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Generalized linear modeling approach to stochastic weather generators
Ist Teil von
Climate research, 2007-07, Vol.34 (2), p.129-144
Ort / Verlag
Inter-Research
Erscheinungsjahr
2007
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
Stochastic weather generators are a popular method for producing synthetic sequences of daily weather. We demonstrate that generalized linear models (GLMs) can provide a general modeling framework, allowing the straightforward incorporation of annual cycles and other covariates (e.g. an index of the El Niño-Southern Oscillation, ENSO) into stochastic weather generators. We apply the GLM technique to daily time series of weather variables (i.e. precipitation and minimum and maximum temperature) from Pergamino, Argentina. Besides annual cycles, the fit is significantly improved by permitting both the transition probabilities of the first-order Markov chain for daily precipitation occurrence, as well as the means of both daily minimum and maximum temperature, to depend on the ENSO state. Although it is more parsimonious than typical weather generators, the GLM-based weather generator performs comparably, particularly in terms of extremes and overdispersion.