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SUMMARY:Stochstic representation of model uncertainties in ECMWF's forecas
 ting system - Steinheimer\, M (ECMWF)
DTSTART:20100827T103000Z
DTEND:20100827T113000Z
UID:TALK25905@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The Integrated Forecasting System (IFS) is a sophisticated sof
 tware system for weather forecasting\, which was jointly developed by the 
 European Centre for Medium-Range Weather Forecasts (ECMWF) and Meteo Franc
 e. All applications needed for generating operational weather forecasts ar
 e included\, such as data assimilation\, atmospheric model and post proces
 sing.\nThe IFS is used for deterministic 10 day forecasts and ensemble for
 ecasts with forecast ranges from 15 days for the medium range EPS\, 32 day
 s for the monthly forecast up to 13 month for the seasonal forecasts.\nIn 
 addition to a good deterministic forecast model as basis of the ensemble p
 rediction system\, the ingredients needed to produce good ensemble forecas
 ts are realistic and appropriate representations of the initial and model 
 uncertainties.\nThe stochastic schemes used for the model error representa
 tion will be presented. These are the Spectral Stochastic Backscatter Sche
 me (SPBS) and the Stochastically Perturbed Parametrization Tendency Scheme
  (SPPT). \nThe basis of both schemes is a random spectral pattern generato
 r\, in which the spectral coefficients are evolved with a first order auto
 -regressive process. The resulting pattern varies smoothly in space and ti
 me with easy to control spatial and temporal correlation.\nThe two schemes
  address different aspects of model error. SPPT addresses uncertainty in e
 xisting parametrization schemes\, as for example parameter settings\, and 
 therefore generalizes the output of existing parametrizations as probabili
 ty distributions. SPBS on the other hand describes upscale energy transfer
  related to spurious numerical dissipation as well as the upscale energy t
 ransfer from unbalanced motions associated with convection and gravity wav
 es\, process missing in conventional parametrization schemes.\nCellular Au
 tomata (CA) are an alternative way for generating random patterns with tem
 poral and spatial correlations. A pattern generator based on a probabilist
 ic CA was implemented in the IFS. The implementation allows the interactio
 n of model fields with the CA\, i.e. \nthe characteristics of the CA are i
 nfluenced by the atmospheric state.\nThe impact of the stochastic schemes 
 on the forecast skill will be presented for different forecast ranges.
LOCATION:Seminar Room 1\, Newton Institute
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