SUMMARYThe European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) is described. In addition to an unperturbed (control) forecast, each ensemble comprises 32 10-day forecasts starting from initial conditions in which dynamically defined perturbations have been added to the operational analysis. The perturbations are constructed from singular vectors of a time-evolution operator linearized around the short-rangeforecast trajectory. These singular vectors approximately determine the most unstable phase-space directions in the early part of the forecast period, and are estimated using a forward and adjoint linear version of the ECMWF numerical weather-prediction model. An appropriate norm is chosen, and relationships between the structures of these singular vectors at initial time and patterns showing the sensitivity of short-range forecast error to changes in the analysis are discussed. A methodology to perform a phase-space rotation of the singular vectors is described, which generates hemispheric-wide perturbations and renormalizes them according to analysis-error estimates from the data-assimilation system.The validation of the ensembles is given firstly in terms of scatter diagrams and contingency tables of ensemble spread and control-forecast skill. The contingency tables are compared with those from a perfect-model ensemble system; no significant differences are found in some cases. Brier scores for the probability of European flow clusters are presented, which indicate predictive skill up to forecast-day 8 with respect to climatological probabilities. The dependence of these scores on flow-dependent model errors is also discussed. Finally, ensemble-member skillscore distributions are presented, which confirm the overall satisfactory performance of the EPS, particularly in summer and autumn 1993. In winter, cases of poor performance over Europe were associated with the Occurrence of a split westerly flow with a blocking high and/or a cut-off low in the verifying analysis.' h o cases are studied in detail, one having large ensemble dispersion, the other corresponding to a more predictable situation. The case studies are used to illustrate the range of ensemble products routinely disseminated to ECMWF Member States. These products include clusters of flow types, and probability fields of weather elements.

A stochastic representation of random error associated with parametrized physical processes (‘stochastic physics’) is described, and its impact in the European Centre for Medium‐Range Weather Forecasts Ensemble Prediction System (ECMWF EPS) is discussed. Model random errors associated with physical parametrizations are simulated by multiplying the total parametrized tendencies by a random number sampled from a uniform distribution between 0.5 and 1.5. A number of diagnostics are described and a choice of parameters is made. It is shown how the scheme increases the spread of the ensemble, and improves the skill of the probabilistic prediction of weather parameters such as precipitation. A choice of stochastic parameters is made for operational implementation. the scheme was implemented successfully in the operational ECMWF EPS on 21 October 1998.

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The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques.

SUMMARYA stochastic representation of random error associated with parametrized physical processes ('stochastic physics') is described, and its impact in the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF EPS) is discussed. Model random errors associated with physical parametrizations are simulated by multiplying the total parametrized tendencies by a random number sampled from a uniform distribution between 0.5 and 1.5. A number of diagnostics are described and a choice of parameters is made. It is shown how the scheme increases the spread of the ensemble, and improves the skill of the probabilistic prediction of weather parameters such as precipitation. A choice of stochastic parameters is made for operational implementation. The scheme was implemented successfully in the operational ECMWF EPS on 21 October 1998.