EPS Training Module 1: Introduction Richard Verret (Normand Gagnon) Meteorological Service of Canada The illusion of determinism…

Module 1: Introduction – Page 2 R. Verret – Welcome to the EPS Training session

Module 1: Introduction – Page 3 R. Verret – Welcome to the EPS Training session What do you expect of this workshop on Ensemble Prediction System?

Module 1: Introduction – Page 4 R. Verret – Outline of module 1 Context. Scope of the training session. Training session schedule. Expected results. Quick overview of the different modules. The illusion of determinism…

Module 1: Introduction – Page 5 R. Verret – Context Ensemble prediction system (EPS) have been around for a long time: –An Ensemble forecast can be thought of as a collection of two or more Numerical Weather Prediction (NWP) model forecasts verifying at the same time: ▪Helps to gain a feel for possibilities of pattern evolution. ▪Helps to partially gage confidence in a particular model solution.

Module 1: Introduction – Page 6 R. Verret – Context 48-h GEM-Regional48-h GEM-Global48-h NCEP-GFS 48-h NCEP-NAM 48-h UKMetO 48-h ECMWF R. Sauvageau, CMC Mean sea level pressure – all valid at 00 UTC November

Module 1: Introduction – Page 7 R. Verret – 48-h GEM regional48-h GEM global48-h NCEP-GFS 48-h NCEP-NAM48-h UKMetO48-h ECMWF Context M.-F. Turcotte, CMC Mean sea level pressure – all valid at 12 UTC February

Module 1: Introduction – Page 8 R. Verret – Context Forecasts are usually generated within the deterministic paradigm – one scenario is selected. Determinism is favoured by: –Heritage of the past. –Improvements of high resolution Numerical Weather Prediction (NWP) models. –Satellite and radar (and other) remote sensing technologies. >>> Over-confidence in NWP models <<< Clients are not necessarily prepared to use probabilistic forecasts or measures of forecast uncertainty: –Training is required both on the users side and on the forecasters’ side.

Module 1: Introduction – Page 9 R. Verret – Context S. Bélair, RPN

Module 1: Introduction – Page 10 R. Verret – Context S. Bélair, RPN EPS-M.1-Introduction.ppt

Module 1: Introduction – Page 11 R. Verret – Models look quite realistic these days… HRDPS at 2.5 km main/loop20_N1.gif main/loop20_N1.gif main/loop20_N2.gif main/loop20_N2.gif M. Faucher, CMC/CMDN

Module 1: Introduction – Page 12 R. Verret – T. Robinson, CMC ~10 years 48-h gain in predictability in ~ 20 years NWP is improving!

Module 1: Introduction – Page 13 R. Verret – Context 120-h integration – mean sea level pressure Two integrations done with identical NWP models but on different computers M. Lajoie, CMC

Module 1: Introduction – Page 14 R. Verret – Context Precipitation amount difference > 45mm MSL pressure Bit flipping experiment N. Gagnon, CMC 240-h forecast

Module 1: Introduction – Page 15 R. Verret – Context Ensemble forecasts have evolved significantly over the past years: –Systematic approach to model uncertainty. –Perturbations as simulation of uncertainty. –Better simulation of uncertainties in forecast processes. –Increasing number of members. –Increasing resolution of members. With Ensemble forecast, it is possible to evaluate, express and forecast uncertainty.

Module 1: Introduction – Page 16 R. Verret – Context An Ensemble Prediction System is a set of integrations of one or several NWP models that differ in their initial states (and sometimes in their configurations and boundary conditions). Ensemble prediction is an attempt to estimate the non-linear time evolution of the forecast error probability distribution function. Ensemble prediction is a potential method of estimating forecast predictability beyond the range in which error growth can be described by linearized dynamics.

