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LARS-WG 3.0 manual - Rothamsted Research

1. 2 MODEL DESCRIPTION LARS WG is based on the series weather generator described in Racsko et al 1991 It utilises semi empirical distributions for the lengths of wet and dry day series daily precipitation and daily solar radiation The semi empirical distribution Emp ao ai hi i 1 10 is a histogram with ten intervals a 1 a where a lt a and h denotes the number of events from the observed data in the i th interval Random values from the semi empirical distributions are chosen by first selecting one of the intervals using the proportion of events in each interval as the selection probability and then selecting a value within that interval from the uniform distribution Such a distribution is flexible and can approximate a wide variety of shapes by adjusting the intervals a a The cost of this flexibility however is that the distribution requires 21 parameters 11 values denoting the interval bounds and 10 values indicating the number of events within each interval to be specified compared with for example 3 parameters for the mixed exponential distribution used in an earlier version of the model to define the dry and wet day series Racsko et al 1991 The intervals a 1 a are chosen based on the expected properties of the weather variables For solar radiation the intervals a a are equally spaced between the minimum and maximum values of the observed data for the month whereas for the lengths of dry and w
2. 47 60 21 60 114 SERIES WET and DRY DJF 0 0 1 0 2 215 0 136 0 70 0 0 1 0 3 183 0 175 0 92 MAM 0 0 is 2 209 0 131 66 0 0 1 2 163 0 79 JJA 0 0 1 243 0 122 a a 4 For Help press F1 b Debrecen wg WordPad Iof x File Edit View Insert Format Help pelil Sia a He BI NAME Debrecen LAT LON and ALT 47 60 21 60 114 00 SERIES 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 6 0 10 0 13 0 78 0 46 0 27 0 14 0 4 0 4 0 4 0 1 0 1 0 2 0 0 0 1 0 2 0 3 0 4 0 5 0 7 0 10 0 14 0 19 0 25 0 60 0 44 0 26 0 8 0 11 0 16 0 10 0 5 0 2 0 3 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 TaD 8 0 9 0 10 0 59 0 40 0 20 0 14 0 8 0 1 0 2 0 0 0 1 0 1 0 0 0 1 0 3 0 6 0 10 0 15 0 22 0 31 0 42 0 55 0 70 0 56 0 41 0 28 0 14 0 4 0 4 0 0 0 0 0 0 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 10 0 70 0 51 0 18 0 15 0 1 0 0 0 1 0 2 0 0 0 0 0 0 0 1 0 2 0 3 0 4 0 6 0 9 0 13 0 18 0 24 0 31 0 54 0 25 0 16 0 18 0 17 0 16 0 6 0 2 0 3 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 10 0 56 0 40 0 22 0 10 0 8 0 6 0 5 0 0 0 0 0 0 0 mi For Help press F1 ee ee YY Figure 3 8 Examples of the parameter files produced by LARS WG in the Site Analysis process Both files are derived from observed weather data a the sta file which contains statistical characteristics of the observed weather data b the wg file which contains the parameters used by LARS WG to simulate artificial weather data o0 LARS WG Stochastic Weather Generator 10 So using debrece
3. 0 OoOnooonorognd For Help press F1 Box139 wg WordPad OF xi File Edit View Insert Format Help peal SIA al Hel I Cuain ooo i e a ae E AE TESEN AAE AAEE ee ee NAME Box139 LAT LON and ALT 52 5 0 0 99 0 SERIES o a H m Oo H N H or BB oO N o N N _ ee ooovonogand uw a ee OrororROMO J J Ho Oroo0o00o0cevoraoevdd ononooor oyoooOMoOHOD ovooowvoroowOo owvnoo0orao0OND OonoooroHOHDA For Help press F1 Figure 3 16 The wg parameter files obtained by calibrating LARS WG with daily GCM data from HadCM2 for the periods 1961 1990 top and 2035 2064 bottom The section of the files illustrated indicates the wet and dry series empirical distribution information which is used to calculate the relative change in wet and dry spell length LARS WG Stochastic Weather Generator 25 gs_box139 sce WordPad OF x File Edit View Insert Format Help pela all a lel BI Courier New x SES ESS BS Rain Wet NAME gsbox139 DATA Jan Feb Mar Apr May Jun 984 091 921 948 138 035 Jul 989 Aug 832 Sep 0 916 Oct 1 035 Nov 1 049 Dec 1 009 For Help press F1 o 1 o 0 1 1 o m POORPRRPRPHPHPOOR PRPRPHRPNFOROONEH oOobWUUD NUD AAT SF JNO Figure 3 18 The scenario file for Rothamsted indicating mean changes in precipitation mean temperature and solar radiation and relative changes in wet and
4. the observed standard deviations for each month are adjusted to give an estimated average daily standard deviation by removing the estimated effect of the changes in the mean within the month The adjustment is calculated using the fitted Fourier series already obtained for the mean The observed residuals obtained by removing the fitted mean value from the observed data are used to analyse a time autocorrelation for minimum and maximum temperatures For simplicity both of these are assumed to be constant through the whole year for both dry and wet days with the average value from the observed data being used Minimum and maximum temperature residuals have a pre set cross correlation of 0 6 Occasionally simulated minimum temperature is greater than simulated maximum temperature in which case the program replaces the minimum temperature by the maximum less 0 1 The analysis of daily solar radiation over many locations showed that the normal distribution for daily solar radiation commonly used in other weather generators is unsuitable for certain climates Chia and Hutchinson 1991 The distribution of solar radiation also varies significantly on wet and dry days Therefore separate semi empirical distributions were used to describe solar radiation on wet and dry days An autocorrelation coefficient was also calculated for solar radiation and assumed to be constant throughout the year Solar radiation is modelled independently of temperature LARS W
5. 2069 to represent the 2050s The simplest method is to use data from the grid box within which your station is located Although some GCMs do not have 365 days in the model year e g HadCM2 and HadCM3 have 12 months each of 30 days in length to give a year total of 360 days it is not necessary to represent these days as missing values LARS WG will automatically recognise that some days are absent and will use missing values to represent these days 2 Set up the st file which provides LARS WG with site and directory location information and the sr files which contain the model data Use the latitude and longitude values for the centre of the appropriate grid box as the site location Make sure you select SITE tags in the st file which will enable you to easily identify the parameter files for the two time periods You must make sure that the files are named differently otherwise the parameter files will be overwritten 3 Undertake Site Analysis using the daily global climate model data for both the baseline and future time periods This will result in two wg and two sta parameter files corresponding to the two time periods you have chosen 4 To incorporate variability into your scenarios you need to calculate the relative change in wet and dry series lengths Calculate the mean of the empirical distributions for wet and dry spell length from the baseline and future time period wg files To do this calculate the mid point value f
6. Interpolating mean rainfall using thin plate smoothing splines Inter J Geograph Inform Systems 9 385 403 Katz R W 1996 Use of conditional stochastic models to generate climate change scenarios Climatic Change 32 237 255 Racsko P Szeidl L amp Semenov M 1991 A serial approach to local stochastic weather models Ecological Modelling 57 27 41 Richardson C W 1981 Stochastic Simulation of Daily Precipitation Temperature and Solar Radiation Water Resources Research 17 182 190 Richardson C W amp Wright D A 1984 WGEN A model for generating daily weather variables US Department of Agriculture Agricultural Research Service ARS 8 USDA Washington DC Rietveld M R 1978 A new method for the estimating the regression coefficients in the formula relating solar radiation to sunshine Agricultural and Forest Meteorology 19 243 252 Semenov M A amp Barrow E M 1997 Use of a stochastic weather generator in the development of climate change scenarios Climatic Change 35 397 414 Semenov M A Brooks R J Barrow E M amp Richardson C W 1998 Comparison of the WGEN and LARS WG stochastic weather generators in diverse climates Climate Research 10 95 107 Semenov M A amp Brooks R J 1999 Spatial interpolation of the LARS WG stochastic weather generator in Great Britain Climate Research 11 137 148 5 ADDRESS FOR COMMUNICATIONS Dr Mikhail Semenov Rothamsted Research Harpenden Hertfordshire
7. Scaling value to 0 3 This is a quick and dirty way of generating scenarios for other time periods and it is not a method which should generally be used particularly since global climate model output is now available usually continuously from about 1900 to 2100 Using the default value of 1 means that no scaling is applied Select the Number of years you wish to generate by typing in the appropriate number in this window There is no limit to the number of years of data that LARS WG can generate the only constraint is the amount of disk space available 100 years of data requires approximately 1 5MB of LARS WG Stochastic Weather Generator disk space Finally you need to select the Random seed value The stochastic component of LARS WG is controlled by a random number seed There are a number of pre set random seeds available click on the arrow on the right hand side of this window to obtain a listing of these values It is advised that you use these pre set random seed values but if you wish to select your own random seed value then you should choose a prime number within the range of 500 to 1500 You can generate a number of different realisations of weather time series by selecting a different random seed value These realisations will all have the same statistical characteristics but they will differ on a day to day basis If you repeat a run with the same seed value then you will get exactly the same data as in the earlier
