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On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF

On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF. Daniel Leuenberger 1 , Christian Keil 2 and George Craig 2 1 MeteoSwiss, Zurich, Switzerland 2 DLR, Oberpfaffenhofen, Germany COSMO GM 2008, Cracow. Introduction.

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On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF

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  1. On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF Daniel Leuenberger1, Christian Keil2 and George Craig2 1MeteoSwiss, Zurich, Switzerland 2DLR, Oberpfaffenhofen, Germany COSMO GM 2008, Cracow

  2. Introduction • Convective-scale assimilation of radar rainfall data • Latent Heat Nudging (LHN) • Results of a 7 month test suite at MeteoSwiss • What determines the impact of LHN on QPF?

  3. ECMWF IFS • COSMO-7 • 6.6km, 60 levels • Param. deep convection • Assimilation of conv. obs. COSMO-7 COSMO-2 COSMO-2 • 2.2km, 60 levels • Explicit deep convection • Assimilation of conv. obs. and radar rainfall Radar ~600 km MeteoSwiss Model Setup

  4. Setup of Experiments • 2.2km assimilation cycle with/without LHN • Forecasts out to +12h, initialized at 00 and 12 UTC • 11. June 2007 – 15. January 2008 (346 forecasts)

  5. 0-6hPrecipitation forecast (12.06.2007) Verifying Radar LHN NOLHN Radar 6-12h Precipitation Forecast (19.06.2007) Verifying Radar LHN NOLHN Radar Examples of Improvement

  6. Verification against Radar 346 Forecasts, 11. June 2007 - 15. January 2008, hourly sums

  7. Verification against Radar (Summer) 9 Forecasts, 11. June - 19. July 2007, hourly sums

  8. Wind direction 66 64 62 deg 60 58 • 18 00 • Time UTC Verification of other Variables RMS of 74 12UTC Forecasts (Reference: ca. 60 Swiss Sfc. Stations) Surface Pressure 10m Wind speed 335 2.25 330 2.20 2.15 m/s 325 Pa 2.10 320 NOLHN LHN 315 2.05 • 18 00 • Time UTC • 18 00 • Time UTC

  9. Verification of other Variables RMS of 74 12UTC Forecasts (Reference: ca. 60 Swiss Sfc. Stations) 2m Temperature 2m Dewp. Temperature Cloud cover 2.6 34 3.0 NOLHN LHN 2.4 2.8 32 K 2.6 2.2 K % 30 2.4 2.0 28 2.2 1.8 26 • 18 00 • Time UTC • 18 00 • Time UTC • 18 00 • Time UTC

  10. 12. July 2006 31.July 2006 28. June 2006 non-forced frontal airmass forced frontal What determines the impact of LHN? • Use high-resolution NWP ensemble (2.8km mesh size) • Driven by regional COSMO-LEPS ensemble • 10 members with LHN, 10 members without • Different mesoscale environment in each member • 3 differently forced convection cases

  11. NOLHN 1.0 0.8 Radar 0.6 0.4 NWP Ensemble 0.2 0.0 18 15 21 06 09 12 00 Time UTC Example: Airmass convection Timelines of observed and simulated area-averaged surface rainfall LHN mm Forecast Assimilation 18 12 15 06 09 00 21 Time UTC

  12. FLHN 1 0.5 time tLHN Definition of Time Scales • LHN impact factor • LHN time scale tLHN • Convective time scale • Done et al. (QJ 2006)

  13. forced frontal, non-forced frontal airmass Stratification of Simulations • Results suggest 2 different regimes: • equilibrium situation: • short tc • precipitation only redistributed • short-lived impact of LHN 100 tLHN [h] 10 • non-equilibrium situation: • long tc • LHN triggers convection • long lasting impact of LHN 1 10 100 1 0.1 tc [h]

  14. Findings • LHN improves high-resolution NWP forecasts • QPF improvement in the first 3-12h (dependent on score and rainfall intensity) • Other variables slightly improved, particularly in summer • More realistic rainfall input for soil moisture • Impact on QPF dependent on • Precipitation forcing (equilibrium vs. non-equilibrium) • Life time of precipitation system • Mesoscale environment of convection (e.g. stability) • Extent of NWP model domain and radar data coverage

  15. What Next at MeteoSwiss? • Introduce quality control • Based on statistics of radar data, can handle e.g. • visibility • non-rain echoes • foreign radar data (unknown specifications) • => quality weight for each pixel • Add foreign radar data • France, Germany, Italy, Austria

  16. Thank you for your attention

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