Module 1: Introduction – Page 17 R. Verret – Context Initial states Final states True initial state True final state Climatology Ensemble mean Analysis Deterministic forecast Uncertainty on initial state R. Verret, N. Gagnon, CMC

Module 1: Introduction – Page 18 R. Verret – Context Common usages of Ensemble forecasts: –Ensemble mean as a substitute for a single deterministic forecast. –Clustering to produce a small set of forecast states characterized with the cluster mean. –A priori prediction of forecast skill. –Ensemble probability distribution function. –Measure of uncertainty. –Extension of forecast range.

Module 1: Introduction – Page 19 R. Verret – Context There is an important research effort on EPS around the world: –Research done at most EPS producing Centers. –THORPEX (THe Observing system Research and Predictability EXperiment). –NAEFS (North American Ensemble Forecast System). There is an important effort devoted to the usage of EPS: –At each EPS producing Centers. –NAEFS.

Module 1: Introduction – Page 20 R. Verret – Context Scope of this workshop on EPS: –Introduce participants to Ensemble forecasting. –Provide basic training on Ensemble Prediction Systems for operational forecasters. –Move away from the deterministic paradigm toward a probabilistic paradigm - estimating and expressing uncertainty. Uncertainty is a fundamental characteristic of weather forecasts and no forecast is complete without a description of its uncertainty.

Module 1: Introduction – Page 21 R. Verret – Session - schedule Session begins at 9:00am Session finishes around 4:30pm. Lunch ~ 11:30 – 12:30. Health-breaks – 1 in the morning. – 2 in the afternoon. 7 modules –Theorie: morning + part of the afternoon. –Case studies. –Future. –Conclusions.

Module 1: Introduction – Page 22 R. Verret – Session schedule Day 1 8:00 AM – 8:45 AM Module 1: Introduction The illusion of determinism… 8:45 AM – 10:00 AM Module 2: Probabilistic forecasts Measuring the odds… 10:00 AM– 10:15 AM Health break 10:15 AM– 11:30 AM Module 2: Probabilistic forecasts Measuring theodds… 11:30 AM– 12:30 PM Lunch 12:30 PM - 2:00 PM Module 3: EPS basic concepts Uncertainty is part of forecasting… 2:00 PM – 2:15 PM Health break 2:15 PM – 4:00 PM Module 4: EPS construction Modeling uncertainty…

Module 1: Introduction – Page 23 R. Verret – Session schedule Day 2 8:00 AM – 10:00 AM Module 5: Products and usage Visualizing uncertainty… 10:00 AM– 10:15 AM Health break 10:15 AM– 11:45 AM Module 6: Applications Putting it all into practice… 11:45 AM– 12:45 PM Lunch 12:45 PM – 1:15 PM Module 6: Applications Putting it all into practice… 1:15 PM – 2:00 PM Quiz 2:00 PM – 2:15 PM Health Break 2:15 PM – 3:00 PM Module 7: Future And what is next…

Module 1: Introduction – Page 24 R. Verret – Expected results Shift from a deterministic paradigm toward one where uncertainty is part of forecasts: –EPS can provide flow-dependent predictive probability distribution for future weather quantities or events. –Probabilistic forecasts allow one to quantify weather-related risks and show greater economic value than deterministic forecasts. –Ensemble forecasts are not meant to be a consensus technique.

Module 1: Introduction – Page 25 R. Verret – Expected results Warning: –It is the first training workshop on Ensemble Prediction Systems – likely to be followed by several others. –It will not be possible to answer all questions. –In view of the current workload, it is not clear how to mesh EPS into the operational forecast process – it will be a slow process. –The usage of EPS will find its way as: ▪New products are developed. ▪Forecast beyond day 5 is produced using the EPS. ▪The paradigm shifts toward a probabilistic one.

Module 1: Introduction – Page 26 R. Verret – Expected results Overall result: –Develop a motivation to use EPS outputs/products. Module 2 – probabilistic forecasts: –Understanding of basic concepts in probability. Module 3 – EPS basic concepts: –Understanding of basic concepts in Ensemble forecasting. Module 4 – EPS construction: –Basic understanding of how EPS are constructed. Module 5 – EPS products and usage: –Know how to access and use EPS products. Module 6 – EPS application: –Know how to apply EPS in the forecast process.

