Dynamic bayesian networks for rainfall forecasting
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 informati...
Guardado en:
| Autores principales: | , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2001
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23299 |
| Aporte de: |
| Sumario: | 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. |
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