Federal Department of Home Affairs FDHA Federal Office
- Slides: 28
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology Meteo. Swiss WG 4: interpretation and applications overview Pierre Eckert Meteo. Swiss, Geneva
Topics • Sochi Olympic games PP CORSO • FIELDEXTRA presentation by JM Bettems • Postprocessing • • CORSO Kalman filter COSMO-MOS CAT diagnostics Use of chekclist • Guidelines • Plans COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 2
Priority project CORSO • Task 1: implementation of high resolution model • Task 2: postprocessing and usability • Task 3: development of EPS COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 3
Priority project CORSO • Task 1: implementation of high resolution model • Task 2: postprocessing and usability • Task 3: development of EPS COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 4
RESULT FROM KALMAN FILTERING OF T 2 M I. Rozinkina, S. Cheshin, M. Shatunova, I. Ruzanova Hydrometeorological Research Center of Russia
Kalman Filter The temperature T observed at the station at time t is represented as where Tp is the window width used for expanding the temperature forecasts (4 -7 days) and Np is the number of harmonics used (Tp multiplied by (1, 2, or 3)), The difference D between the observed temperature and the averaged forecast at time t is represented as where Td is the window width used for expanding D (1 day) and Nd is the number of harmonicas used (1, 2, or 3), The forecast at the time t is calculated using the formula COSMO General Meeting 2012, Lugano, September, 10 -13
Results Corrected 2 m temperature for Tp=7 days, Td=1 day, and various Np and Nd The 2 m temperature forecast at Krasnaya Polyana station was corrected over February, 2012 by applying the described method, The errors in the initial forecasts: average deviation: 2, 86 K root-mean-square deviation: 3, 89 K For Np=7 and Nd=1, the errors of the corrected forecasts: average deviation: 0, 18 K root-mean-square deviation: 2, 55 K For Np=14 and Nd=2, the errors of the corrected forecasts: average deviation: 0, 40 K root-mean-square deviation: 2, 3 K For Np=21 and Nd=3, the errors of the corrected forecasts: average deviation: 0, 39 K root-mean-square deviation: 2, 19 K • observed data • T 2 forecasts • revised T 2 forecast COSMO General Meeting 2012, Lugano, September, 10 -13
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology Meteo. Swiss Local forecasts with COSMO-MOS Concept, Performance and Implementation ECAC & EMS, September, 14 th 2010 COSMO-GM, Lugano, 10. 09. 2012 Vanessa Stauch
Objectives of statistical PP ü To complement the NWP forecasts with the information in observations ü To reduce systematic NWP forecast errors, e. g. due to simplified (small scale) processes, incorrect (smoothed) local forcing, … ü To calibrate (ensemble) forecasts such that they are reliable and sharp ü To derive forecasts for variables that are not predicted by the NWP model taken from Wilks 2005 COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 9
Dilemma global MOS Length of training period ~ “MOSMIX” + insensitive to model error„MOSMIX“: multiple linear regression based changes COSMO- simple error model, little MOS discrimination MOS complexity + sampling of many cases, Updateable good MOS discrimination → long lead times, rare events » correction mainly of the systematic errorupdate online “KF” “UMOS” - inert when model error changes on global NWP models “UMOS”: ‘updateable’ MOS of Canadians (and Austrians), weighting of model versions “KF”: Kalman Filter based online update of systematic error correction » reduction of the mean error and Temporal flexibility (e. g. its variability change of model version) COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 10
Implemented statistical approaches ü Multiple linear regression with stepwise forward model selection ü Logistic regression (returns probability of exceedance for one threshold q) ü Extended logistic regression (Wilks, 2009, returns entire probability distribution of forecast) COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 11
Data sampling & estimation strategies COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 12
10 m wind speed: setup comparison 01. 12. 2010 – 28. 02. 2011 COSMO-7 COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 13
10 m wind speed: setup comparison 01. 12. 2010 – 28. 02. 2011 COSMO-2 COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 14
