Potential use of the ECMWF Ensemble Prediction System in cases of extreme weather events

The combined use of the European Centre for Medium-range Weather Forecasts (ECMWF) high-resolution operational model, at T213 spectral triangular truncation and with 31 vertical levels (T213L31), and the Ensemble Prediction System (EPS), during cases of intense Mediterranean storms, is studied. In particular, it is discussed how EPS products can be used to provide a measure of confidence in the high-resolution precipitation forecast. Three case studies (two extreme events plus one false-alarm case) are analysed. The first event took place between 21 and 22 October 1994 over Greece, where heavy rainfall led to local flash-floods in many areas that cost the loss of 12 lives, and caused significant property damage. The second event occurred in northwest Italy (and some parts of southern France) exactly two weeks later. More than 60 lives were lost and the cost of damage was enormous. For both these cases, the EPS probability values for precipitation occurrence supported the medium-range T213L31 prediction, which proved to be successful. A third case is also investigated, where the high-resolution forecast suggested heavy rainfall over northern Italy but was not supported by the EPS. The T213L31 prediction for this case was poor. Finally, the reliability of EPS probability predictions is discussed. It is shown that probability forecasts of low-level temperature and wind show considerable skill. Reliability statistics of clusters of large-scale flow show considerable skill as well. Probabilistic precipitation predictions are less skilful, possibly due to the EPS model resolution. Copyright © 1997 Royal Meteorological Society.

[1]  E. Epstein,et al.  Stochastic dynamic prediction , 1969 .

[2]  A. Simmons,et al.  The ECMWF medium-range prediction models development of the numerical formulations and the impact of increased resolution , 1989 .

[3]  Tim N. Palmer,et al.  Extended‐range predictions with ECMWF models: Time‐lagged ensemble forecasting , 1990 .

[4]  Timothy W. Barker,et al.  The Relationship between Spread and Forecast Error in Extended-range Forecasts , 1991 .

[5]  R. Buizza.  Potential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System , 1997 .

[6]  A. H. Murphy,et al.  Diagnostic verification of probability forecasts , 1992 .

[7]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[8]  E. Epstein,et al.  Stochastic dynamic prediction1 , 1969 .

[9]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[10]  Rex J. Fleming,et al.  ON STOCHASTIC DYNAMIC PREDICTION: I. The Energetics of Uncertainty and the Question of Closure , 1971 .

[11]  Rex J. Fleming,et al.  ON STOCHASTIC DYNAMIC PREDICTION , 1971 .

[12]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[13]  James M. Murphy,et al.  The impact of ensemble forecasts on predictability , 1988 .

[14]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

[15]  A. H. Murphy,et al.  The attributes diagram A geometrical framework for assessing the quality of probability forecasts , 1986 .

[16]  Edward S. Epstein,et al.  A Scoring System for Probability Forecasts of Ranked Categories , 1969 .

[17]  Thomas A. Gleeson.  Statistical-Dynamical Predictions , 1970 .