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Introduction to Ensemble Prediction Systems (EPS)

Introduction to Ensemble Prediction Systems (EPS). José A. García-Moya & Carlos Santos AAPL-AEMET Eumetcal NWP Applications Course November 2009. Outline. How to introduce in operational forecasting. Uncertainties of deterministic models. Models to make probabilistic prediction, PDF.

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Introduction to Ensemble Prediction Systems (EPS)

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  1. Introduction to Ensemble Prediction Systems (EPS) José A. García-Moya & Carlos Santos AAPL-AEMET Eumetcal NWP Applications Course November 2009

  2. Outline • How to introduce in operational forecasting. • Uncertainties of deterministic models. • Models to make probabilistic prediction, PDF. • Predictability, spread and skill. • Extreme events. • ¿What is an EPS? • ECMWF’s EPS • Singular vectors • Stochastic Physics • Conclusions Eumetcal NWP Applications Course - Chaos & Predictability

  3. What is a perfect forecast? Ideal forecaster Incomplete knowledge of the atmosphere Perfect forecast Eumetcal NWP Applications Course - Chaos & Predictability

  4. How to introduce in operational forecasting Added value forecaster Forecast and Direct Model Output Partial knowledge of the state of the atmosphere Eumetcal NWP Applications Course - Chaos & Predictability

  5. Deterministic NWP Models • Deterministic Model • PDEs Single set of Initial Conditions Single Prediction Eumetcal NWP Applications Course - Chaos & Predictability

  6. Improvement of skill of NWP deterministic models • DA + more obs • Better model formulation Better deterministic models ++ Skill ++ Increased forecast length 5  7d Courtesy of B. Elvira Eumetcal NWP Applications Course - Chaos & Predictability

  7. Limited performance of deterministic models But there are still model failures D+3..D+7 (depending on weather regime) Why? Investigation of the sources of error in NWP models Getting deeper in theory Lorenz’s Model Eumetcal NWP Applications Course - Chaos & Predictability

  8. Sources of uncertainty Eumetcal NWP Applications Course - Chaos & Predictability

  9. Lorenz’s Strange Attractor – Phase Space X X = one point in the “butterfly”  a single state of the atmosphere All the X = the whole “butterfly”  the ensemble of all possible states of the atmosphere Eumetcal NWP Applications Course - Chaos & Predictability

  10. Probabilistic Prediction Models Single input = PDF Single output = PDF Eumetcal NWP Applications Course - Chaos & Predictability

  11. Time evolution of the PDF Eumetcal NWP Applications Course - Chaos & Predictability

  12. PDF: Single place time evolution Eumetcal NWP Applications Course - Chaos & Predictability

  13. The problem of predictability in a nonlinear system The atmosphere is a nonlinear system Predictability in function of time Sensitivity to the initial conditions Evolution of PDF and spread Courtesy of T. Palmer Eumetcal NWP Applications Course - Chaos & Predictability

  14. Spread & skill Spread Skill These factors are a key aspect of EPS. There should be a linear relationship between them. Calibration of the EPS Courtesy of T. Palmer Eumetcal NWP Applications Course - Chaos & Predictability

  15. Pepe’s dilemma Pepe has a large orange ground in Valencia (Spain) If T2m falls down below 0 C deg oranges will frozen and Pepe will loose money. Pepe has several heating machines in the ground. If he switch on them oranges will not frozen but it costs some money. P is the probability of frost, C costs and L loses. Is C > Lp? If p > C/L then he must use the machines. See the butterfly… Courtesy of T. Palmer Eumetcal NWP Applications Course - Chaos & Predictability

  16. ECMWF EPS forecast Forecaster based on the distribution of EPS members will make a frost-free weather But PDF had no spread enough and the verification was… “The large the spread the small the skill” Courtesy of T. Palmer Eumetcal NWP Applications Course - Chaos & Predictability

  17. Goals of the EPS To find an estimation of the weather PDF. To investigate the effects of the different sources of errors of the NWP models. To identify area with low predictability To calibrate the flow dependent predictability To compute the probability of different weather scenarios Eumetcal NWP Applications Course - Chaos & Predictability

