Petra Friederichs - Chair of Statistics STAT

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Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Ensemble Postprocessing – Precipitation Case study – 3rd July 2009 Spatial Modelling – Wind Gusts Spatial modeling of peak wind speed observations ◮ Marginal distributions: Spatial interpolation of marginal distribution - conditional on large scale weather situation - conditional on local conditions (surface roughness, altitude,. . .) → Transform marginals to standard Fréchet ◮ Dependence structure: Gaussian random fields to construct max-stable processes P.Friederichs Extreme weather 28 / 45
Mesoscale Weather Prediction Statistical Postprocessing Applications Conclusions and Challenges Ensemble Postprocessing – Precipitation Case study – 3rd July 2009 Spatial Modelling – Wind Gusts Conditional marginal distribution Non-stationary process is characterised by atmospheric circulation ◮ COSMO-DE forecasts: wind velocity 10m above ground ◮ Diurnal and annual cycle ◮ Topography and surface roughness ◮ Spatially and temporally constant hyperparameters (MLE) µ t = Z(X t ) T γ ; σ t = Z(X t ) T ρ ; ξ t = ξ → Diploma thesis of S. Memmel: BHM to spatially model parameter surfaces P.Friederichs Extreme weather 29 / 45
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Petra Friederichs - Chair of Statistics STAT

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