This paper describes a versatile stochastic daily weather generator (WeaGETS) for producing daily precipitation, maximum and minimum temperatures (Tmax and Tmin). The performance of WeaGETS is demonstrated with respect to the generation of precipitation, Tmax and Tmin for two Canadian meteorological stations. The results show that the widely used first-order Markov model is adequate for producing precipitation occurrence, but it underestimates the longest dry spell for dry station. The higher-order models have positive effects. The gamma distribution is consistently better than the exponential distribution at generating precipitation quantity. The conditional scheme is good at simulating Tmax and Tmin. The spectral correction approach built in WeaGETS successfully preserves the observed low-frequency variability and autocorrelation functions of precipitation and temperatures.
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Chen et al. (2012) studied this question.
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