Bayesian Model Averaging (BMA) and Bayesian Hierarchical Model (BHM) are statistical postprocessing techniques for calibrating precipitation forecast ensembles. BMA is a mixture model of predictive densities, while BHM is a fully Bayesian alternative to BMA. Both techniques are applied on a case-study. BMA is applied to quantitative precipitation, yielding a better calibration than the ensemble in homogeneous areas. For qualitative precipitation, both BMA and BHM forecasts are more calibrated than the ensemble. However, BHM yields a worse performance due to the “shrinkage” effect, that lets the forecasts vary across a small range of values.

Alternative approaches for probabilistic precipitation forecasting

BRUNO, FRANCESCA;COCCHI, DANIELA;
2011

Abstract

Bayesian Model Averaging (BMA) and Bayesian Hierarchical Model (BHM) are statistical postprocessing techniques for calibrating precipitation forecast ensembles. BMA is a mixture model of predictive densities, while BHM is a fully Bayesian alternative to BMA. Both techniques are applied on a case-study. BMA is applied to quantitative precipitation, yielding a better calibration than the ensemble in homogeneous areas. For qualitative precipitation, both BMA and BHM forecasts are more calibrated than the ensemble. However, BHM yields a worse performance due to the “shrinkage” effect, that lets the forecasts vary across a small range of values.
Spatial Data Methods for Environmental and Ecological Processes - 2nd Edition. Proceedings
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Bruno F.; Cocchi D. ; Rigazio A.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/106910
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