1. 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

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

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

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

5. COSMO-LEPS forecast for 72h precipitation

6. COSMO-LEPS forecast for 72h precipitation

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

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

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

10. 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

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

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

13. 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

14. 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

15. 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

16. 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.