8. freedom the Chi squared test value and the probability that the observed and synthetic values come from the same probability distribution 2 RAIN distribution degrees of freedom Chi squared values and probabilities As 1 but for monthly precipitation amount 3 RAIN MONTHLY obs wg mean and sd t and F values and probabilities Block of eight lines of data following the header line Lines 1 and 2 indicate the monthly mean precipitation totals and standard deviations calculated from the observed data Monthly mean totals and standard deviations of the synthetic data are indicated in lines 3 and 4 Lines 5 and 6 illustrate the results of the t test which compares the mean values Line 5 is the calculated t value and line 6 is the associated p value i e the probability that the observed and synthetic mean values are derived from the same population Lines 7 and 8 indicate the results of the F test which is used to see if the observed and synthetic data are from normal distributions with the same variance Line 7 is the calculated F value and line 8 is the associated p value 4 MIN MONTHLY obs wg mean and sd t and F values and probabilities As 3 but for monthly minimum temperature 5 MIN DAILY obs wg mean and sd t and F values and probabilities As 3 but for daily minimum temperature 6 MAX MONTHLY obs wg mean and sd t and F values and probabilities As 3 but for monthly maximum temperature 7 MAX D
9. icon located in the c Program Files LARS WG 3 0 directory or on the butterfly icon on your desktop to start LARS WG see Section 0 1 for details of how to create a shortcut to LARS WG on the desktop Once LARS WG has been started and you have accepted the licence conditions the first small window which appears the main menu for the weather generator appears see Figure 3 1 The key functions of LARS WG are demonstrated using Debrecen Hungary as an example Data for this site LARS WG Stochastic Weather Generator 3 0 BBE Analysis Generator QTest rierpolation Options Help Exit Escher 1959 Press F1 for help at any time Figure 3 1 The LARS WG main window LARS WG Stochastic Weather Generator are included with the LARS WG software To quit LARS WG at any time simply click on the Exit tab on the main menu 3 1 Site Analysis The first step in the procedure for generating daily time series of weather data is SITE ANALYSIS In this process observed weather data for the site in question are analysed and two files are produced 1 a parameter file wg which contains the parameters required by LARS WG to generate synthetic weather time series and 2 a Statistics file sta containing the seasonal frequency distributions for wet and dry series length and for hot and cold spells which is used in the QTest process These files are automatically stored in the Sitebase directory a sub directory located unde
10. values simulated by LARS WG for January Changes in monthly precipitation length of wet and dry spells and temperature standard deviation are multiplicative i e they are expressed as ratios future baseline value and so a value of 1 indicates no change A value greater less than 1 indicates increases decreases in the relevant parameter LARS WG multiplies each value that it chooses for precipitation length of wet and dry series and temperature variability by the corresponding values in this file For example setting the wet series parameter for January equal to 1 5 will result in the lengths of each of the wet series chosen by LARS WG in January being multiplied by 1 5 Changes in precipitation mean temperature and solar radiation are at the monthly time scale whilst changes in wet and dry series length and temperature variability must be derived from daily data An example of a climate change scenario file is given in Figure 3 13 In this example changes in the average values of total monthly precipitation m rain mean temperature tem and radiation rad were calculated from changes in monthly precipitation mean temperature and solar radiation between the 1961 1990 baseline period and the 2035 2064 period of the HadCM2 greenhouse gas only climate change experiment For example the m rain value of 1 23 for January means that monthly precipitation in January for 2035 2064 is 1 23 times higher than monthly precipitation for 1961 1990 the t
11. 0 5 8 No Rad is likely incorrect in 1960 5 1 5 3 6 555 6 Temp is likely incorrect in 1966 251 19 3 16 9 1 2 16 7 Figure 3 6 Identification of errors in Temp is likely incorrect in observed weather data files by LARS WG 1977 60 5 7 3 7 8 6 5 a The Errors in input window which Tenp is likely incorrect in appears when possible errors are identified 1978 295 14 7 10 7 1 4 9 3 b An example of the Error txt file generated i Temp is likely incorrect in when running Site Analysis for Debrecen 1985 42 15 5 7 9 6 2 5 2 LARS WG lists the data values which are likely to be incorrect Successful completion of the Site Analysis process will result in the Success window see Figure 3 7 being displayed Simply click on OK to be returned to the main LARS WG screen If you look in the Sitebase directory under c Program Success Files LARS WG 3 0 or the relevant sub directory if you changed the location details using the Options facility then you That s all folks will see that two files have been created debrecen sta and debrecen wg The files are named according to the information given in the SITE tag in the st file These two files provide the statistical characteristics of the data and parameter information which will be used by LARS WG to synthesise artificial weather data in the GENERATOR process respectively You can open these files using for example WordPad and view their contents The layout of these fi
12. 0 tests will still be less than 0 05 even when there is no difference For example if a run of the generator was treated as the observed data and was tested using the QTest option on average 1 in 20 tests would give a p value less than 0 05 even though both sets of data do actually come from the same source Sample size also affects the likelihood of a significant p value The tests are more useful are more likely to give a significant result with more data A small sample size 1 e little observed data or a short run of simulated data gives little information as to the true distribution for a particular climate variable Reasons for significant differences Significant differences between simulated and observed data are likely to be due to LARS WG smoothing the observed data For example LARS WG fits smooth curves to the average daily mean values for minimum temperature and for maximum temperature It does this in order to eliminate as much as possible the random noise in the observed data in order to get closer to the actual climate for the site Differences are likely to be due to departures of the observed values from the smooth pattern for the data For example suppose that the observed monthly maximum temperatures for January to July for a site are Month Jan Feb Mar Apr May Jun Jul Maximum temperature C 1 4 10 16 13 24 26 In this case the mean maximum temperature for May does not follo
13. 3 1 3 5 9 1 6 0 0 2 6 1 4 9 9 O 1 0 0 4 5 1 5 4 0 Zee 0 0 Sal 1 6 9 6 0 7 0 0 Liev 1 7 9 8 260 0 0 Ta 1 8 1 9 5 8 O12 1 0 1 9 3 3 ted 0 0 6 6 1 10 1 0 3 6 0 4 2 9 1 11 7 4 4 8 0 0 4 0 1 12 14 1 1 5 0 0 4 6 1 13 19 5 4 7 0 3 3 7 1 14 11 5 0 5 2 8 5 8 1 15 10 6 0 9 0 9 2 3 1 16 4 0 0 5 0 0 3 8 1 17 2 2 a Ii 0 0 1 4 1 18 0 7 367 0 0 TeL 1 19 1 4 5 5 0 0 3 0 zi For Help press F1 Figure 3 14 An example of the file format of generated data The first two columns represent year number and day number respectively and the next four columns indicate minimum then maximum temperature precipitation and solar radiation respectively This format corresponds to that of the input file used in the calibration process LARS WG Stochastic Weather Generator 3 3 1 Creating climate scenarios from GCM output One of the main uses of stochastic weather generators is in the generation of daily weather data representing scenarios of climate change Most climate change scenarios are derived from the output of global climate models GCMs with changes in the different climate variables expressed on a monthly rather than a daily time scale The simplest method of applying climate change scenarios is to perturb an observed daily time series by monthly changes in the relevant climate variable For example if January temperatures are projected to increase by 2 C then all daily January values in the observed record would be increas
14. 77 v E 2 i gt Rand see Figure 3 9 The windows for the QTest options Test left and Compare right LARS WG Stochastic Weather Generator The Compare option allows comparison of the statistics of existing sta parameter files In the Compare statistics window see Figure 3 9 right you simply need to fill in the appropriate file names with the name of the observed sta file in the upper window and that of the sta file derived from simulated data in the lower window e g debrecen sta and debrecenWG sta respectively in this example Click on the graph icon to start the test and once it is completed a Success window will appear and you will be asked if you wish to view the results Click on Yes or No as desired The results are written to the tst file located in the Sitebase directory 3 2 1 Explanation of the tst file The tst file contains the results of comparing the statistical characteristics of the observed data with those of simulated data generated from the parameter files calculated from the observed data This file starts with the location information for the site in question The results of the tests are then given in the following order 1 SERIES Wet Dry degrees of freedom Chi squared values and probabilities The quarterly probability distributions for the length of wet and dry series are compared using the Chi squared goodness of fit test For each season the output is the number of degrees of