  18. What is an EPS? • Ensemble Prediction System allows a probabilistic approach to weather forecasting • Using different methods an ensemble of forecasts for the same place is computed. Every of these forecasts is called “member” of the EPS. All together is: • A PDF of the weather situation. • It allows to see slightly different weather scenarios all of them with the same probability to be the best one. • Give us back information about the predictability to the weather situation. Eumetcal NWP Applications Course - Chaos & Predictability

  19. What is an EPS? Eumetcal NWP Applications Course - Chaos & Predictability

  20. How to simulate uncertainty? Perturbations in the initial conditions Perturbations in the model physics Eumetcal NWP Applications Course - Chaos & Predictability

  21. Post-process PDF’S EFI Clusters Plumes Stamps Tropical cyclone tracks Eumetcal NWP Applications Course - Chaos & Predictability

  22. Summary • Direct EPS output • Stamps • Plumes • Control vs Operational • Spaguetis • PDF’s • Ensemble mean and Spread • Probability maps • EPSgrams • Special post-process • Groups • Tubes • EFI • Tropical cyclones • What is new? • Can we use EPS in a deterministic way? • Quality and value of EPS • Conclusions Eumetcal NWP Applications Course - Chaos & Predictability

  23. Stamps High resolution operatinal 1 Control 50 members Eumetcal NWP Applications Course - Chaos & Predictability

  24. Plumes • Plots of variables vs. forecast length at one concrete place. • There is a line for every ensemble member and colours for probabilities. • It can be easily seen the dispersion of the EPS in that place. • Predictability can be measured in function on that dispersion. Eumetcal NWP Applications Course - Chaos & Predictability

  25. Control vs Operational (AEMET) • Z500T500 • PsfcT850 • Pcp24h Eumetcal NWP Applications Course - Chaos & Predictability

  26. Summary • Direct EPS output • Stamps • Plumes • Control vs Operational • Spaguetis • PDF’s • Ensemble mean and Spread • Probability maps • EPSgrams • Special post-process • Groups • Tubes • EFI • Tropical cyclones • What is new? • Can we use EPS in a deterministic way? • Quality and value of EPS • Conclusions Eumetcal NWP Applications Course - Chaos & Predictability

  27. Ensemble mean and spread Z1000 Martin Storm Eumetcal NWP Applications Course - Chaos & Predictability

  28. Probability maps Eumetcal NWP Applications Course - Chaos & Predictability

  29. EPSgrams • Interpolation of EPS in a place • Variables: • Cloudiness • Precipitation /6h • 10m wind speed • 2m temperature • Time evolution (D+1...D+10)/6h: • Box-Plot (min-p10-p25-p50-p75-p90-max): PDF using EPS 50 members • Red: control • Blue: high resolution operational model Eumetcal NWP Applications Course - Chaos & Predictability

  30. Clustering • To make information more understandable for the forecaster we can join members of EPS with “similar evolution” in a certain norm. • We can choose cluster mean as a representative forecast of each cluster • Clustering (Ward’s algorithm): • Similarity = diference RMS in Z500 para H+120..H+168  it guarantees “synoptic continuity” • # clusters depends on three factors: • Spread of the day, • RMS threshold, • multimodal degree • Tubing • Focusing on extreme more than in centroid • Trying to get information on extreme weather • Every method has advantages and disadvantages. Eumetcal NWP Applications Course - Chaos & Predictability

  31. Grups: Centroid (I) Eumetcal NWP Applications Course - Chaos & Predictability

  32. EFI 2mT Productos derivados: índice de predicción extrema (EFI) Eumetcal NWP Applications Course - Chaos & Predictability

  33. What is new? • EPS is a probabilistic forecasting system. • EPS spread can represent the uncertainty of the weather situation. This information cannot be get from a deterministic model. • NOT new • Forecasters adjust the forecast according their experience on deterministic models errors (flow dependent errors, forecast length) • The inconsistency of different model runs has been used as a sign of uncertainty of the forecast. • NEW • EPS gives an explicit representation of the uncertainty and they can represent the probability of extreme events. Eumetcal NWP Applications Course - Chaos & Predictability

  34. EPS in Dutch TV Cortesía de Robert Mureau, KNMI Eumetcal NWP Applications Course - Chaos & Predictability

  35. Questions? • csantos@inm.es • j.garciamoya@inm.es Eumetcal NWP Applications Course - Chaos & Predictability

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