Joint Seminar: Generalized Linear Modeling Approach to Stochastic Weather Generators

Stochastic weather generators are a popular method for producing synthetic sequences of daily weather. It is demonstrated 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. As an application, the GLM technique is applied to daily time series of weather variables (i.e., precipitation amount as well as minimum and maximum temperature) at 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. Extensions of the GLM framework to improve the simulation of extreme precipitation events are also considered.



Furrer, E.M., and R.W. Katz, 2007: Generalized linear modeling approach to stochastic weather generators. Climate Research, 34, 129–144.

Furrer, E.M., and R.W. Katz, 2008: Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resources Research, 44 (in press).




13:30 Uhr


Bundesstr. 53, room 022/023
Seminar Room 022/023, Ground Floor, Bundesstrasse 53, 20146 Hamburg, Hamburg


Rick Katz, NCAR


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