15. AILY obs wg mean and sd t and F values and probabilities As 3 but for daily maximum temperature 8 SPELLS FROST HOT degrees of freedom Chi squared values and probabilities As 1 but for hot maximum temperature gt 30 C and cold minimum temperature lt 0 C spells It is assumed that temperature is measured in degrees Celsius and not in degrees Fahrenheit 9 RAD MONTHLY obs wg mean and sd t and F values and probabilities As 3 but for monthly solar radiation 10 RAD DAILY obs wg mean and sd t and F values and probabilities As 3 but for daily solar radiation The Chi squared test is usually used to compare an empirical data histogram with a probability distribution function In this case the x test is used to compare two data histograms i e that derived using observed data with that derived using synthetic data The data are first divided into discrete LARS WG Stochastic Weather Generator 4 Debrecen tst Notepad ojx File Edit Search Help NAME Debrecen LAT LON and ALT 47 60 21 60 114 00 SERIES WET Dry degree of freedom chsq values and probabilities DJF 8 6 37 1 666 6 71 6 994 MAM 6 38 1 666 2 15 6 989 JJA 6 81 6 997 17 89 6 119 SON 6 34 1 666 2 69 6 978 RAIN distribution degree of freedom chsq values and probabilities 6 96 1 666 6 67 1 666 6 26 1 666 1 13 6 997 6 75 6 998 6 44 1 666 6 49 1 666 1 62 6 998 DAaAALALZDZ TNA Figure 3 10 An exam
16. ALS 2JQ UNITED KINGDOM Tel 44 0 1582 763133 ext 2395 E mail mikhail semenov bbsre ac uk Web site http www iacr bbsrc ac uk mas models larswg html 6 LICENCE AGREEMENT An individual academic can use LARS WG for research purposes The use of the LARS WG as a part of a research project requires a licence agreement LARS WG CANNOT be used for any commercial purposes
17. G accepts sunshine hours as an alternative to solar radiation data If solar radiation data are unavailable then LARS WG Stochastic Weather Generator sunshine hours may be used these are automatically converted to solar radiation using the approach described in Rietveld 1978 2 1 Outline of the stochastic weather generation process The process of generating synthetic weather data can be divided into three distinct steps 1 Model Calibration SITE ANALYSIS observed weather data are analysed to determine their statistical characteristics This information is stored in two parameter files 2 Model Validation QTEST the statistical characteristics of the observed and synthetic weather data are analysed to determine if there are any statistically significant differences 3 Generation of Synthetic Weather Data GENERATOR the parameter files derived from observed weather data during the model calibration process are used to generate synthetic weather data having the same statistical characteristics as the original observed data but differing on a day to day basis Synthetic data corresponding to a particular climate change scenario may also be generated by applying global climate model derived changes in precipitation temperature and solar radiation to the LARS WG parameter files The operation of LARS WG is now described in detail 3 KEY FUNCTIONS OF THE SOFTWARE Double click on the larswg exe file denoted by the butterfly
18. LARS WG A Stochastic Weather Generator for Use in Climate Impact Studies Developed by Mikhail A Semenov Version 3 0 User Manual Mikhail A Semenov and Elaine M Barrow August 2002 Rothamsted Research Harpenden Hertfordshire AL5 2JQ UK Canadian Climate Impacts Scenarios CCIS Project c o Environment Canada Prairie and Northern Region Atmospheric and Hydrologic Science Division 2365 Albert Street Room 300 Regina Saskatchewan S4P 4K1 Canada LARS WG Stochastic Weather Generator 1 Contents 0 TECHNICAL INFORMATION FOR USING LARS WG cccsccsseseseesseeseeeseeee 0 1 Set up and starting LARS WG cccsccsssssssessseesseesseessessseesseeesseeeseeeseeeseeel 2 M OD EL DESCRI PTION PITTI eee 4 2 1 Outline of the stochastic weather generation process eeeeeeeeeD 3 KEY FU NCTIONS OF TH E SOFTWARE Pee ere ECO Creer Cre CeCe eer er CLCR TCT ET rl Pe A J C tan A i 6 2k Sie Analys ea aa Creating climate scenarios from GCM output sseeeeeeeeeseeerererrreerererereesees 20 3 4 Spatial interpolation Of LARS WG cccecsseseeseeeseesseesseeseeseeeseeeeeeseeneeee ZO 4 REF EREN CES nnnnannnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn 26 LARS WG Stochastic Weather Generator 0 TECHNICAL INFORMATION FOR USING LARS WG LARS WG can be downloaded from _http Avww iacr bbsrc ac uk mas models larswg html Ple
19. RS WG This example is for Debrecen and is organised according to the FORMAT statement shown in Figure 3 4 i e Year Julian day mininum then maximum temperature precipitation and solar radiation wo JJa PWN He no _ REASON OD wo o n l ao ooo oco oO fFfoOKHFOCONWOMOrROO J For Help press F1 For best results observed weather data should be supplied for several years according to the following recommendations LARS WG will be able to simulate artificial weather data based on as little as a single year of observed weather data However since the simulated weather data will be based on these observed data then the more data used the closer is LARS WG likely to be able to match the true climate for the site in question The use of at least 20 30 years of daily weather data is recommended In order to be able to capture some of the less frequent climate events e g droughts as long an observed record as possible should be used The observed weather data can be contained in more than one file Simply list the directory path and file names in chronological order under the WEATHER FILES tag in the st file Fach line in the data record represents a single day and the values must be in the same order for each day according to the tags in the FORMAT statement in the st file The days must be in chronological order starting with the earliest record LARS WG Stochastic Weather Generator e The values for
20. ase register to use LARS WG by providing your name affiliation and address in the appropriate places on the web form By registering to use LARS WG you will receive information about any updates to LARS WG LARS WG is protected by a licence agreement It may be used free of charge with due acknowledgement to the model s developer for academic research purposes For use within a research project a licence is required LARS WG cannot be used for any commercial purposes LARS WG Version 3 0 is implemented in C with a full Windows interface It can be run on a PC with Windows 9x NT 2000 XP There are no special requirements for memory size or disk space note that 100 years of generated data takes 1 5Mb of disk space LARS WG requires little processing time for most situations 0 1 Set up and starting LARS WG Once you have downloaded the larswg exe file into the directory of your choice simply double click on the larswg exe file to activate the set up process Fill in the details of the directory to which you wish to unpack the set up files when prompted Double click on the setup exe file in this directory to install LARS WG onto your PC Follow the prompts for successful installation of LARS WG Unless you specify otherwise LARS WG will be installed into the c Program Files LARS WG 3 0 directory which will be created during the set up process The directions given in this user manual assume that the default settings have been used On
21. ce LARS WG has been installed the model is started by double clicking on the larswg exe file denoted by the butterfly icon located in the c Program Files LARS WG 3 0 directory To simplify this process a shortcut to this file can be created Simply right click with the mouse on the larswg exe file and select the Create Shortcut option A shortcut to this file will appear in this directory and can be moved to the desktop by selecting and dragging the file with the mouse After this has been done LARS WG can be started by double clicking on the butterfly icon on the desktop LARS WG Stochastic Weather Generator 1 INTRODUCTION LARS WG is a stochastic weather generator which can be used for the simulation of weather data at a single site Racsko et al 1991 Semenov et al 1998 Semenov amp Brooks 1999 under both current and future climate conditions These data are in the form of daily time series for a suite of climate variables namely precipitation mm maximum and minimum temperature C and solar radiation MJm day Stochastic weather generators were originally developed for two main purposes 1 To provide a means of simulating synthetic weather time series with statistical characteristics corresponding to the observed statistics at a site but which were long enough to be used in an assessment of risk in hydrological or agricultural applications 2 To provide a means of extending the simulation of weather time series t
22. dry spell length and temperature standard deviation LARS WG Stochastic Weather Generator 26 3 4 Spatial interpolation of LARS WG A methodology for the spatial interpolation of LARS WG parameters has also been developed for Great Britain Semenov amp Brooks 1998 and this option is described in this section although it is not activated in the version of the software currently available The INTERPOLATION option allows the LARS WG parameters to be interpolated to any location in Great Britain even where observed weather data are unavailable the resulting parameter file can then be used to generate synthetic weather data The Great Britain database had been derived from two data sets of observed weather i 138 sites in Great Britain with daily values of minimum and maximum temperature precipitation and radiation or sunshine hours over relatively long periods of between 20 and 40 years This data set was used to produce parameter files for all 138 sites ii a 1961 1990 database of mean monthly precipitation for 2376 stations and of minimum and maximum temperature for 623 stations This data set was used to calculate spline interpolation functions for monthly precipitation and minimum and maximum temperatures as a function of latitude longitude and elevation The spatial interpolation procedure of LARS WG combines global and local interpolation Similarity in the nature of the distributions of the weather variables for nearby sites is
