turkey has 3 6 12 24h data and applies rain gauge
250. ulated at 13 00 UTC bottom panel of 06 July 2010 ee eececcceeeeesseeeeeseseeees 102 Figure 78 Synoptic chart at 1200 UTC on 15th of August 2010 ee eecccccsseceeeeesececeeeeceeeeeneeeseeees 103 Figure 79 Total lighting map of Poland showing electrical activity between 1145 and 1215 UTC on 15th O AVEUST 20 LO ccrccracceenecasacsncassaenesauuaaseessagaswaasaapeessassaauauamnenaasaceesasacaesaasaenseeansiesseanasenaasamarsasneseeieaecee a aeces 104 Figure 80 HO2 at 1208 UTC on the 15th of August 2010 right panel and 10 minute precipitation interpolated from RG data from 1210 UTC left panel eeccccccceesseccceeeeeseecceeeeeeeeeeseeeeaeeeeeseeenes 104 Figure 81 Scatter plot for measured RG and satellite derived H 02 rain rate obtained for all HO2 data on the 15 of 7A 4 0 1 7 0 EO eae A EE A A nm eo AA 105 Figure 82 Percentage distribution of HO2 precipitation classes in the rain classes defined using rain gauges RG data on the 15 of August 2010 c ccccecesecesecesesescsescscscscececaaacacscsvavecststatststatatatetetstseseess 106 Figure 83 Synoptic chart at 1200 UTC on 27th of September 2010 ce cecccccessseeeceeeeceeeeeeeceseeees 107 Figure 84 PR OBS 1 at 1251 UTC on the 27 of September 2010 right panel and 10 minute precipitation interpolated from RG data from 1250 UTC left panel oo eee ccceeeeccsseeeeesseeeeseseeees 107 Figure 85 Scatter plot for measured RG and satellite derived H 02 rain rate o
251. untry by country ground data description Section 4 5 4 13 The chapter has the object to provide ground data information and to highlight their error sources 4 2 Rain Gauge in PPVG In this section the complete inventory of the raingauges used in the PPVG with some considerations are reported as first results of the Rain gauge WG Annex 2 4 2 1 The networks The validation work carried on with raingauges uses about 3500 instruments across the 6 Countries Belgium Bulgaria Germany Italy Poland and Turkey as usual irregularly distributed over ground A key characteristic of such networks is the distance between each raingauge and the closest one averaged over all the instruments considered in the network and it is a measure of the raingauge density Instruments number and density are summarized in Table 8 The gauges density ranges between 7 for Bulgaria where only 3 river basins are considered to 27 km for Turkey These numbers should be compared with the decorrelation distance for precipitation patterns at mid latitude Usually the decorrelation distance is defined as the minimum distance between two measures to get the correlation coefficient Pearson Coefficient reduced to e A recent study on the H SAF hourly data for Italy shows this decorrelation distance varies from about 10 km in warm months where small scale convection dominates to 50 km in cold months when stratified and long lasting precipitation mostly occur In
252. up down scaling tecniques are reported in table below INTERPOLATION Step 150p peterreneyeneeeeees siege tans tare apoyen uunpuyensa een i PEEP PNA ee w p Barnes Kriging Nearest Neighbor Inverse Distance gt standard Deviation original field mm Figure 122 STD vs RMSE for interpolations by step 2 Taking into account the results discussed before is possible to define a range of uncertainty that is necessary to consider when comparing the results of validation with operational requirements More effort has to be done to understand if exist a link between the error of remap procedure and precipitation intensity but the preliminary study shows that in the best case an error of 30 has to be considered for the up down scaling remapping procedure Using the previous equation we can derive RMSEsat RMSD RMSEground RMSEcomparison where RMSD is provided by the validation activity RMSEground is provided by tab 64 using the University of Ferrara numbers At the moment for the RMSEcomparison is assumed equal to three values resulting from the ISAC study for validation w r t rain gauges and equal zero for validation w r t radar plus a 30 for the remapping procedure Conclusions 1 It is believed that the results of the validation activity cannot be substantially improved they are most probably consistent with the size of the error sources satellite ground stations and comparison method This needs to b
