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WG4: interpretation and applications overview Pierre Eckert MeteoSwiss, Geneva

This presentation discusses the use of statistical methods for improving weather forecasts, including Kalman filtering and logistic regression. Results show a reduction in forecast errors and improved reliability.

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WG4: interpretation and applications overview Pierre Eckert MeteoSwiss, Geneva

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  1. WG4: interpretation and applicationsoverviewPierre EckertMeteoSwiss, Geneva

  2. Topics • Sochi Olympic games  PP CORSO • FIELDEXTRA  presentation by JM Bettems • Postprocessing • CORSO Kalman filter • COSMO-MOS • CAT diagnostics • Use of chekclist • Guidelines • Plans

  3. Priority project CORSO • Task 1: implementation of high resolution model • Task 2: postprocessing and usability • Task 3: development of EPS

  4. Priority project CORSO • Task 1: implementation of high resolution model • Task 2: postprocessing and usability • Task 3: development of EPS

  5. Result from Kalman Filtering of T2m I.Rozinkina, S.Cheshin, M.Shatunova, I. Ruzanova Hydrometeorological Research Center of Russia

  6. 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

  7. Results Corrected 2m temperature for Tp=7 days, Td=1 day, and various Np and Nd The 2m temperature forecast at KrasnayaPolyana 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 • T2forecasts • revised T2 forecast COSMO General Meeting 2012, Lugano, September, 10-13

  8. Local forecasts with COSMO-MOS Concept, Performance and Implementation COSMO-GM, Lugano, 10.09.2012 Vanessa Stauch ECAC & EMS, September, 14th 2010

  9. Objectives of statistical PP • To complement the NWP forecasts with the information in observations • To reduce systematic NWP forecast errors, e.g. due to simplified (small scale) processes, incorrect (smoothed) local forcing, … • To calibrate (ensemble) forecasts such that they are reliable and sharp • To derive forecasts for variables that are not predicted by the NWP model taken from Wilks 2005

  10. Dilemma global MOS “MOSMIX” Length of training period ~ MOS complexity + insensitive to model error changes - simple error model, little discrimination » correction mainly of the systematic error „MOSMIX“: multiple linear regression based 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 COSMO-MOS online update “KF” Updateable MOS “UMOS” + sampling of many cases, good discrimination → long lead times, rare events - inert when model error changes » reduction of the mean error and its variability Temporal flexibility (e.g. change of model version)

  11. Implemented statistical approaches • Multiple linear regression with stepwise forward model selection • Logistic regression (returns probability of exceedance for one threshold q) • Extended logistic regression (Wilks, 2009, returns entire probability distribution of forecast)

  12. Data sampling & estimation strategies

  13. 10m wind speed: setup comparison 01.12.2010 – 28.02.2011 COSMO-7

  14. 10m wind speed: setup comparison 01.12.2010 – 28.02.2011 COSMO-2

  15. Summary multiple linear regression • MOS forecasts reduce forecast error variance and systematic error • In comparison to Kalman filter approach, effect on the error variance much higher • Comparison COSMO-2 with COSMO-7 shows positive effect of higher resolved (=better) inputs • Recommendation for production setup: • training period: 50 days for temperature, 90 days for wind speed • daytime dependent coefficients, all runs. • update once a day

  16. COSMO-MOS: Performance and recommendations RESULTS WITH EXTENDED LOGISTIC REGRESSION

  17. Simulation setup 10m 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

  18. Overall comparison CRPSS

  19. Summary 10m wind gusts • Extended logistic regression is a suitable statistical model for deriving PDFs from deterministic model output • COSMO-2 time lagged ensemble does contain useful ensemble information for statistical post-processing • Leadtime dependency of “eps” approach apparent but might be alleviated with longer runs (→ COSMO NExT?) • Training periods need to be seasonal → maybe include more years in order to improve the distributions

  20. Clear Air Turbulence over Europe: Climatology, Dynamics and Representation in COSMO-7 Masterthesis of Lysiane Mayoraz Supervised by Michael Sprenger and Vanessa Stauch IACETH

  21. Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz • Turbulence indices: • TI2(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)

  22. Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz • Turbulence indices: • TI2(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

  23. Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz Observations Data Flight Data Monitoring Data from Swiss (year 2011) Selection criteria  50 turb. events (out of 100'000 flights)

  24. Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz Comparison Observations / Model • Results: • All clear detected events are associated with a large and long-lasting event from the model!

  25. Check list «risk of thunderstorms»

  26. Guidelines http://www.wmo.int/pages/prog/www/manuals.html

  27. 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

  28. Plans Aviation • COSMO-MOS: visibility, ceiling, wind direction • Improve and operationalise CAT forecasts • Other applications First guessintoforecast matrix • «Best» deterministic input temperature, wind, sunshine duration, precipitation,… • Estimates for probabilities (compatible withdeterministic) Guidelines • Strenghts and weaknesses of the variousmodels • Use of O(1km) models, use of O(2km) EPS Exchange of experiences and methods

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