23. e 2035 2064 data illustrated in Figure 3 16 bottom to obtain the average January wet spell length for this time period 19 01 The relative change in wet spell length in this month is then simply wet spello35 2964 wet spell 961 1990 19 01 20 68 i e 0 919 Follow the same procedure to calculate the relative change in wet and dry series length for each month 6 The daily mean temperature data for both time periods from the HadCM2 experiment were derived by averaging daily maximum and minimum temperature data from the same experiment For each month and time period all daily values were pooled and the mean and standard deviations calculated The change in monthly mean temperature was calculated by subtracting the monthly mean temperature values for 1961 1990 from the monthly mean values for the LARS WG Stochastic Weather Generator 23 2035 2064 period The relative change in temperature standard deviation was calculated simply by dividing the 2035 2064 standard deviation by that for 1961 1990 This process resulted in the values illustrated below Month J F M A M J J A S O N D Mean temperature 1 4 2 0 0 5 0 7 14 0 7 1 7 2 6 1 9 1 9 1 9 1 0 change C Change in 1 06 0 97 1 09 1 11 0 96 0 97 0 94 1 20 1 13 1 09 1 09 1 06 standard deviation 7 The monthly mean changes in precipitation and solar radiation may be obtained from the sta files s
24. each day should be separated by spaces or tabs The data should not contain blank lines comment lines or headers e Missing data values should be coded 99 0 Once the st and data files have been prepared then the SITE ANALYSIS process can continue Click on the icon indicating a graph second from the left in the Site Analysis window If LARS WG encounters illegal data during the Site Analysis process then it will display an error message see Figure 3 6 a Illegal data includes for example minimum temperature greater than maximum temperature and negative precipitation values You can opt to view the Error txt file which LARS WG automatically creates when possible errors are located by clicking on the Yes button in the Errors in input window which is displayed An example of some errors deliberately contained in the debr6090 sr data file is illustrated in Figure 3 6 b Viewing the Error txt file gives you the opportunity to go back and correct the data file if possible before running LARS WG If you choose not to view the Error txt file or you are unable to correct the errors then simply click on No to continue LARS WG will ignore the suspect data values it has identified i e it will treat them as missing values and they will not be included in the parameter calculation process a b File Edit Search Help Errors in input Precip is likely incorrect in Possible errors Do you want to read Error txt 1960 1 1 7 6 8 19
25. ean solar radiation and standard deviation These values are obtained by pooling the monthly mean solar radiation values 11 RAD DAILY max min N mean and sd Finally the statistical characteristics of daily solar radiation are provided maximum and minimum daily solar radiation the number of days of record N daily mean solar radiation and standard deviation These values are obtained by pooling the daily solar radiation values Weather generator parameters wg files e g debrecen wg The wg files contain the statistical parameters derived from the observed weather data and used by LARS WG to simulate synthetic weather data see Figure 3 8 b for an example This file also starts with the site name and location followed by the parameter information in the following order 1 SERIES Monthly histogram intervals and frequency of events in these intervals for wet and dry series length Each block of four lines one block for each month starting in January and ending in December represents wet first pair of lines and dry second pair of lines series information The first line in each pair corresponds to the histogram intervals whilst the second indicates the frequency of events within each interval 2 RAIN Histogram intervals and frequency of events in these intervals for precipitation amount by month from January to December The first line in each pair corresponds to the histogram intervals whilst the second indicates the fre
26. ed by 2 C i e the new perturbed time series will have exactly the same variability as the original but the January temperatures will be 2 C warmer A stochastic weather generator however allows the generation of synthetic daily weather data which will incorporate these changes but which will be different from the original time series on a day to day basis although the statistical characteristics will be almost identical It also allows changes in climate variability to be incorporated if desired and not just changes in mean values The following section outlines the procedure for constructing a scenario of climate change to be used with LARS WG to generate synthetic daily weather data If you wish to incorporate changes in climate variability then you will need to have access to daily GCM output If you are interested only in generating daily data from monthly changes in climate then you will need only scenarios of climate change at the monthly time scale The following process assumes that perturbations are being made to all four of the climate variables which LARS WG can generate if you are using a subset of these variables then obviously you need only climate change information for the relevant variables 1 Extract daily precipitation maximum and minimum temperature and solar radiation data from the appropriate global climate model experiment for the baseline period usually 1961 1990 and the relevant future time period e g 2040
27. ed to use the bs sce file see Figure 3 12 located in the Sitebase directory and provided with LARS WG The information contained in this file tells LARS WG that it should not apply any changes to the parameter values and so the synthetic data generated using the parameter files should have the same statistical characteristics as the observed data This bs sce file can be edited to create a scenario file containing values corresponding to a climate change scenario if required Click on the Edit scenario icon a piece of paper on which something is being written on the right hand side of the Generator window and the scenario file which is specified in the Scenario window see Figure 3 11 will be opened in Notepad to allow edits to be made Remember to change the NAME tag so that the output file will be named appropriately to describe the scenario you are using and remember to use the SaveAs option to save this information in a new file with the extension sce also named appropriately The bs sce file contains the following information see Figure 3 12 1 NAME This tag is used to name the output file containing the synthetic data In this example the output file will be called base sr Simply type in the name you wish to be used to identify the output file 2 DATA This block of data contains the information used to perturb the weather generator parameter files In column order from left to right we have name of month monthly precipitati
28. ee Figure 3 17 For precipitation scroll down to the block of data headed by the tag RAIN MONTHLY max min N mean sd in the baseline and future sta files and then calculate the relative change between the future and baseline periods For example from Figure 3 17 monthly total precipitation for January February and March in 1961 1990 is 69 8mm 56 3mm and 63 4mm respectively Corresponding values for the 2035 2064 period are 68 7mm 61 4mm and 58 4mm respectively Hence the relative changes in monthly precipitation for January February and March are 0 984 68 7 69 8 1 091 61 4 56 3 and 0 921 58 4 63 4 respectively For solar radiation simply scroll down to the block of data preceded by the tag RAD MONTHLY max min N mean and sd in each time period sta file and calculate the simple difference between the future and baseline periods From the results of 7 and above a scenario file was prepared for Rothamsted see Figure 3 18 This scenario file can be used to generate any number of years of data representing the climate of the 2050s LARS WG Stochastic Weather Generator 24 Box139 wg WordPad OF x File Edit View Insert Format Help Delal sle a ele B foen i a wf ale Ee AA E NAME Box139 LAT LON and ALT 52 5 0 0 99 0 SERIES Ga s p on H H O ONONO ror Ho omooo0oevrococraowad OoOnooonorROoOWwO oooo0oo0wvo 00 0 owooonooconad 0 4 0 1 0 1 0 2 0 7
29. em value of 1 80 for January means that mean temperature in January for 2035 2064 is 1 80 C warmer than that for 1961 1990 Changes for wet and dry spells and changes in temperature standard deviation were derived from daily HadCM2 model output Changing wet and dry spell lengths without making changes to any of the other variables in the parameter file will usually lead to changes in monthly mean precipitation amount mean temperature LARS WG Stochastic Weather Generator Cent GG35 64 sce Notepad of x File Edit Search Help Climate Change Scenario derived from HADCH2 experiment Baseline run 1961 1996 Perturbed run 2635 2664 GG greenhouse gases only GS greenhouse gase and sulfate m rain relative change in monthly mean rainfall wet dry relative change in duration of wet and dry spell tem and sd relative changes in daily temperature and absolute chan rad absolute changes in radiation Mj m2 day NAHE CentGG2635 64 DATA m rain wet dry tem sd rad 1 23 6 83 1 86 1 62 6 67 1 23 6 98 2 45 6 96 6 61 1 65 1 66 1 76 1 48 60 90 1 81 1 13 1 86 1 38 0 69 6 34 6 95 1 56 1 19 6 36 6 95 1 63 1 45 1 14 0 34 1 11 8 92 1 58 1 17 8 81 6 98 6 91 1 66 1 12 6 79 1 15 1 67 1 86 1 17 6 47 1 36 6 89 1 76 1 26 6 67 1 32 6 84 2 40 1 31 0 09 1 25 6 91 1 66 6 93 0 04 Figure 3 13 An example of a climate change scenario file The changes indicated in this file are derived from the HadCM2 greenhouse gas only experiment for