253. upscaling procedure over Belgium On the left the original images from Wideumont radar RMI Belgium and on the right the corresponding upscaled images The images appear correctly upscaled 24h radar precipitation accumulation mm Adjusted Radar Wideumont 31 01 2010 06 UT 01 02 2010 06 UT N_FILES 288 288 RMI Belgium AT R G 10 SIG_ADJ R G dB 0 00000 Figure 112 The Wideumont radar image of 1 2 2010 cumulated rainfall in the previous 24 hours raingauge adjusted TERTA i Figure 113 The Wideumont radar image of 1 2 2010 upscaled to the COSMO grid The EUMETSAT p d t V lid ti R t PVR 02 Doc No SAF HSAF PVR 02 1 1 te Application roquct Valldation Report a HSAF P Issue Revision Index 1 1 lo ond Waler Product H02 PR OBS 2 Date 30 09 2011 Page 165 177 24h radar precipitation accumulation mm Adjusted Radar Wideumont 01 02 2010 06 UT 02 02 2010 06 UT N_FILES 185 288 RMI Belgium NDAT R G 46 SIG_ADI R G dB 1 75967 2nd Order Polyn Fit 9 i Figure 114 The Wideumont radar image of 2 2 2010 cumulated rainfall in the previous 24 hours raingauge adjusted TERTA Figure 115 The Wideumont radar image of 2 2 2010 upscaled to the COSMO grid 24h radar precipitation accumulation mm Adjusted Radar Wideumont 03 02 2010 06 UT 04 02 2010 06 UT N_FILES 288 288 RMI Belgium NDAT R G 48 SIG ADJ R G dB 1 66052 2nd Order Polyn Fit y7 Figure 116 The Wideumont rad
254. us measurements without rain gauge adjustment Radar data are available within t5 minutes around the satellite passage Comparison Two representative examples of the comparison between HO2 and upscaled radar are given ieee eee SERS EEA Ayr DR I A ri ae ae a ke A Lie aeri A eee eee ne popas TEE ae CER D E E E Figure 54 H02 image of November 13th 2010 at 12 53 left compared with upscaled radar at 12 55 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 t f K A IS i i SEER FREER as lt ECR 8 ARE HA rh PTS PTH SET Pasay ES ere Rear pT a im AEE Ww AFA a Figure 55 H02 image of November 15th 2010 at 1 29 left compared with upscaled radar at 1 30 right The scale corresponds to thresholds of 0 1 1 and 10 mm h 1 The matching of the precipitation area is very week and only in one case a cell with rain rate greater than 1 mm h is detected Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 85 177 Product Validation Report PVR 02 Scores evaluation The statistical scores of the comparison between HO2 and upscaled radar data are given on the following table Sample 58 Mean error 0 60 Standard deviation 0 49 Mean absolute error 0 64 Multiplicative bias
255. usions The results for H02 were worse than for H01 the probability of detection was less the false alarm rate nearly same higher than POD All the quantitative precipitation amounts were underestimated Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 racy rate Product H02 PR OBS 2 Date 30 09 2011 Page 98 177 The shar eae eo Ie H SAF Product Validation Report PVR 02 5 4 Case study analysis in Hungary OMSZ 5 4 1 Case study July 18 2010 Description At Iceland a cyclone multiple center derives the weather of Europe Along the front lot of clouds with rain develope thunderstorms are also observed ID J R SI HELYZET 2010 07 18 00 UC SZTEREOGRAFIKUS VETULET A 60 ST LESSEGI FOKON HOSSZTARTO oliya Orsz gos i fr Meteorol giai aE http www methu AM Data used H SAF H02 2010 07 18 va 00 25 m A 4 aoe Sa Magyar kompozit Es Kompozit mm h 2010 07 18 va 00230 n a N c ae 3 re ee Doc No SAF HSAF PVR 02 1 1 Application HSAF roquc alldqation REPO hee i rata Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 99 177 Magyar kompozit Esi Kompozit mm h 2010 07 18 va 02 15 4 Fi 47 92 La 23 09 H SAF H02 Nincs adat H SAF HO2 2010 07 18 va 11 56 NA E Ti j i Magyar kompozit Esi Kompozit mm h 2010 07 18 va 12 00 4 aia s Ns 7 4 S p ow Fiz 45 P La 14 70 THu Est Kompozi
256. value about 300 mmh can be assumed for tipping bucket raingauges Most of these shortcomings could be avoided by using instruments based on different principle or mechanisms The German network and a part of the Bulgarian network as an example are equipped by precipitation weighting gauges that allow continuous precipitation both solid and liquid measurements with higher accuracy Other option could be the use of disdrometers that give more information about the precipitation structure and a more accurate rainrate measure In table 53 relevant characteristics of the raingauges used in the different countries are reported Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 131 177 Product Validation Report PVR 02 The networks The validation work carried on with raingauges uses about 3000 instruments across the 6 Countries as usual irregularly distributed over the ground A key characteristic of such networks is the distance between each raingauge and the closest one averaged over all the instruments considered in the network and it is a measure of the raingauge density Instruments number and density are summarized in table 55 The gauges density ranges between 7 for Bulgaria where only 3 river basins are considered to 27 km for Turkey These numbers should be compared with the decorrelation distance for precipitation patterns at mid latitude Usually the decorrelation
