Abstract
This paper presents a new stochastic multi-variable weather generator (MV-WG) and compares its performance with LARS-WG version 4.0. Daily data of 109 meteorological stations from a North American database were used in a twofold comparison of the two generators: (1) the capability of reproducing the mean and variance of annual, seasonal and monthly values, and (2) the capability of reproducing extreme weather events were compared. Both generators did very well on imitating the mean and the variance of the monthly values of the investigated variables, but both showed a more moderate performance as far as the generation of extreme events was concerned. The three-parameter Weibull function, which is first introduced in MV-WG, was found to be a powerful tool to describe not only the distribution of the daily precipitation amounts, but also the distribution of dry and wet spell lengths, as well.
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