Dynamic bayesian networks for rainfall forecasting

• Autores: Gutiérrez Llorente, José Manuel; Cano, R.
• Año de publicación: 2001
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Learning; informática; Weather forecasting; downscaling; expert systems; probabilistic networks; temporal modeling
• Institución: Universidad Nacional de La Plata

In this paper we deal with the problem of forecasting local rainfall at multiple meteorological stations over the Iberian peninsula. To this aim a dynamic Bayesian network is introduced for relating rainfall to broad-scale atmospheric circulation patterns. In this way statistical historic information gathered at the available stations is combined with numerical atmospheric predictions developed at different weather services, resulting a single consensus prediction. This technique can be considered an hybrid statistical-numerical method for precipitation downscaling (predicting local values based on broad-scale grided predictions), and can be easily adapted to other meteorological variables of interest.
Eje: Informática teórica
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