Petra Friederichs - Chair of Statistics STAT

More documents

Recommendations

Info

Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Conclusions ◮ Weather forcasts provide information that conditions occurence of extremes ◮ Linear (non-linear) statistical modeling extracts information ◮ Extreme value theory provides distributions tailored for extremes ◮ Parametric method less uncertain than non-parametric method and non-linear dependecy (shape parameter) is not parsimony ◮ High-impact weather: insufficient data available for training and for validation P.Friederichs Extreme weather 42 / 45
Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Challenges ◮ Improve physical understanding of generation processes of extremes ◮ Application to multi-variable and spatio-temporal predictionswhich ◮ ◮ ◮ Combine spatial statistics with model postprocessing (Berrocal et al., 2007) Develop methods for multivariate postprocessing Develop ensemble methods tailored to extremes ◮ Verification tailored to extremes ◮ Verification for probabilistic multivariate and spatial forecasts P.Friederichs Extreme weather 43 / 45
Page 1 and 2: Mesoscale Weather Prediction Statis
Page 41: Mesoscale Weather Prediction Statis
Page 45: Mesoscale Weather Prediction Statis

Petra Friederichs - Chair of Statistics STAT

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?