257. vel whereas in flat countries like Hungary or Belgium their height position is not exceeding 400m This information collected will be useful in the future steps of the Working Group to assess the partial or total beam shielding by mountains in the propagation way of the radar signals VVVVVV V All radars are C band radars working at frequency in C band at 5 6 GHz All radars are equipped by Doppler capacity which means that ground clutters can be removed from the radar data measurements effectively however not all of them have dual polarization which would be important to correct rain path attenuation The scan strategy for each of the radars used has been investigated In this matter all countries have shared their information on the number of elevations minimum and maximum elevations scan frequency maximum nominal range distance and range resolution Height Azimuth lt lt ART e Figure 14 Radar scan procedure In the PPVG the scan frequency ranges from 5 minutes in Belgium Germany and Slovakia to 10 minutes in Turkey and Poland and 15 minutes in Hungary and varying frequency for Italian radars The number of elevation stays between 4 and 15 in average around 10 Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 39 177 Product Validation Report PVR 02 The range distance used is 240 km in general But in some places in Italy and for the Turkish radars
258. velopment Is any quality NO in development map available National CAPPI 2 km Projection Mercator Resolution 1 km Threshold 31 5 dBZ No rain gauge correction composite Acc periods 3 6 12 24h National CAPPI 2 km Projection Mercator Resolution 1 km Threshold 31 5 dBZ No rain gauge correction composite Table 55 List of products used MAX PPI CAPPI VIL ETOPS EBASE RAIN Accumulation 1 3 6 12 24h Clutter Removal VPR Correction Z R A 200 b 1 6 CAPPI Projection Azimuthal Equidistant Resolution 250 m Threshold Rain Gauge Correction with limited number of gauges Acc periods 1 3 6 12 24h Projection Equidistant Resolution 250 m Threshold Rain gauge correction applied for 1h Rain Acc Azimuthal The EUMETSAT Doc No SAF HSAF PVR 02 1 1 sotene Aoi Product Validation Report PVR 02 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 143 177 11 Annex 4 Study on evaluation of radar measurements quality indicator with regards to terrain visibility Jan Ka k uboslav Okon SHMU For validation of H SAF precipitation products it is necessary to know errors distribution of used ground reference In this case precipitation intensity or accumulated precipitation measured by SHM radar network is considered as a ground reference To find distribution of errors in radar range next steps must be done e simulations of terrai
259. x 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 112 177 Product Validation Report PVR 02 UTC passage This is in agreement with the observed better consistency of precipitation intensities in the first passage However other scores such as Mean error do not confirm this observation The values of correlation coefficient are relatively high and reflect good spatial consistency of the compared fields Better values were obtained again for the 12 07 UTC passage Satellite passage viele PAO G Common passag NOAA18S NOAA19 093 0o96 f 094 0 24 016 Table 36 Scores for dichotomous statistics for precipitation threshold of 0 25 mm h The POD see Table 2 reaches values very close to 1 and the FAR values are on the other hand quite low This also supports the observed good spatial match between the radar and H02 fields Conclusions In this event of intense convective precipitation the HO2 product overestimated the precipitation as compared to radars especially in case of higher precipitation rates This is not in agreement with results of long term statistics for August 2010 showing slight underestimation of H02 The H02 overestimation was slightly lower in case of NOAA19 passage at 12 07 where the IFOVs were larger due to the longer distance from the satellite track compared to NOAA18 passage 1 min later Thus heavy precipitation in horizontally small convective cells could have been more smoothed by the Gauss filte
260. xt figures Sonntag 05 12 2010 Te IN wd ita ZX Figure 67 Synopsis for Central Europe a 05th December 2010 FU Berlin nie wkserv met fu berlin de Init Sat 04DEC2010 06Z Valid Wed O8DEC2010 00Z aasit bis zum Termin mm Daten GFS Modell des US Wetterdienstes C Wetterzentrale www wetterzentrale de Figure 68 96h totals of precipitation Der Wetterservice fur NRW und Deutschland R ckblick Starkniederschlage Hochwasser West Mitteleuropa 05 12 09 12 2010 Doc No SAF HSAF PVR 02 1 1 Product Validation Report PVR 02 Issue Revision Index 1 1 Network of Satellite Application Facilities Support to Operational hi Product H02 PR OBS 2 Date 30 09 2011 Page 95 177 10 12 14 6 10 12 1 Figure 69 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 12 05 02 29 UTC and for RADOLAN RW left filled raster 2010 12 05 02 50 UTC and station data right dots 2010 12 05 03 00 UTC 10 12 14 10 12 14 Figure 70 Hourly precipitation sum mm for H02 satellite data crosses time stamp 2010 12 06 02 18 UTC and for RADOLAN RW left filled raster 2010 12 06 02 50 UTC and station data right dots 2010 12 06 03 00 UTC Data used HO2 data for Bavaria in the given period were available for 5th December 1 23 2 29 11 51 12 20 and 12 56 UTC and for 6 December 2 18 12 09 and 13 50 UTC Only these data are analysed in this case s
