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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Bayesian approaches for wind gust and quantitative precipitation forecasting
Bayesian approaches for wind gust and quantitative precipitation forecastingAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Mustapha Amrani. Mathematics for the Fluid Earth Co-author: Sabrina Bentzien (Meteorological Institute, University of Bonn, Germany)
We discuss Bayesian approaches for wind gust and quantitative precipitation forecasting. The Bayesian hierarchical model (BHM) for wind gusts uses extreme value theory, namely a generalized extreme value distribution (GEV), in the data level. A process level for the parameters is introduced which, on the one hand, models the spatial response surfaces of the GEV parameters as Gaussian random fields, and, on the other hand, incorporate the information of the COSMO -DE forecasts. The spatial BHM provides area wide forecasts of wind gusts in terms of a conditional GEV . It models the marginal distribution of the spatial gust process and provides not only forecasts of the conditional GEV at locations without observations, but also uncertainty information about the estimates. At this stage, the BHM ignores the conditional dependence between gusts at neighboring locations. However, an outline is given how this will be incorporated in a subsequent study using max-stable random fields. For quantitative precipitation forecasting we use Bayesian quantile regression and its spatially adaptive extension together with a variable selection based on a Bayesian LASSO . All this is illustrated for the German-focused mesoscale weather prediction ensemble COSMO -DE-EPS, which runs operationally since December 2010 at the German Meteorological Service (DWD). We further discuss the issue of objective out-of-sample verification, where performance is measured using proper scoring rules and their decomposition. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
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