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Verification of COSMO-LEPS and coupling with a hydrologic model

Verification of COSMO-LEPS and coupling with a hydrologic model. André Walser 1) and Simon Jaun 2) 1) MeteoSwiss 2) Institute for Atmospheric and Climate Science, ETH. Overview. Part 1: Status report of Task 4.2 of PP Interpretation: Use of COSMO-LEPS in hydrologic models

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Verification of COSMO-LEPS and coupling with a hydrologic model

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  1. Verification of COSMO-LEPS and coupling with a hydrologic model André Walser1) and Simon Jaun2) 1)MeteoSwiss 2)Institute for Atmospheric and Climate Science, ETH

  2. Overview • Part 1: Status report of Task 4.2 of PP Interpretation:Use of COSMO-LEPS in hydrologic models • Part 2: COSMO-LEPS verification against SYNOP messages

  3. Model chain • Global ensembles ECMWF EPS • Downscaling with COSMO-LEPS • PREVAH as hydrologic model Fig. M. Verbunt ECMWF EPS  COSMO-LEPS  PREVAH Uncertainties in the hydrologic model/processes not considered

  4. Data flow for MAP D-PHASE Main partner WSL: Swiss Federal Institute for Forest, Snow and Landscape Research

  5. “MAP D-PHASE Catchments”

  6. Product delivered for D-PHASE VP

  7. Comparison different models • August 2007 event Linth at Mollis, initial time 2007-08-06

  8. Comparison different models • August 2007 event Linth at Mollis, initial time 2007-08-07

  9. Comparison different models • August 2007 event Linth at Mollis, initial time 2007-08-08

  10. Verification August 2005 Event Probability to exceed 10y event > 10 y event Jaun et al.: A probabilistic view on the August 2005 floods in the upper Rhine catchment, to be submitted

  11. Conclusions & Outlook • Operational ensemble runoff forecast are established for MAP D-PHASE • Seems to provide reliable information about the forecast uncertainty and early indication for flood events  Verification over an extended period needed

  12. Part II • Most recent COSMO-LEPS Verification Results: • 12-h sum of precipitation from SYNOP messages, COSMO-LEPS domain • Brier Skill Score for 1, 5, 10, 25 mm • Climatological event frequency estimated from data from 1996-2005

  13. BSS for precipitation > 1 mm/12h night-time precipitation daytime precipitation No skill lead-time

  14. BSS for precipitation > 5 mm/12h lead-time

  15. BSS for precipitation > 10 mm/12h lead-time

  16. BSS for precipitation > 25 mm/12h lead-time

  17. Large spatial variability of the skill BSS for precipitation > 1 mm/12h MAM 2006 (+42h)

  18. Station Sion:BSS for precipitation > 1 mm/12h lead-time

  19. “No skill station”: Sion 12-h precipitation sum > 1mm MAM 2006:

  20. Station Sion:BSS for postprocessed precipitation > 1 mm/12h Very simple post-processing: p’=0.5p lead-time

  21. Conclusions • For low precipitation thresholds skill up to forecast day 5 for all seasons • No skill for high threshold 25mm/12h for all seasons • In spring and summer higher skill for night-time precipitation than for daytime • High spatial variability • There is potential to improve precip. forecast in Alpine region with post-processing  sophisticated method Talk/Poster of F. Fundel

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