Benefits of increased resolution in the ECMWF ensemble system and comparison with poor‐man's ensembles

In November 2000 the resolution of the forecast model in the operational European Centre for Medium-Range Weather Forecasts Ensemble Prediction System was increased from a 120 km truncation scale (EPS) to an 80 km truncation scale (High-resolution EPS or HEPS). The HEPS performance is compared with that of EPS and with different flavours of poor-man's ensembles. Average results based on Brier skill scores and the potential economic value of probabilistic predictions for 57 winter and 30 summer cases indicate that the new HEPS system is about 12 hours more skilful than the old EPS. Averages over 39 winter cases indicate that HEPS forecasts perform better than five-centre ensemble forecasts. Results also show that if forecasts are transformed into parametrized Gaussian distribution functions centred on the bias-corrected ensemble mean and with re-scaled standard deviation, HEPS-based parametrized forecasts outperform all other configurations. Diagnostics based on parametrized forecast probabilities indicate that the different impact on the probabilistic or deterministic forecast skill is related to the fact that HEPS better represents the daily variation in the uncertainty of the atmosphere, and is not simply a reflection of improved mean bias or of a better level of spread. Copyright © 2003 Royal Meteorological Society

[1]  A. H. Murphy,et al.  The Value of Climatological, Categorical and Probabilistic Forecasts in the Cost-Loss Ratio Situation , 1977 .

[2]  C. Ziehmann.  Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models , 2000 .

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

[4]  David S. Richardson,et al.  Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size , 2001 .

[5]  Mats Hamrud,et al.  Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System , 1998 .

[6]  F. Atger,et al.  The Skill of Ensemble Prediction Systems , 1999 .

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

[8]  L. Bosart,et al.  The Complex Relationship between Forecast Skill and Forecast Value: A Real-World Analysis , 1996, Weather and Forecasting.

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

[10]  Roberto Buizza,et al.  The Singular-Vector Structure of the Atmospheric Global Circulation , 1995 .

[11]  A. Hollingsworth,et al.  Storm prediction over Europe using the ECMWF Ensemble Prediction System , 2002 .

[12]  D. Richardson.  Skill and relative economic value of the ECMWF ensemble prediction system , 2000 .

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

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

[15]  Roberto Buizza,et al.  3D‐Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system , 1999 .

[16]  Allan H. Murphy,et al.  Anders Ångström and His Early Papers on Probability Forecasting and the Use/Value of Weather Forecasts , 1994 .

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

[18]  Roberto Buizza,et al.  Sensitivity Analysis of Forecast Errors and the Construction of Optimal Perturbations Using Singular Vectors , 1998 .

[19]  M. Iredell,et al.  DOES INCREASED MODEL RESOLUTION ENHANCE PREDICTABILITY ? , 2001 .