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SUMMARY:Bayesian approaches for wind gust and quantitative precipitation f
 orecasting - Friederichs\, P (Universitt Bonn)
DTSTART:20131031T141500Z
DTEND:20131031T145000Z
UID:TALK48626@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-author: Sabrina Bentzien (Meteorological Institute\, Univer
 sity of Bonn\, Germany) \n\n Due to large uncertainties\, predictions of h
 igh-impact weather on the atmospheric mesoscale are probabilistic in natur
 e. Mesoscale weather ensemble prediction systems (EPS) are developed to ob
 tain probabilistic guidance for high impact weather. An EPS not only issue
 s a deterministic future state of the atmosphere but a sample of possible 
 future states. Ensemble postprocessing then translates such a sample of fo
 recasts into probabilistic measures.\n\nWe discuss Bayesian approaches for
  wind gust and quantitative precipitation forecasting. The Bayesian hierar
 chical model (BHM) for wind gusts uses extreme value theory\, namely a gen
 eralized extreme value distribution (GEV)\, in the data level. A process l
 evel 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 f
 orecasts. The spatial BHM provides area wide forecasts of wind gusts in te
 rms of a conditional GEV. It models the marginal distribution of the spati
 al gust process and provides not only forecasts of the conditional GEV at 
 locations without observations\, but also uncertainty information about th
 e estimates. At this stage\, the BHM ignores the conditional dependence be
 tween gusts at neighboring locations. However\, an outline is given how th
 is will be incorporated in a subsequent study using max-stable random fiel
 ds.\n\nFor 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 Germa
 n-focused mesoscale weather prediction ensemble COSMO-DE-EPS\, which runs 
 operationally since December 2010 at the German Meteorological Service (DW
 D). We further discuss the issue of objective out-of-sample verification\,
  where performance is measured using proper scoring rules and their decomp
 osition.\n\n
LOCATION:Seminar Room 1\, Newton Institute
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