30. er file wg will be created in the Interpolation Result directory as specified in OPTIONS along with a int file which contains the monthly values for total precipitation minimum and maximum temperature predicted by the spline functions This file can then be used to generate daily weather series The Interpolation Data directory includes the database of about 150 parameters files for sites in Great Britain as well as parameters of spline functions for monthly total precipitation minimum and maximum temperature The support of Dr Mike Hutchinson Australian National University in providing the ANUSPLIN program and discussing interpolation issues is gratefully acknowledged 4 REFERENCES Bailey N T J 1964 The Elements of Stochastic Processes Wiley New York Chia E amp Hutchinson M F 1991 The beta distribution as a probability model for daily cloud duration Agric For Meterol 56 195 208 Downing T E Harrison P A Butterfield R E amp Lonsdale K G Eds 2000 Climate Change Climatic Variability and Agriculture in Europe An Integrated Assessment Environmental Change Institute University of Oxford LARS WG Stochastic Weather Generator Harrison P A Butterfield R E amp Downing T E Eds 1995 Climate Change and Agriculture in Europe Assessment of Impacts and Adaptations Environmental Change Unit Research Report No 9 Environmental Change Unit University of Oxford Hutchinson M F 1995
31. et series and for precipitation the interval size gradually increases as increases In the latter two cases there are typically many small values but also a few very large ones and this choice of interval structure prevents a very coarse resolution being used for the small values The simulation of precipitation occurrence is modelled as alternate wet and dry series where a wet day is defined to be a day with precipitation gt 0 0 mm The length of each series is chosen randomly from the wet or dry semi empirical distribution for the month in which the series starts In determining the distributions observed series are also allocated to the month in which they start For a wet day the precipitation value is generated from the semi empirical precipitation distribution for the particular month independent of the length of the wet series or the amount of precipitation on previous days Daily minimum and maximum temperatures are considered as stochastic processes with daily means and daily standard deviations conditioned on the wet or dry status of the day The technique used to simulate the process is very similar to that presented in Racsko et al 1991 The seasonal cycles of means and standard deviations are modelled by finite Fourier series of order 3 and the residuals are approximated by a normal distribution The Fourier series for the mean is fitted to the observed mean values for each month Before fitting the standard deviation Fourier series
32. expected since these sites will normally be subject to the same basic type of weather on each day However systematic differences can occur particularly if the sites are at significantly different elevations with precipitation tending to increase and temperature tending to decrease with elevation The interpolation procedure devised consists of an initial local interpolation in which the weighted average of the weather generator parameters for three neighbouring sites from the database are calculated The precipitation and temperature distributions of the target site were adjusted for the site elevation Precipitation elevation and temperature elevation relationships were obtained from global interpolation of monthly average precipitation and temperature by thin plate spline functions using elevation as an independent variable in addition to the geographical coordinates Hutchinson 1995 The parameters for precipitation and temperature at the target site were then adjusted based on the mean values predicted by the spline functions If the interpolated site coincides with one of existing sites from database the actual parameter file will be used e Run the INTERPOLATION option to interpolate LARS WG parameters to any given site in Great Britain specified in longitude latitude altitude coordinates Enter the name of the site its latitude and longitude in degrees and its altitude in metres in the dialogue box As a result the interpolated paramet
33. f the st file is as follows SITE the station name identifier e g Debrecen LAT LON and ALT latitude longitude and altitude for the site WEATHER FILES the directory path location and name of the file containing the observed weather data for the site FORMAT the format of the observed weather data in the file Here the DEBR6090 sr file contains information for the year YEAR Julian day JDAY i e from 1 to 365 or 366 minimum temperature MIN C maximum temperature MAX C precipitation RAIN mm and solar radiation RAD MJm day Other tags which can be used in the format line are DAY day of month MONTH month identifier from 1 January to 12 December and SUN sunshine hours If solar radiation is not available for a particular site then sunshine hours may be used instead the weather generator automatically converts sunshine hours to solar radiation using an algorithm based on that described in Rietveld 1978 LARS WG will work with precipitation data alone or with precipitation plus any combination of the other climate variables listed above END denoting the end of the file Debr6090 sr WordPad oO x File Edit View Insert Format Help Olea Sia a Hee Figure 3 5 An example of jigeo 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ao H oo the layout of a typical file of weather data for use in LA
34. iation derived from the observed weather data 7 AUTO MIN Average autocorrelation value for minimum temperature 8 AUTO MAX Average autocorrelation value for maximum temperature 9 AUTO RAD Average autocorrelation value for solar radiation Solar radiation is also modelled using empirical distributions based on frequency histograms Improved representation of solar radiation was obtained by modelling the solar radiation amounts separately for wet and dry days 10 WET RAD Solar radiation amount MJm day on wet days by month from January to December Each pair of lines indicates the histogram intervals first line and the frequency of events in each interval second line The histogram interval values are obtained from the observed weather data and are not pre set LARS WG Stochastic Weather Generator 11 DRY RAD Solar radiation amount MJm7day on dry days by month from January to December Each pair of lines indicates the histogram intervals first line and the frequency of events in each interval second line The histogram interval values are obtained from the observed weather data and are not pre set 3 2 QTest Once LARS WG has been calibrated using observed station data the next step in the process is to determine how well the model performs i e to assess the ability of LARS WG to simulate the climate at the chosen site in order to determine whether or not it is suitable for use in your appl
35. iation information The file containing the synthetic data is only temporary as soon as the parameter files have been generated the data file is deleted However there must be enough disk space on your PC to allow generation of this data file To run this option simply select the name of your site from the pull down menu insert the number of years of data you wish to generate if different from the default value of 100 years and finally select the random seed number you wish to use default is 577 as shown in the QTest window illustrated in Figure 3 9 left Then click on the graph icon and the test will be undertaken Once it is complete a Success window will appear and you will be asked if you wish to view the results Simply click on Yes or No as desired The results are written to the file with extension tst and named after your site e g for Debrecen the file is called Debrecen tst and is located in the Sitebase subdirectory The parameter files generated from the synthetic data are also housed in this subdirectory and they are distinguished from the original parameter files calculated from the observed station data by the WG in the filename For example the original parameter files for Debrecen are named Debrecen sta and Debrecen wg whilst those derived from the synthetic data are called DebrecenWG sta and DebrecenWG wg Site Debrecen x LE Observed se debrecen sta Num years 300 2 Simulated 2 debrecenwG i gt d 5
36. ication This can be done in two ways either i use the GENERATOR option to synthesise daily weather data based on the information in the site parameter files and then undertake comparisons between the observed and synthetic data off line or ii use the QTest option Here the QTest option will be described with details about the GENERATOR option following in Section 3 3 The QTest option carries out a statistical comparison of synthetic weather data generated using LARS WG with the parameters derived from observed weather data In order to ensure that the simulated data probability distributions are close to the true long term observed distributions for the site in question a large number of years of simulated weather data should be generated To start QTest click on the QTest button on the LARS WG main menu A drop down menu with two options Test or Compare will appear Both options compare the probability distributions for the synthetic and observed data using the Chi square goodness of fit test and the means and standard deviations using the t and F tests respectively with the results written to the tst file e g debrecen tst located in the Sitebase directory The Test option enables the user to generate synthetic data for any number of years default 300 based on the parameter files for the site in question The synthetic data are then analysed and parameter files produced containing probability distribution mean and standard dev
