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首页> 外文期刊>Discrete and continuous dynamical systems, Series S >GAUSSIAN MIXTURE MODELS FOR CLUSTERING AND CALIBRATION OF ENSEMBLE WEATHER FORECASTS
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GAUSSIAN MIXTURE MODELS FOR CLUSTERING AND CALIBRATION OF ENSEMBLE WEATHER FORECASTS

机译：GAUSSIAN MIXTURE MODELS FOR CLUSTERING AND CALIBRATION OF ENSEMBLE WEATHER FORECASTS

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摘要

Nowadays, most weather forecasting centers produce ensembleforecasts. Ensemble forecasts provide information about probability distri-bution of the weather variables. They give a more complete description ofthe atmosphere than a unique run of the meteorological model. However, theymay suffer from bias and under/over dispersion errors that need to be corrected.These distribution errors may depend on weather regimes. In this paper, wepropose various extensions of the Gaussian mixture model and its associatedinference tools for ensemble data sets. The proposed models are then usedto identify clusters which correspond to different types of distribution errors.Finally, a standard calibration method known as Non homogeneous GaussianRegression (NGR) is applied cluster by cluster in order to correct ensembleforecast distributions. It is shown that the proposed methodology is effective,interpretable and easy to use. The clustering algorithms are illustrated onsimulated and real data. The calibration method is applied to real data oftemperature and wind medium range forecast for 3 stations in France.

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