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Stochstic representation of model uncertainties in ECMWF's forecasting system

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If you have a question about this talk, please contact Mustapha Amrani.

Mathematical and Statistical Approaches to Climate Modelling and Prediction

The Integrated Forecasting System (IFS) is a sophisticated software system for weather forecasting, which was jointly developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and Meteo France. All applications needed for generating operational weather forecasts are included, such as data assimilation, atmospheric model and post processing. The IFS is used for deterministic 10 day forecasts and ensemble forecasts with forecast ranges from 15 days for the medium range EPS , 32 days for the monthly forecast up to 13 month for the seasonal forecasts. In addition to a good deterministic forecast model as basis of the ensemble prediction system, the ingredients needed to produce good ensemble forecasts are realistic and appropriate representations of the initial and model uncertainties. The stochastic schemes used for the model error representation will be presented. These are the Spectral Stochastic Backscatter Scheme (SPBS) and the Stochastically Perturbed Parametrization Tendency Scheme (SPPT). The basis of both schemes is a random spectral pattern generator, in which the spectral coefficients are evolved with a first order auto-regressive process. The resulting pattern varies smoothly in space and time with easy to control spatial and temporal correlation. The two schemes address different aspects of model error. SPPT addresses uncertainty in existing parametrization schemes, as for example parameter settings, and therefore generalizes the output of existing parametrizations as probability distributions. SPBS on the other hand describes upscale energy transfer related to spurious numerical dissipation as well as the upscale energy transfer from unbalanced motions associated with convection and gravity waves, process missing in conventional parametrization schemes. Cellular Automata (CA) are an alternative way for generating random patterns with temporal and spatial correlations. A pattern generator based on a probabilistic CA was implemented in the IFS . The implementation allows the interaction of model fields with the CA, i.e. the characteristics of the CA are influenced by the atmospheric state. The impact of the stochastic schemes on the forecast skill will be presented for different forecast ranges.

This talk is part of the Isaac Newton Institute Seminar Series series.

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