37. les is explained as follows Figure 3 7 The Success window which is displayed after successful completion of the Site Analysis process Statistical characteristics sta files e g debrecen sta The first few lines in this file give the site name and location LARS WG Stochastic Weather Generator information This is followed by the statistical characteristics of the observed weather data Figure 3 8 a illustrates an excerpt from the debrecen sta file Each block of output in this file is preceded by a header line describing its contents 1 SERIES WET AND DRY This block of output indicates the empirical distribution characteristics for the length of wet and dry series of days in the observed data This information is given in blocks of four lines by season i e winter DJF spring MAM summer JJA and autumn SON The first two lines of each seasonal block refer to the WET series whilst the last two lines represent the DRY series As explained in Section 2 the wet and dry series are modelled based on histograms constructed from the observed data The histograms consist of 10 intervals or bins and the cut off points for each bin are given in the first line of each set of two lines The second line corresponds to the number of events in the observed data falling into each interval a Debrecen sta WordPad Ioj x File Edit View Insert Format Help oele SI al Hae B NAME Debrecen LAT LON and ALT
38. month This can be derived from GCM daily mean temperature data or it may be necessary to determine daily mean temperature by averaging daily maximum and minimum temperature GCM output Pool the daily temperature values for each month and calculate the standard deviation so for example for a model which has 31 days in January and for which you are using 30 years of data you would have a total of 930 data values 30 years x 31 days from which you would calculate the standard deviation Do this for both the baseline and future time periods Calculate the relative change in temperature standard deviation by dividing the standard deviation for the future time period by the standard deviation for the baseline time period e g St dev 2940 2069 St dev 1961 1990 You also need mean changes in precipitation amount mean temperature and solar radiation for each month To calculate the monthly mean changes in precipitation amount you need to refer to the sta files generated using daily GCM data for the baseline and future time periods Go to the block of data in these files which has the header RAIN MONTHLY max min N mean sd and extract the values corresponding to mean precipitation amount 5 line below this header The relative change in monthly precipitation amount is simply the value for the future period divided by that for the baseline period e g precip amounty949 2960 precip amounto61 1990 Alternatively you may use corresponding monthly p
39. n sta it can be seen that the WET series intervals hj are O lt h lt 1 1 lt h lt 2 2 lt h3 lt 3 3 lt h lt 4 4 lt h lt 5 5 lt h lt 6 6 lt h7 lt 7 7 lt hg lt 8 8 lt ho lt 10 and 10 lt hj 9 lt 13 with corresponding frequencies of occurrence of 215 136 70 39 19 8 10 1 5 and 2 respectively see Figure 3 8 a Similarly the winter DRY series intervals are O lt h lt 1 1 lt h lt 3 3 lt h3 lt 6 6 lt h lt 10 10 lt h lt 15 15 lt he lt 22 22 lt h7 lt 31 31 lt hg lt 42 42 lt ho lt 55 and 55 lt hj9 lt 70 with corresponding occurrence frequencies of 183 175 92 43 9 8 1 0 0 and 1 respectively The histogram intervals are derived from the observed data and are not pre set Hence they will differ from site to site WET and DRY SERIES mean and sd The following block of data describes the mean and standard deviation by month of wet and dry series length The first two lines are the mean and standard deviation for the WET series followed by the same information for the DRY series The mean indicates the average length in days of the appropriate series in each month whilst the standard deviation gives an indication of the variability of the series length in each month DISTRIBUTIONS OF RAIN Precipitation amount is modelled in the same way as series length i e empirical distributions are derived using frequency histograms the intervals of which are based on the observed weather data An empirical precipitation amoun
40. o unobserved locations through the interpolation of the weather generator parameters obtained from running the models at neighbouring sites It is worth noting that a stochastic weather generator is not a predictive tool that can be used in weather forecasting but is simply a means of generating time series of synthetic weather statistically identical to the observations New interest in local stochastic weather simulation has arisen as a result of climate change studies At present output from global climate models GCMs is of insufficient spatial and temporal resolution and reliability to be used directly in impact models A stochastic weather generator however can serve as a computationally inexpensive tool to produce multiple year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability Semenov amp Barrow 1997 The first version of the LARS WG weather generator was developed in Budapest in 1990 as part of Assessment of Agricultural Risk in Hungary a project funded by the Hungarian Academy of Sciences Racsko et al 1991 The main focus of this work was to overcome the limitations of the Markov chain model of precipitation occurrence Bailey 1964 Richardson 1981 This widely used method of modelling precipitation occurrence which generally considers two precipitation states wet or dry and considers conditions on the previous day only is not always able to correc
41. on change m rain changes in the length of the wet series wet changes in the length of the dry series dry followed by changes in mean temperature tem in the standard deviation of temperature sd and in mean radiation rad respectively LARS WG uses the information in the DATA block in the following manner The changes in mean temperature and solar radiation are additive changes i e a zero indicates that no change is to be applied The mean temperature change value for a given month is applied to both the minimum and LARS WG Stochastic Weather Generator 17 Fi Bs sce Notepad Jol x File Edit Search Help This is an example of scenario file for the baseline climate m rain relative change in monthly mean rainfall wet dry relative change in duration of wet and dry spell tem and sd relative changes in daily temperature and absolute chan rad absolute changes in radiation Mj m2 day NAME base oDoooooonoooooo h h h h b d d h d d hk ooooooonoooooono 4 1 4 4 4 4 4 41 4 4 4 4 h h h b b mb mb mb mb b d b h h h h b h b d d Md Md Md Figure 3 12 The bs sce file used to generate synthetic weather data with the same statistical characteristics as the observed weather data used to calibrate LARS WG maximum temperature values for that month For example if the tem parameter is set to 1 5 for January then 1 5 C will be added to each of the daily minimum and maximum temperature
42. or each histogram bin by averaging the bin boundary values Multiply this mid point value by the number of events in this bin to obtain an average number of days in this category Do this for each histogram bin and sum these values together to get an approximation for the total number of wet or dry days Calculate the total number of events by simply adding together the number of events in each bin To calculate the mean value for the distribution divide the sum of the wet or dry days by the total number of events For the January wet series indicated in Figure 3 8 b for Debrecen wg this would result in the following values The mid point values are 0 5 1 5 2 5 3 5 4 5 5 5 6 5 7 5 9 0 and 11 5 Average number of days in each bin 39 0 5x78 69 1 5x46 67 5 49 18 22 26 7 5 9 and LARS WG Stochastic Weather Generator 23 totalling 330 The total number of events is 181 The average length of a January wet spell is therefore 1 82 days 330 181 Do this for wet and dry series for each month and for the baseline and future time periods To calculate the relative change in length of wet or dry series divide the average length of the series in the future time period by the average length of the series in the baseline time period e g length2040 2069 length 961 1990 You now have relative changes for each series for each month 5 You also need to calculate the relative change in mean temperature standard deviation for each
43. ple of the output from the QTest Test or Compare options Here the Debrecen tst file is illustrated classes or bins and the test statistic is then calculated by counting the data values falling into each class in relation to the computed theoretical probabilities which in this case are calculated from the observed data In each class the number of data values expected to occur according to the fitted distribution i e the observed data is simply the probability of occurrence in that class multiplied by the sample size n If the synthetic data are very close to the observed data the expected and observed counts will be very close for each class and the squared differences in the above equation will be very small yielding a small x If the fit is not good at least a few of the classes will exhibit large discrepancies Meaning of the p value and interpretation of output statistics The X t and F tests assume that the observed weather is a random sample from some existing distribution which represents the true climate at the site In the absence of any changes in climate this true distribution could be estimated accurately from observed data over a very long time period The simulated climate distribution is estimated from a long run of synthetic weather data generated by LARS WG using the parameter files output during the model calibration process in principle this distribution could be determined for any given site from the paramete