Summary multiple linear regression ü MOS forecasts reduce forecast error variance and systematic error ü In comparison to Kalman filter approach, effect on the error variance much higher ü Comparison COSMO-2 with COSMO-7 shows positive effect of higher resolved (=better) inputs ü Recommendation for production setup: • training period: 50 days for temperature, 90 days for wind speed • daytime dependent coefficients, all runs. • update once a day COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 15
COSMO-MOS: Performance and recommendations RESULTS WITH EXTENDED LOGISTIC REGRESSION COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 16
Simulation setup 10 m wind gusts Verification period: 01. 09. 2010 – 02. 11. 2010 Hourly wind gust observations from the Swiss automatic measurement network (~70 stations used) Thresholds for estimation: 25, 50, 75 % quantiles COSMO-2 time lagged ensemble “eps”: median and std as predictors “lag”: all members separate predictors COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 17
Overall comparison CRPSS COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 18
Summary 10 m wind gusts ü Extended logistic regression is a suitable statistical model for deriving PDFs from deterministic model output ü COSMO-2 time lagged ensemble does contain useful ensemble information for statistical post-processing ü Leadtime dependency of “eps” approach apparent but might be alleviated with longer runs (→ COSMO NEx. T? ) ü Training periods need to be seasonal → maybe include more years in order to improve the distributions COSMO-MOS | COSMO-GM, 10. 09. 2012 Vanessa Stauch 19
IACETH Clear Air Turbulence over Europe: Climatology, Dynamics and Representation in COSMO-7 Masterthesis of Lysiane Mayoraz Supervised by Michael Sprenger and Vanessa Stauch
IACETH Turbulence indices: • TI 2 (Ellrod & Knapp Index 2) → deformation, shearing und divergence • RI (Gradient Richardson Number) → rate between the static stability and the vertical windshear. If RI < 1: instable • EDR (Eddy Dissipation Rate) → rate at which turbulent kinetic energy is converted into heat → Turbulent spot well visible with the three indices calculated from the COSMO-7 forecasts! → But signal too low (~ 1'000 m) 15/05/2012 Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz 21
IACETH Turbulence indices: • TI 2 (Ellrod & Knapp Index 2) → deformation, shearing und divergence • RI (Gradient Richardson Number) → rate between the static stability and the vertical windshear. If RI < 1: instable • EDR (Eddy Dissipation Rate) → rate at which turbulent kinetic energy is converted into heat Without extended turbulence parametrisation Extended turbulence parametrisation: brings a significant amelioration compared to the operational forecasts 15/05/2012 Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz 22
IACETH Observations Data Flight Data Monitoring Data from Swiss (year 2011) Selection criteria 50 turb. events (out of 100'000 flights) 15/05/2012 Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz 23
IACETH Comparison Observations / Model Results: All clear detected events are associated with a large and long-lasting event from the model! 15/05/2012 Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz 24
Check list «risk of thunderstorms» COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 25
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology Meteo. Swiss Guidelines http: //www. wmo. int/pages/prog/ www/manuals. html
• 2. WHY SHOULD WE USE EPS? • 3. TYPES OF EPS • 3. 1 Global EPS • 3. 2 Regional EPS • 3. 3 Convective-scale EPS • 6. USE OF EPS IN DETERMINISTIC FORECASTING • 6. 1 Decision-making from deterministic forecasts • • 7. SCENARIOS 8. FULL PROBABILISTIC FORECASTS 9. POST-PROCESSING 10. USE OF EPS IN PREDICTION OF SEVERE WEATHER AND ISSUE OF WARNINGS • 11. SEVERE WEATHER IMPACT MODELLING • 13. FORECASTER TRAINING COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 27
Plans Aviation • COSMO-MOS: visibility, ceiling, wind direction • Improve and operationalise CAT forecasts • Other applications First guess into forecast matrix • «Best» deterministic input temperature, wind, sunshine duration, precipitation, … • Estimates for probabilities (compatible with deterministic) Guidelines • Strenghts and weaknesses of the various models • Use of O(1 km) models, use of O(2 km) EPS Exchange of experiences and methods COSMO General meeting ¦ Lugano, September 2012 Pierre. Eckert[at]meteoswiss. ch 28
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