261. y in 3 river basins only covering the western part of Anatolia 4 2 2 The instruments Most of the gauges used in the National networks by the PPVG Partners are of the tipping bucket type which is the most common device used worldwide to have continuous point like rainrate measurement Nevertheless several source of uncertainty in the measurements are well known but difficult to mitigate First very light rainrates 1 mm h and less can be incorrectly estimated due to the long time it takes the rain to fill the bucket Tokay et al 2003 On the other side high rainrates above 50 mm h are usually underestimated due to the loss of water during the tips of the buckets Duchon and Biddle 2010 Drifting wind can also greatly reduce the size of the effective catching area if rain does not fall vertically resulting in a rainrate underestimation quantitatively assessed in about 15 for an average event Duchon and Essenberg 2001 Further errors occur in case of solid precipitation snow or hail when frozen particles are collected by the funnel but not measured by the buckets resulting in a temporal shift of the measurements since the melting and the measure can take place several hours or days depending on the environmental conditions after the precipitation event Leitinger et al 2010 Sugiura et al 2003 This error can be mitigated by an heating system that melts the particles as soon as are collected by the funnel All these
262. y of own software for upscaling INCA data into native satellite grid Table 57 Questionnaire Case studies Several case studies comparing the INCA analyses with their source precipitation fields from radars and raingauges and with selected H SAF products have been elaborated at SHMU The precipitation fields from individual observations have been compared visually but have also been used as a ground reference for statistical analysis of the PR OBS 2 product during selected precipitation events Case study PR OBS 1 vs INCA 15 August 2010 15 00 UTC This is the first case study elaborated at SHMU which compares the PR OBS 1 product with precipitation fields produced by the INCA system In order to make precipitation fields from the microwave instruments and ground observations at 1 km resolution comparable the INCA precipitation fields have been upscaled into the PR OBS 1 native grid using the Gaussian averaging method Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 155 177 Product Validation Report PVR 02 Ellipses in next figure represent the satellite instrument IFOVs with colour corresponding to the upscaled radar rain gauge and INCA analysis rain rate value in case of next figure a b and c respectively or the satellite rain rate value in case of next figure part d As can be seen in next figure part b the rain gauge network captured intense precipitation
263. yes forecast no observed no Range 2 to 1 O indicates no skill Perfect score 1 Dry to Wet Ratio DWR false alarm correct negative _ observed no i Range 0 to oe Perfect score n a hits misses observed yes DWR Doc No SAF HSAF PVR 02 1 1 Issue Revision Index 1 1 Product H02 PR OBS 2 Date 30 09 2011 Page 31 177 Product Validation Report PVR 02 3 9 Case study analysis Each Institute in addition to the large statistic verification produces a case study analysis based on the knowledge and experience of the Institute itself Each institute following a standard format here reported decides whether to use ancillary data such as lightning data SEVIRI images the output of numerical weather prediction and nowcasting products The main sections of the standard format are e description of the meteorological event e comparison of ground data and satellite products e visualization of ancillary data e discussion of the satellite product performances e indications to Developers e indication on the ground data if requested availability into the H SAF project More details on case study analysis will be reported in the Chapter 5 3 10 Next steps On the base of the development phase it is possible to say that the ground data error characterization is necessary and that a validation of a common protocol is not enough Only the use of the same software can guarantee that the results obt

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