eps training module 1: introduction richard verret (normand gagnon) meteorological service of canada...
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EPS Training
Module 1: Introduction
Richard Verret (Normand Gagnon)Meteorological Service of Canada
The illusion of determinism…
Module 1: Introduction – Page 2R. Verret –
Welcome to the EPS Training session
Module 1: Introduction – Page 5R. 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 7R. Verret –
48-h GEM regional 48-h GEM global 48-h NCEP-GFS
48-h NCEP-NAM 48-h UKMetO 48-h ECMWF
Context
M.-F. Turcotte, CMCMean sea level pressure – all valid at 12 UTC February 15 2007
Module 1: Introduction – Page 8R. 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 11R. Verret –
Models look quite realistic these days… HRDPS at 2.5 km
• http://iweb.cmc.ec.gc.ca/~afsgfau/LAM2.5km/NationalDomain/loop20_N1.gif
• http://iweb.cmc.ec.gc.ca/~afsgfau/LAM2.5km/NationalDomain/loop20_N2.gif
M. Faucher, CMC/CMDN
Module 1: Introduction – Page 12R. Verret –
T. Robinson, CMC
~10 years ~10 years
48-h gain in predictability in ~ 20 years
NWP is improving!
Module 1: Introduction – Page 13R. Verret –
Context
120-h integration – mean sea level pressureTwo integrations done with identical NWP models but on different computers
M. Lajoie, CMC
Module 1: Introduction – Page 14R. Verret –
Context
Precipitation amountdifference > 45mm
MSL pressure
Bit flipping experiment
N. Gagnon, CMC
240-h forecast
Module 1: Introduction – Page 15R. 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 16R. 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 17R. Verret –
ContextInitial states Final states
True initial stateTrue final state
Climatology
Ensemble mean
Analysis
Deterministicforecast
Uncertainty oninitial state
R. Verret, N. Gagnon, CMC
Module 1: Introduction – Page 18R. 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 19R. 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 24R. 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 26R. 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 27R. Verret –
Training proposed schedule8:15 – 8:45 Module 1 - Introduction
8:45 – 10:00 Module 2 – Probabilistic Forecasts
10:00 – 10:30 Break
10:30 - 11:30 Module 3 – EPS Basic Concepts
11:30 – 12:30 Lunch
12:30 – 13:30 Module 4 – EPS Construction
13:30 – 13:45 Break
13:45 – 14:45 Module 5 – EPS Products
14:45 - 15:00 Break
15:00 – 16:00 Module 6 – Application, Web Sites
16:00 - 16:15 Module 7 – Future / Conclusion
Module 1: Introduction – Page 35R. Verret –
Conclusions
Uncertainty is the only certainty.
Module 1: Introduction – Page 36R. Verret –
Questions?