44. quency of events in each interval Temperature is modelled in LARS WG by using Fourier series i e the annual cycle of temperature is described using sine and cosine curves These curves can be constructed with information pertaining to only a small number of parameters i e the mean value amplitude of the sine cosine curves and phase angle Both maximum and minimum temperature are modelled more accurately by considering wet and dry days separately 3 WET MIN First two lines are four Fourier coefficients a i and b i i 1 4 for the means of minimum temperature on wet days and second two lines are Fourier coefficients for standard deviations of minimum temperature on wet days 4 WET MAX Fourier coefficients for the means and standard deviations of maximum temperature on wet days 5 DRY MIN Fourier coefficients for the means and standard deviations of minimum temperature on dry days 6 DRY MAX Fourier coefficients for the means and standard deviations of maximum temperature on dry days The weather on a given day is related to some extent by what has happened on the previous day e g if the previous day was hot then storage of heat energy in the soil etc and release of this energy over time means that it is likely that the next day will be warm as well This dependence on the previous day s weather is known as autocorrelation LARS WG uses an average autocorrelation value for minimum and maximum temperature and solar rad
45. r c Program Files LARS WG 3 0 If you wish to create a different directory and save the files there instead then you will need to change the locations using the OPTIONS facility Click on the Options es Lars wa 3 O Sitebase button at the top of the LARS WG main menu and Site Analysis he the window illustrated in Figure 3 2 will appear Generator Jes Lars wg 3 0 Sitebase 7 Check that the directory locations indicated in the f les Lars wg 3 0 UKcase I SITE ANALYSIS and GENERATOR windows Interpolation Dats are correct If it is necessary to amend these details Interpolation Result s Larswo 2 0 Interbase then simply click in the appropriate window and type in the relevant information To save these new Figure 3 2 The Options window for details and return to the main menu click on the red changing directory location specifications tick icon on the right hand side of this window if the details are correct then simply click on the hand icon to exit this window and return to the main menu To start the SITE ANALYSIS process click on the Analysis button on the LARS WG main menu The window indicated in Figure 3 3 will appear Here LARS WG requires details regarding the directory location and name of the file containing the site information Illustrated in Figure 3 3 are the details for Debrecen To change this information you i 3 O debrdebr st can either click in the window and type in the site gt appropriate details or yo
46. r file for the site although the calculation is difficult because of complex interactions between the parameters The statistical tests carried out in QTest look for differences between the simulated climate and the true climate Each of the tests considers a particular weather statistic and compares the values from the observed and simulated data All of the tests calculate a p value which is used to accept or reject the hypotheses that the two sets of data could have come from the same distribution i e there LARS WG Stochastic Weather Generator is no difference between the true and simulated climate for that variable Therefore a very low p value means that the simulated climate is unlikely to be the same as the true climate If the p value is not very low it is plausible that the climates are the same although statistical tests cannot prove this Particular weather variables for which the test process exhibits very low p values should therefore be investigated see reasons for differences below The level of p value to consider significant is subjective and depends on the importance of a very close fit for your application However it is suggested that a p value of less than 0 01 be taken as indicating the likelihood of a substantial difference between the true and simulated climate for that particular variable Although the 0 05 value is a common significance level used in statistical tests on average in 2
47. recipitation scenario values i e the relative change in monthly precipitation between the future and baseline periods since these should be identical to the changes derived from the daily precipitation data For monthly mean changes in mean temperature and solar radiation you need the monthly mean change in these values between the future and baseline periods For solar radiation these values may also be derived from the sta files Simply scroll down to the block of data headed by the RAD MONTHLY max min N mean and sd tag in each sta file and calculate the difference between the future and baseline periods Remember that LARS WG requires the solar radiation data to be in units of MJm day Most GCM output is in units of Wm so you will need to convert the data into MJm day by multiplying the Wm values by 0 0864 You now have the information required to construct the climate change scenario file and thus to generate daily weather data representing future scenario conditions A worked example of creating a climate change scenario using Rothamsted UK as an example is illustrated below 1 Daily weather data for Rothamsted for the period 1961 1990 were converted into the correct format for use in LARS WG and the model calibrated using the Analysis option Thirty years of synthetic weather data were then generated using the Generator option Daily maximum and minimum temperature and precipitation data were extracted from
48. run However if you change the seed value then the data will be different on a day to day basis although the statistical characteristics will be the same Remember to change the NAME tag in the sce file you are using in order to distinguish between the output data files produced using different random number seeds if you do not do this then the output file will simply be overwritten Different weather sequences may have different effects in your application and so as with any stochastic modelling process the application may need to be run several times with the different weather sequences The longer the time period of simulated weather that is used the more it will cover the full range of possible weather events Long weather sequences are usually required when assessing risk Figure 3 14 illustrates the format of a typical file of synthetic weather data generated by LARS WG The first two columns are the year number and the day number respectively The format of the rest of the file will mirror the format of the input file For example if your observed weather data file was in the order MAX MIN RAIN RAD then this will be the format of the synthetic weather data generated in the output file If you have used SUN as an input climate variable it is converted to solar radiation and RAD will be output FA base sr WordPad O x File Edit View Insert Format Help Delt SI al 1 1 5 2 0 6 0 0 5 5 1 2 3 5 2 9 0 0 6
49. st two lines represent the extremes of daily maximum temperature i e the absolute maximum and minimum daily maximum temperature values respectively N is the number of days in the record i e the number of days in the relevant month multiplied by the number of years of record and this is followed by the daily mean maximum temperature and standard deviation i e the day to day variation for the month in question MIN MONTHLY max min N mean and sd As 5 but for monthly mean minimum temperature MIN DAILY max min N mean and sd As 6 but for daily minimum temperature SPELLS OF FROST and HOT TEMPERATURE Periods of cool and warm weather are also modelled using empirical distributions by season A frost is defined as a minimum temperature less than 0 C whilst a hot day occurs if maximum temperature exceeds 30 C Each seasonal block of data consists of four lines with the first line of each pair describing the histogram intervals spell length and the second line the frequency of occurrence of events within each interval respectively The first two lines represent frost events whilst the last two lines relate to hot spells LARS WG Stochastic Weather Generator 11 10 RAD MONTHLY max min N mean and sd Statistical characteristics of monthly mean solar radiation MJm7day are given First of all the maximum and minimum monthly mean solar radiation values followed by the number of years of record N monthly m
50. t distribution is derived for each month resulting in the 24 lines in this block listed from January through to December Each pair of lines represents the histogram intervals followed by the frequency of precipitation occurrence within each interval RAIN MONTHLY max min N mean and sd Following the precipitation distribution characteristics are summary precipitation statistics by month The first two lines represent the absolute maximum and minimum precipitation totals mm recorded in each month The next line indicates the number of years of data in the record N 31 for the Debrecen example followed by monthly mean precipitation total and standard deviation MAX MONTHLY max min N mean and sd Next are a number of statistics related to monthly mean maximum temperature arranged as in 4 above These are derived by pooling the mean maximum temperature for each month and year The first two lines represent the extremes of monthly mean maximum temperature i e the absolute maximum and minimum monthly mean maximum temperature values respectively N is the number of years of record followed by the monthly mean maximum temperature and standard deviation i e the year to year variation for the month in question MAX DAILY max min N mean and sd LARS WG also provides information about the statistical characteristics of daily maximum temperature derived by pooling the daily maximum temperature values for each month and year The fir
