Petra Friederichs - Chair of Statistics STAT

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Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Mesoscale Extremes Predicting high-impact weather Ensemble Postprocessing from Hamill (2006) Probabilistic forecasting: Maximize sharpness of the predictive distribution subject to calibration Calibration: Raw ensemble data need adjustmend: biased and underdispersive Gneiting et al. (2005) P.Friederichs Extreme weather 12 / 45
Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Semi-Parametric Approaches Parametric Approaches Forecast Verification: Proper Scoring Rules Statistical postprocessing ◮ Marginal distributions: Conditional on weather forecast Standard ensemble postprocessing ◮ Ensemble MOS (e.g., Wilks, 2006; Gneiting et al., 2004) ◮ ◮ Ensemble dressing – Ensemle kernel dressing (e.g., Wang and Bishop, 2004; Broecker and Smith, 200) Bayesian model averaging (e.g., Hoeting et al, 1999; Raftery et al, 2005) ◮ Dependence structure: Transformed Gaussian random fields (e.g., Berrocal et al., 2008) Max-stable random fields (e.g., Kabluchko et al., 20098) P.Friederichs Extreme weather 13 / 45
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Petra Friederichs - Chair of Statistics STAT

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