1 / 24

WG4 activities Pierre Eckert MeteoSwiss, Geneva

WG4 activities Pierre Eckert MeteoSwiss, Geneva. Topics. Guidelines for forecasters, incl. stratified verification ( ↔ WG5) Postprocessing Sochi Olympic games  PP CORSO FIELDEXTRA  presentation by Jean-Marie Bettems. New (automatic) weather classifications (MeteoSwiss).

minerva-antony
Download Presentation

WG4 activities Pierre Eckert MeteoSwiss, Geneva

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. WG4activitiesPierre EckertMeteoSwiss, Geneva

  2. Topics • Guidelines for forecasters, incl. stratified verification (↔ WG5) • Postprocessing • Sochi Olympic games  PP CORSO • FIELDEXTRA  presentation by Jean-Marie Bettems

  3. New (automatic) weather classifications (MeteoSwiss) • The old manual weather classifications are replaced with new automated weather classifications. NEW OLD Alpenwetterstatistik AWS Perret Zala-Klassifikation GWT & CAP/PCACA automated Since January 2011, Calculated back until 01.09.1957 Manual, until 31.12.2010

  4. 1. Neue (automatisierte) Wetterlagenklassifikationen Methods 10 classifications are computed every day, based on two different kind of methods 1. CAP = Cluster Analysis of Principal Component 2. GWT = GrossWetterTypes CAP9, CAP18 and CAP27 based on MSLP GWT10, GWT18 and GWT26 based on (1) MSLP and (2) Z500 3. GWTWS = adapted GWT GWTWS with 11 classes based on GWT8 for Z500, mean wind at 500 hPa and mean MSLP

  5. 1. Neue (automatisierte) Wetterlagenklassifikationen Database • Classifications computed back using ECMWF reanalyses 01.09.1957-31.08.2002 ERA40 01.09.2002-31.12.2010 ERA interim • For daily computation (since 01.01.2011), use of the operational IFS 12z run from ECMWF; Analysis and forecasts out to 10 days are classified • Domain: alpine region 41N - 52N (12pts) 3E - 20E (18pts)

  6. Neighbourhood verification for precipitation(MeteoSwiss, T. Weusthoff) Results for 2010 3h accumulated precipitation sums over the domain of the Swiss radar composite models: COSMO-2 and COSMO-7 for all 8 daily forecast runs, precipitation sums from +3 to +6h observation precipitation estimates of the swiss radar composit in case of a missing value, the full date will not be evaluated

  7. differences in Fractions Skill Score for weather-type dependant verif COSMO-2 minus COSMO-7 COSMO-7 better COSMO-2 better YEAR 2010 NE (11x) S (10x) F (78x) SW (49x) N (18x) H (73x) E (4x) NW (38x) W (56x) SE (4x) L (25x)

  8. Summary neighbourhood verification precipitation in 2010 • The skill of the models varies for different weather types and the differences between COSMO-2 and COSMO-7 varies also:- best skill: Autumn and Spring, south to northwest weather types- greatest difference COSMO-2 minus COSMO-7: Summer and Winter, north- and east types, convective cases Tanja Weusthoff

  9. Conditional verification Flora Gofa

  10. 1 2 3 4 5 6 7 8 9 10 11 12 For southerly weather situations the cloud cover is more overestimated….

  11. Weather type Dependent Verification w.r.t. high density rainguage network Maria Stefania Tesini

  12. 6-Northerly cyclonic

  13. 10-Central Mediterranean Low

  14. Some considerationson modelsperformances • At low threshold (e.g. 1 mm/24h) • Cosmo Models perform well in cyclonic situations (CLM,CMT,MC) – high TS and BIAS ≈1 • ECMWF is strongly biased • In anticyclonic situation COSMO-MED and ECMWF are better in terms of POD but they tend to overestimate the number of events • At higher thresholds (e.g. 5 m/24h and 10 mm/24h) • COSMO-I7 and I2 miss the anticyclonic situation • still good performance for all models for the cyclonic situations

  15. Postprocessing • COSMO-MOS • Diagnostics of turbulence for aviation • Exchange of postprocessing methods

  16. Diagnostics of turbulence for aviation, M. Raschendorfer DWD Turbulence index = 1 (light) Turbulence index = 4 (moderate) Turbulence index = 5 (severe) Colours for measurement height in [m] DWD Matthias Raschendorfer COSMO Rome 2011

  17. Distribution between Model- and ARCAS-EDR: • Prediction-pedictor correlation: 0.44 DWD Matthias Raschendorfer COSMO Rome 2011

  18. Final distribution after successive regression: • 21 predictors • most effective besides edr: p, dt_tke_(con, sso, hsh) • Successive cubic regression of residuals • Prediction-pedictor correlation: 0.627 • Variance reduction: 39.9 % DWD Matthias Raschendorfer COSMO Rome 2011

  19. Accounting for Change:Local wind forecasts from the high- resolution model COSMO Vanessa Stauch (MeteoSwiss) ECAC & EMS, September, 14th 2010 COSMO-GM, September 2011, Roma

  20. Spatial verification of wind speed Model topography fairly complex Model performance pretty good

  21. Spatial verification of wind speed Model topography fairly complex Model performance pretty good Model performance at some stations rather poor

  22. “COSMO-MOS” + Sampling for many cases, good discrimination - A bit inert when model changes Accounting for change “MOS with reforecasts” + insensitive to model changes - simple error model, poor discrimination of weather condition „global MOS “: e.g. MOSMIX at DWD, multiple linear regression based on global NWP models (GME and IFS) “UMOS”: ‘updateable’ MOS of Canadians, weighting when model chsnges “KF”: Kalman Filter based estimation, online update “global MOS” Length of database ~ complexity of statistical correction “UMOS” “KF” temporal flexibility (e.g. when model error changes) Need for models with few parameters

  23. Extended logistic regression Obs Fcst threshold Sample climatology Add thresholds as predictor, estimate one additional parameter Wind speed Wilks 2009

  24. Results: bias correction for vmax

More Related