Module 1: Introduction – Page 27 R. Verret – Training proposed schedule 8:15 – 8:45Module 1 - Introduction 8:45 – 10:00Module 2 – Probabilistic Forecasts 10:00 – 10:30Break 10: :30Module 3 – EPS Basic Concepts 11:30 – 12:30Lunch 12:30 – 13:30Module 4 – EPS Construction 13:30 – 13:45Break 13:45 – 14:45Module 5 – EPS Products 14: :00Break 15:00 – 16:00Module 6 – Application, Web Sites 16: :15Module 7 – Future / Conclusion

Module 1: Introduction – Page 28 R. Verret – Module 2 – probabilistic forecasts Deterministic forecasts and uncertainty. Definition of probabilities. Distributions: –Frequency distributions. –Measures of central tendency. –Measures of dispersion. –Measures of shapes. –Statistics of probability distributions. –Probability density functions. –Cumulative density functions. –Distributions used in meteorology. –Joint distributions. Probabilistic forecasts –Probability rules. –Bayes’ theorem. –Attributes of probability forecasts. ▪Brier Score. –Advantages of probability forecasts. Probabilities by statistical adaptation. Cost/loss model. Exercises: –Wind mill energy production. –Road salting. Conclusions. Measuring the odds…

Module 1: Introduction – Page 29 R. Verret – Module 3 – EPS basic concepts Principles of numerical weather prediction (NWP). –Basic concepts. –Data assimilation. –NWP modelling. –GEM-global model. –GEM-regional model. –GEM-mesoscale model. Sources of error in NWP. –Initial conditions. –Model errors. –Boundary conditions. –Dependency on initial conditions – chaos. EPS basic concepts. EPS and probabilistic forecasts. –Spread-skill relationship. –Probabilities ▪Member count. ▪Calibration. ▪Clustering. ▪PDF. –Value of EPS. Conclusions. Uncertainty is part of forecasting…

Module 1: Introduction – Page 30 R. Verret – Module 4 – EPS Construction Ad hoc ensemble. Perturbations: –Ensemble Kalman Filters. –Breeding Vectors. –Singular Vectors. –Comparisons. NAEFS: –Principles. –Multi-model ensemble. –Calibration. Conclusions. Modeling uncertainty…

Module 1: Introduction – Page 31 R. Verret – Module 5 – EPS products and usage Possibilities and limitation. Basic products. –Mean and standard deviation. –Spaghetti charts. –EPS-grams. Probabilistic products. –Probability of exceedance. –Bayesian Model Averaging. NAEFS. –Bias correction. –Week-2 product. Extending forecasts beyond day 5. Conclusions. Visualising uncertainty…

Module 1: Introduction – Page 32 R. Verret – Module 6 – Applications General guidelines. Improvement of deterministic forecasts: –Gain in predictability. –Confidence. –Alternative scenarios. –Indication of potential for extreme events. Probabilistic forecasts: –Uncertainty. –Risk analysis and decision making. Putting it all into practice…

Module 1: Introduction – Page 33 R. Verret – Module 7 – Future So far… Future plans for Canadian EPS. Future plans for NAEFS. Regional ensembles. Conclusions. Contributions. References. And what is next…

Module 1: Introduction – Page 34 R. Verret – Probabilities of event occurrences. Risk calculation. Decision making based on probabilities and cost/loss ratio. The spread-skill relationship can be used to assess forecast confidence. Forecast is incomplete without information on expected flow dependent skill. Evaluation of possible scenarios. EPS outputs  downscaling  application models Construction of pdf from a finite ensemble. Can be used to extend forecast range beyond day 5. Conclusions

Module 1: Introduction – Page 35 R. Verret – Conclusions Uncertainty is the only certainty.

Module 1: Introduction – Page 36 R. Verret – Questions?