André Walser MeteoSwiss, Zurich

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Presentation transcript:

André Walser MeteoSwiss, Zurich Evaluation of the COSMO-LEPS forecasts for the floods in Switzerland in August 2005 and further recent results… André Walser MeteoSwiss, Zurich

Case study: Flood event in Switzerland in August 2005 Photos:Tages-Anzeiger

Synoptic overview: 22 August 2005 Temperature 850 hPa and geopotential 500 hPa: 18º 10º 2º

Total precipitation over 3 days (20. – 22.8.) (06 - 06 UTC) C. Frei, MeteoSwiss About 400 stations, precipitation sum locally over 300 mm!

COSMO-LEPS forecast for 72h precipitation

COSMO-LEPS forecast for 72h precipitation

Probability precipitation > 100mm/72h C. Frei, MeteoSwiss

Probability precipitation > 100mm/72h C. Frei, MeteoSwiss

Probability precipitation > 250mm/72h C. Frei, MeteoSwiss

Using COSMO-LEPS for hydrologic forecasts Hydrologic model setup: Rhine upstream of Rheinfelden (34550 km2) Model: PREVAH (Gurtz et al. 1999) Driven by COSMO-LEPS A few test cases with 51 ensemble members Reuss (Luzern) Mark Verbunt (ETH Zurich) et al., submitted to J. Hydrometeorology

Case study: Ensemble runoff prediction for flood event 1999 Mark Verbunt et al., submitted to J. Hydrometeorology

Case study: Ensemble runoff prediction for flood event 1999 Mark Verbunt et al., submitted to J. Hydrometeorology

Synop-Verification: COSMO-LEPS & aLMo aLMo: - deterministic 7 km model (45 levels) of MeteoSwiss for 72h forecasts - same code (LM) as COSMO-LEPS aLMo domain Common verification domain, about 1000 synop stations

Standard deviation: 2 m temperature 12 UTC DJF 2004/2005 JJA 2005 Forecast range (h) Forecast range (h) uw: unweighted / w: weighted according to cluster population

Standard deviation: 2 m temperature 00 UTC DJF 2004/2005 JJA 2005 w Forecast range (h) Forecast range (h) uw: unweighted / w: weighted according to cluster population

Summary COSMO-LEPS forecasts for 2 case studies provides early warnings for extreme events. Coupled high-resolution atmospheric-hydrologic EPSs have a great potential to improve runoff forecasts. COSMO-LEPS ensemble mean for 2m temperature has better skill (lower standard deviation to observations) with weighting the ensemble members according to cluster populations compared to the mean with unweighted members.