Assessment and improvement of CLIGEN non-precipitation parameters for the Loess Plateau of Chinan

Stochastic weather generators are often used to generate daily weather input for hydrologic and crop models. The objective of this study was to evaluate and improve the ability of the Climate Generator (CLIGEN v5.22564) model to generate non precipitation parameters, including dewpoint temperature(T-dp), daily maximum (T-max) and minimum (T-min) air temperatures, solar radiation (SR), and wind velocity (u) at 12 meteorological stations located in the Loess Plateau of China. We used daily weather data to evaluate the model and to improve SR and a simulation. The results showed that CLIGEN reproduced daily T-max and, T-min reasonably well. The t- and F-tests showed that neither means nor standard deviations of measured data were significantly different from those of the CLIGEN-generated data at P = 0.01 for all stations. Means and distributions of daily Tdp were reproduced very well; however, standard deviations were less well reproduced with significant differences at P = 0.01 for 4 out of 12 stations for the F-test. Mean and standard deviation for daily SR were much better reproduced by our modified CLIGEN at all stations, although distributions were slightly worsened. Daily a was reproduced well after fixing a unit conversion error with an absolute relative error (RE) of 0.17% for the means and 0.71% for the standard deviation. Mean of same-day temperature range (T-max1-T-min1) and one-day lag temperature ranges for both (T-max2-T-min2) and (T-max2-T-min1) of the CLIGEN-generated data were reproduced well with the absolute RE close to zero. However, compared with the measured data, standard deviations of T-max1-T-min1 were consistently underestimated with the RE of -38.7%, and those of T-max1-T-min2 and T-max2-T-min1 were consistently overestimated with the respective RE values being 55.8% and 19.6% for all stations. Seasonal serial correlations of SR and cross correlation between temperatures and SR were much better reproduced by the modified model. Specifically, compared to CLIGEN (v5.111), T-min and T-dp were improved considerably in v5.22564, as well as means of T-max1-T-min2 and T-max2-T-min1, but standard deviations of T-max1-T-min1 were worsened. Standard deviations of T-max1-T-min2 and T-max2-T-min1 generated by v5.111 and v5.22564 were similar. Furthermore, generation of SR and a was similar in both versions, but was significantly improved in our modified v5.22564. Due to the improvement in SR generation, seasonal serial correlations of SR and cross correlation between temperatures and SR were also improved. Overall results showed that non precipitation variables were much better generated by the modified version of 5.22564 than the previous versions in which T-max, T-min and T-dp were generated independently.