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EPS: huge amount of information - NWP model pool - ECMWF EPS product family

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0. Introduction. Severe weather prediction by ensemble prediction tools - it´s a challenge - great variability of weather events Forecaster has to deal with the predictability of the event, the cost-lost-ratio, customer needs, model physics. - PowerPoint PPT Presentation
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DWD 2nd SRNWP Workshop on Short Range Ensemble, Bologna, 7-8 April 2005 Deutscher Wetterdienst Zentrale Vorhersage 1 EPS: huge amount of information - NWP model pool - ECMWF EPS product family - COSMO-LEPS, SRNWP PEPS - previous EPS (or model) runs decision for a certain szenario in a limited time fra 0. Introduction Severe weather prediction by ensemble prediction tool it´s a challenge - great variability of weather even Forecaster has to deal with the predictability of the the cost-lost-ratio, customer needs, model physics . 1. First steps - and still favorable used at the AFREG-MIX: developed during the eighties in Potsd low cost system (CPU-time) Mixing GME and ECMWF and deriving weather parame by statistical methods - Metgrams, tables 2. Using an EPS - from the forecasters point of - automatically generate fc´s (AFREG, GMOS) - forecasters experience Questions: 1. Is it possible to add value to forecasts by simple m of NWP models without of any calibration ? 2. The main szenario provided by the EPS or the majorit the NWP models - is this always the correct one ? 3. Could a NWP model PEPS outperform the ECMWF EPS ?
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Page 1: EPS: huge amount of information  -  NWP model pool  -  ECMWF EPS product family

DWD 2nd SRNWP Workshop on Short Range Ensemble, Bologna, 7-8 April 2005

Deutscher WetterdienstZentrale Vorhersage

1

• EPS: huge amount of information - NWP model pool - ECMWF EPS product family - COSMO-LEPS, SRNWP PEPS - previous EPS (or model) runs • decision for a certain szenario in a limited time frame

0. Introduction• Severe weather prediction by ensemble prediction tools - it´s a challenge - great variability of weather events• Forecaster has to deal with the predictability of the event, the cost-lost-ratio, customer needs, model physics ...

1. First steps - and still favorable used at the DWD• AFREG-MIX: developed during the eighties in Potsdam• low cost system (CPU-time)• Mixing GME and ECMWF and deriving weather parameter by statistical methods - Metgrams, tables

2. Using an EPS - from the forecasters point of view

- automatically generated fc´s (AFREG, GMOS)- forecasters experience

Questions:1. Is it possible to add value to forecasts by simple mixing of NWP models without of any calibration ?2. The main szenario provided by the EPS or the majority of the NWP models - is this always the correct one ?3. Could a NWP model PEPS outperform the ECMWF EPS ?

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