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CR 24:215-230 (2003)  -  doi:10.3354/cr024215

A statistical comparison of the stochastic weather generators WGEN and SIMMETEO

Afshin Soltani1,*, Gerrit Hoogenboom2

1Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences, PO Box 386, Gorgan, Iran
2Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia 30223, USA

ABSTRACT: Weather generators are frequently used to provide weather data when the length of historical weather data is inadequate or when future data are required. The main objective of this study was to evaluate the weather generators WGEN and SIMMETEO as implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) for 5 Iranian locations with contrasting climates. The algorithms for generating weather data are the same in both programs, but they differ with respect to the methods used for parameter estimation. While WGEN requires daily weather data for parameter estimation, SIMMETEO uses monthly summaries. Therefore, the second objective of this study was to evaluate the effect of the parameter-estimation method on the quality of generated weather data. Extensive statistical evaluations, including t-, F- and Kolmogorov-Smirnov tests, were conducted to analyze the differences between observed and generated weather data as well as between generated series. The results showed that the algorithms used to generate precipitation work well. For solar radiation, WGEN showed a poor and SIMMETEO a good performance. WGEN was successful in reproducing maximum and minimum temperatures, and SIMMETEO in reproducing minimum temperature. However, SIMMETEO showed a moderate performance for maximum temperature. SIMMETEO did not generate extreme temperatures well, but WGEN showed a good performance for generating the number of frost days and a moderate performance for the number of hot days. It can be concluded that, when daily weather data are available, WGEN should be preferred for generating weather data. However, when only monthly summaries are available or when resources are limited for preparation of daily weather data, SIMMETEO should be used. It was also found that series generated with identical parameters but different Œseeds¹ for initialization may be significantly different from each other. More research is, however, needed on this aspect of weather generators.

KEY WORDS: Weather generator · Stochastic model · Temperature · Precipitation · Solar radiation · DSSAT

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