51. the SITE tags are the same for the 1961 1990 and 2035 2064 time periods the parameter files for each time period were moved to different directory locations immediately after their generation and so they were not overwritten 4 Site Analysis was undertaken using the daily HadCM2 data for the two time periods 1961 1990 and 2035 2064 and wg and sta files produced 5 The relative changes in wet and dry spell length were calculated from the empirical distributions described in the wg parameter files see Figure 3 16 As an example here we will calculate the relative change in January wet spell length If you look at the box139 wg file corresponding to the 1961 1990 HadCM2 data Figure 3 16 top then you would calculate the mean wet spell length in January in the following manner First of all calculate the mid point values for the histogram bins by averaging adjacent bin boundary values In this example the following values result 0 5 2 0 4 5 8 0 13 0 20 0 29 0 40 0 52 0 and 68 0 To obtain the average number of wet days in each category multiply the mid point values by the number of events in that category Here the results are 1 5 0 5x3 16 0 2 0x8 63 0 4 514 88 0 208 0 160 0 261 0 360 0 364 0 340 0 Sum these values and then divide the result by the total number of events 1861 5 90 to obtain the average January wet spell length for the 1961 1990 HadCM2 daily data 20 68 Follow exactly the same procedure for th
52. the greenhouse gas sulphate aerosol climate change experiment undertaken with the HadCM2 GCM for the grid box within which Rothamsted is located referred to as Box 139 Thirty years of data were extracted for two time periods 1961 1990 baseline and 2035 2064 to represent 2050 Two data files sr were prepared in the correct format for input into LARS WG LARS WG Stochastic Weather Generator B box139 st WordPad B box139_2035 2064 st WordPad File Edit View Insert Format Help File Edit View Insert Format Help oela Sia al elel S olele ale al lel S Courier new ft Courier new ft B SITE Box1i39 LAT LON and ALT 52 5 0 0 99 Full path to weather files one per line f Weather files and records in each file should be in chronological order WEATHER FILES e larswg hegs HCGS_2035 2064 box139 sr SITE Box139 LAT LON and ALT 52 5 0 0 99 Full path to weather files one per line Weather files and records in each file f should be in chronological order WEATHER FILES e larswg hegs HCGS_1961 1990 box139 sr FORMAT FORMAT YEAR JDAY MIN MAX RAIN YEAR JDAY MIN MAX RAIN END END Figure 3 15 Examples of the st files used with the HadCM2 daily data Left for 1961 1990 Right for 2035 2064 Note Figures cropped to remove blank space 3 Figure 3 15 illustrates examples of the st files used with the HadCM2 daily data and LARS WG Note that although
53. the years 2035 2064 i e centred on 2050 calculated with respect to the 1961 1990 baseline period and solar radiation since these values are conditioned on the precipitation status of the day in question This unexpected response to changing the precipitation parameters has also been observed in stochastic weather generators based on Markov chain processes Katz 1996 LARS WG performs self adjustment of precipitation amount temperature and solar radiation in order to keep the changes specified for these variables as close as possible to those given in the scenario file The adjustment factors are based on the generation of a large number of years i e 1000 of data and so the changes in the different climate variables will only approach the values in the scenario file if a large amount of data is generated Once you have selected your site and set up the appropriate scenario files there are three further options to be completed The first is the Scaling factor This factor can be used if you have implemented a climate change scenario for a particular future time period say 2100 and you want to obtain data for an earlier time period without having to create a scenario file for this earlier time period The Scaling factor allows you to do this by assuming that the changes in climate over time are linear So for example if you have already created a scenario file for 2100 and you wish to obtain data for 2030 using this file then you would set the
54. tly simulate the maximum dry spell length which is crucial for a realistic assessment of agricultural production in some regions of the world Hungary included This resulted in the new series approach in which the simulation of dry and wet spell length is the first step in the weather generation process A modified version of this weather generator now called LARS WG Long Ashton Research Station Weather Generator the location at which it was developed in its current form was used in the construction of the climate change scenarios used in two major European Union funded research projects examining the impacts of climate change on agricultural potential in Europe i e CLAIRE Harrison et al 1995 and CLIVARA Downing et al 2000 Further details of these high resolution climate change scenarios may be found in Semenov and Barrow 1997 The most recent version of LARS WG version 3 0 for Windows 9x NT 2000 XP has undergone a complete redevelopment in order to produce a robust model capable of generating synthetic weather data for a wide range of climates LARS WG has been compared with another widely used stochastic weather generator which uses the Markov chain approach WGEN Richardson 1981 Richardson and Wright 1984 at a number of sites representing diverse climates and has been shown to perform at least as well as if not better than WGEN at each of these sites Semenov et al 1998 LARS WG Stochastic Weather Generator
55. tor 3 3 Generator Once LARS WG has been calibrated using observed weather data for the site in question Analysis and the performance of the weather generator has been verified QTest synthetic weather data may be simulated using the Generator option This option may be used to generate synthetic data which have the same statistical characteristics as the observed weather data or to generate synthetic weather data corresponding to a scenario of climate change Generator Site oo Figure 3 11 The Generator window Scenario C Program Fil indicating the options available for a generating synthetic weather data Scaling 1 Num years 2 ui Rand seed 677 2 To generate synthetic weather data click on the Generator tab on the LARS WG main menu The window illustrated in Figure 3 11 will appear Fill in the details for your site as necessary Specify the site name in the Site window click on the arrow on the right hand side of this window to obtain a listing of the sites at which LARS WG has been calibrated and highlight the name of your station Debrecen is the default then select the appropriate Scenario file the Scaling factor the Number of years to be generated and the Random seed value LARS WG uses a Scenario file to determine how the weather generator parameter values should be perturbed If you wish to generate synthetic weather data based on the parameters derived from observed data then you will ne
56. u can edit the Debrecen site information file debr st to create a new file containing the information for another site To do this click on the left hand icon illustrating a piece of paper Figure 3 3 The Site Analysis window on which something is being written The debr st file which specifies the location and name of is then opened using Notepad sce Figure 3 4 the file containing the site information This file contains information about the site Ele Edt Search Help name and location the directory path and name of SITE the file containing the weather data for the site Debrecen p g LAT LON and ALT followed by a number of tags denoting the Laenen FILES eee TR organisation of the data in the weather file To c Program Files Lars wg 3 6 debr debr6696 sr create a new st file for your site simply edit this FORMAT GEAR IPAP MIN dae FAIN AD information appropriately and then save as a new END file use the Save As option on the File menu in Notepad make sure that the file is located in the appropriate directory If yo ish to add an Figure 3 4 The debr st file containing neay ry ae 2 site information for Debrecen Notepad window cropped to reduce blank space LARS WG Stochastic Weather Generator explanatory notes to the st file you can use at the beginning of lines containing comments the will result in these lines being ignored by the model The layout o
57. w the expected trend of increasing temperatures during the first half of the year and so there is likely to be a significant difference between the simulated and observed data for May Possible reasons for such data and the appropriate responses are e Errors in the observed data Correct the errors and re run LARS WG e Random variation in the observed data In the above example May could have been unusually cold in the years covered by the observed data and so the data would not be typical of the true climate at that site Random variations from month to month are likely to be greater when there is less observed data If the differences are due to such random variations the smoothing employed by LARS WG will mean that the simulated weather is likely to be closer to the actual climate for the site than the observed data and so the simulated data can be accepted LARS WG assumes that the observed climate is stationary if there are any trends in the observed data then these need to be removed before LARS WG is used e Climate anomalies The variations in the data may be due to some unusual climatic phenomenon and so the data may actually be typical of the climate for the site It is likely that in this case LARS WG will not match the climate for that part of the year In this case careful consideration is needed of the effect on your application of the differences between LARS WG and the typical climate LARS WG Stochastic Weather Genera

LARS-WG 3.0 manual - Rothamsted Research

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