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Precipitation and cloud forecasts in two HIRLAM versions (RCR and H635) in September 2004

This study compares the precipitation and cloud forecasts of two HIRLAM versions (RCR and H635) in September 2004. It analyzes the differences between the forecasts, verifies the accuracy of temperature and cloudiness predictions, and provides conclusions based on the results.

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Precipitation and cloud forecasts in two HIRLAM versions (RCR and H635) in September 2004

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  1. Precipitation and cloud forecasts in two HIRLAM versions (RCR and H635) in September 2004 Kalle Eerola Finnish Meteorological Institute kalle.eerola@fmi.fi

  2. Introduction Precipitation verification of European LAMs from U.K. Met.Off. Comparison of RCR and H635 forecasts Differences between RCR and H635 Accumulated monthly precipitation Verification of T2m, Rh2m and cloudiness Conclusions Contents

  3. Area: United Kingdom Against UK NIMPROD rain-fall composite Time: since January 2004 LAMs Aladin (France) Hirlam RCR (FMI) Lokall MODEL (DWD) Met. Off. unified mesoscale Model (U.K.) Scores FBI - Frequency Bias Index >1  overestimates, < 1  underestimates ETS – Equitable Threat Score =0 for random hit, =1 for perfect forecast Precipitation verification of European LAMs by U.K. Met.Off

  4. FBI and ETS over UK for different thresholds • Results since January 2004 • Hirlam = RCR at FMI (~6.3.0) • Frequency bias: • All model overestimate weak precipitation • Hirlam underestimates moderate/strong precipitation • Other models overestimate them • Equitable Threat Score • Hirlam: very weak rain: score lower • Skill decreases in all model as threshold increases

  5. Results of tests between RCR and Hirlam 6.3.5 • RCR – Regular Cycle with the Reference • RCR = Hirlam 6.2.1 + changes ≈ Hirlam 6.3.0 • Earlier no one ran with reference  no good idea how the reference works • Operational at FMI • Resolution 0.2º x 0.2º, 40 levels • Archived at ECMWF, available for Hirlam community • Products available for Hirlam community in near-real time on WEB-pages • Available a mod. set to run parallel to RCR (will be included into the reference ) • Possible to test against RCR products • A control run already exists

  6. Similarities H635 uses same observations, boundaries, boundary strategies and extra observations as RCR Same area and resolution in horizontal and vertical Differences New release of HIRVDA (mainly technical) Modified water vapour saturation below freezing First Aid Kit by Laura Rontu Tanquay-Ritchie scheme of temperature in SL-scheme Rotation of surface stress vector Physics-dynamics coupling Modified melting of soil ice Smoothed topography Main differences and similarities between RCR and H635

  7. Monthly precipitation: Europe • Rather similar in RCR and H635, compares well to gauge-based analysis • GPCC = The Global Precipitation Climatology Centre • H635 more precipitation than RCR (Scandinavia, Alps,…) • H635 has smoother structure • Smoothed orography • Tanquay-Ritchie SL changes • Dynamics-physics coupling

  8. Monthly precipitation: Scandinavia • Rainy month in Finland: observed is 100-300% of the normal • In H635 more precipitation, fits better to observed (southern Finland, Kainuu), • H635 has smoother sctructure

  9. Convective part of precipitation • As earlier, the 24-48 hour forecasts, ie. second day • In H635 the structure is much smoother than in RCRa • Smoothed orography • Tanquay-Ritchie SL changes • Dynamics-physics coupling • Especially over the mountains

  10. H635 produces more precipitation than RCR The accumulated monthly precipitation has a smoother structure in H635 than in RCR Convective part is especially smoother Conclusion from precipitation

  11. Diurnal variation in surface (near-)parameters

  12. Station verification for EWGLAM stations • T2m • At night almost all negative bias removed • During day negative bias reduced • RH2m • Diurnal cycle in RCRa • In H635 almost unbiased

  13. At night: Bias of T2m, Rh2m and cloudiness

  14. During day: Bias of T2m, Rh2m and cloudiness

  15. U. K. Met. Off. Precipitation verification Frequency bias: All model overestimate weak precipitation Hirlam underestimates moderate/strong precipitation, while other models overestimate them Equitable Threat Score Hirlam: very weak rain: score lower Skill decreases in all model as threshold increases Accumulated monthly precipitation H635 produces more precipitation than RCR The accumulated monthly precipitation has a smoother structure in H635 than in RCR Convective part is especially smoother Conclusions I

  16. At Night T2m: negative bias decreased, even positive bias in some are Rh2m: Positive bias (too humid) decreased Too dry: southern Europe and America Cloudiness. Difficult to interpret the results Daytime T2m: Cold bias still is in H635 but it is reduced In America almost disappeared Rh2m: Positive (too humid bias has reduced in central and northern Europe Even too much in southern Europe, where in H635 is now negative bias Cloudiness: difficult to interpret Conclusions II

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