Multi-model multi-analysis ensembles in quasi-operational medium-range forecasting

Ensemble prediction systems (EPS) for medium-range forecasting attempt to account for uncertainty in numerical weather prediction (NWP) by sampling the distribution function of future atmospheric states. Forecast uncertainty derives from uncertainty in both the analysed initial conditions (analysis errors) and in the forecast evolution (model errors). Current operational systems are primarily based on sampling the analysis errors through initial-condition perturbations with, at best, only limited sampling of model errors. One approach to sampling model errors and also to widening the sampling of analysis errors, is to include more than one NWP model, and more than one operational analysis to which perturbations are added, in the ensemble system. Previous work has demonstrated from a small number of case-studies that this multi-model multi-analysis ensemble (MMAE) approach can perform significantly better than a single-model system such as the Ensemble Prediction System (EPS) run by the ECMWF (European Centre for Medium-Range Weather Forecasts). In this study a MMAE was created by combining the ECMWF ensemble with an ensemble using the Met Office model and analysis, and was run daily for a year to assess the benefits over a larger, quasi-operational sample of forecasts. The results are compared with the operational ECMWF EPS which includes the latest upgrades, including stochastic physics which makes some allowance for uncertainty due to model errors. Results show that both for probabilistic forecasts (assessed by Brier skill scores and relative operating characteristics) and for deterministic forecasts based on the ensemble mean (assessed by root-mean square errors) the MMAE has increased forecast skill relative to the EPS. These improvements are obtained with no overall increase in ensemble size. Ensemble spread is also greater in the MMAE, and the increased skill is believed to be due to the additional model producing solutions which are synoptically more different than those produced by a single model ensemble. Benefits of the MMAE vary both in time and with geographical region, depending on which individual ensemble system performs better in particular synoptic situations. It is found that the MMAE almost always performs as well as the best individual ensemble, and on occasions better than either of them.