ASSESSMENT AND IMPROVEMENT OF STOCHASTIC WEATHER GENERATORS IN SIMULATING MAXIMUM AND MINIMUM TEMPERATURES

Stochastic weather generators are commonly used to generate time series of weather variables to drive agricultural and hydrologic models. One of their most appealing features is the ability to rapidly generate the very long time series used in agricultural and hydrological impact studies. However, they also have various problems, such as the inability to represent the interannual variability of the climate system, and it is difficult for them to accurately preserve the auto- and cross-correlation of maximum and minimum temperatures (T-max and T-min). This research aims to merge two widely used weather generators (CLIGEN (v5.22564) and WGEN) into a hybrid method that combines the strengths of each (referred to as the conditional method) for generating T-max and T-min and apply an approach to correct the interannual variability of T-max and T-min (referred to as the spectral correction method). The results show that CLIGEN reproduced mean daily T-max and T-min very well. WGEN also produced mean daily T-max reasonably well but slightly underestimated mean daily T-min. Moreover, CLIGEN was better than WGEN at producing standard deviations of daily T-max and T-min The conditional and spectral correction methods resulted in a weather generator that accurately produced means, standard deviations, and extremes of daily T-max and T-min. The auto- and cross-correlations of and between daily T-max and T-min were well reproduced and much better than those of CLIGEN- and WGEN-generated data. Moreover, the spectral correction approach successfully reproduced the observed interannual variability of T-max and T-min.