1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss WG4 activities Pierre Eckert MeteoSwiss, Geneva

2. 2 COSMO General meeting ¦ Rome, September 2011 Pierre.Eckert[at]meteoswiss.ch Topics Guidelines for forecasters, incl. stratified verification (↔ WG5) Postprocessing Sochi Olympic games  PP CORSO FIELDEXTRA  presentation by Jean- Marie Bettems

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

4. 4 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert 1. CAP = Cluster Analysis of Principal Component 1. Neue (automatisierte) Wetterlagenklassifikationen 2. GWT = GrossWetterTypes 3. GWTWS = adapted GWT GWT10, GWT18 and GWT26 based on (1) MSLP and (2) Z500 GWTWS with 11 classes based on GWT8 for Z500, mean wind at 500 hPa and mean MSLP CAP9, CAP18 and CAP27 based on MSLP 10 classifications are computed every day, based on two different kind of methods Methods

5. 5 Automatic weather classifications| COSMO GM 2011 Tanja Weusthoff / Pierre Eckert 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 Classifications computed back using ECMWF reanalyses 01.09.1957-31.08.2002 ERA40 01.09.2002-31.12.2010 ERA interim Domain: alpine region 41N - 52N (12pts) 3E - 20E (18pts) 1. Neue (automatisierte) Wetterlagenklassifikationen Database

6. 6 Verification results at MeteoSwiss in 2011 COSMO GM / WG5 Parallel Session, 05.09.2011 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 Neighbourhood verification for precipitation (MeteoSwiss, T. Weusthoff)

7. 7 Verification results at MeteoSwiss in 2011 COSMO GM / WG5 Parallel Session, 05.09.2011 NE (11x) N (18x) NW (38x) SE (4x) S (10x) SW (49x) E (4x) W (56x) F (78x) H (73x) L (25x) COSMO-7 better COSMO-2 better differences in Fractions Skill Score for weather-type dependant verif COSMO-2 minus COSMO-7 YEAR 2010

8. 8 Verification results at MeteoSwiss in 2011 COSMO GM / WG5 Parallel Session, 05.09.2011 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. 9 Flora Gofa

10. 10 CAPE>50CAPE<50 Very high POD values for unstable conditions, FAR not so different

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

12. 12 Weather type Dependent Verification w.r.t. high density rainguage network Maria Stefania Tesini

13. 13 6-Northerly cyclonic

14. 14 10-Central Mediterranean Low

15. 15 11-Central Mediterranean Trough

16. 16 Some considerations on models performances 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

17. 17 COSMO General meeting ¦ Rome, September 2011 Pierre.Eckert[at]meteoswiss.ch Postprocessing COSMO-MOS Diagnostics of turbulence for aviation Exchange of postprocessing methods

18. 18 Turbulence index = 1 (light)Turbulence index = 4 (moderate) Turbulence index = 5 (severe) Colours for measurement height in [m] Matthias Raschendorfer COSMO Rome 2011 DWD Diagnostics of turbulence for aviation, M. Raschendorfer DWD

19. 19 Matthias Raschendorfer Distribution between Model- and ARCAS-EDR: -Prediction-pedictor correlation: 0.44 COSMO Rome 2011 DWD

20. 20 Matthias Raschendorfer 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 % COSMO Rome 2011 DWD

21. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Accounting for Change: Local wind forecasts from the high- resolution model COSMO Vanessa Stauch (MeteoSwiss) ECAC & EMS, September, 14 th 2010 COSMO-GM, September 2011, Roma

22. 22 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Spatial verification of wind speed Model topography fairly complex Model performance pretty good

23. 23 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Spatial verification of wind speed Model topography fairly complex Model performance pretty good Model performance at some stations rather poor

24. 24 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Accounting for change Length of database ~ complexity of statistical correction temporal flexibility (e.g. when model error changes) “global MOS” “KF” “UMOS” “COSMO- MOS” „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 + Sampling for many cases, good discrimination - A bit inert when model changes + insensitive to model changes - simple error model, poor discrimination of weather condition Need for models with few parameters “MOS with reforecasts”

25. 25 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Extended logistic regression Wilks 2009 Sample climatology Wind speed threshold Obs Fcst Add thresholds as predictor, estimate one additional parameter

26. 26 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: bias correction for vmax