A Synoptic Evaluation of the NCEP Ensemble

Ensemble forecasting has been operational at NCEP (formerly the National Meteorological Center) since December 1992. In March 1994, more ensemble forecast members were added. In the new configuration, 17 forecasts with the NCEP global model are run every day, out to 16-day lead time. Beyond the 3 control forecasts (a T126 and a T62 resolution control at 0000 UTC and a T126 control at 1200 UTC), 14 perturbed forecasts are made at the reduced T62 resolution. Global products from the ensemble forecasts are available from NCEP via anonymous FTP. The initial perturbation vectors are derived from seven independent breeding cycles, where the fast-growing nonlinear perturbations grow freely, apart from the periodic rescaling that keeps their magnitude compatible with the estimated uncertainty within the control analysis. The breeding process is an integral part of the extended-range forecasts, and the generation of the initial perturbations for the ensemble is done at no computational cost beyond that of running the forecasts. A number of graphical forecast products derived from the ensemble are available to the users, including forecasters at the Hydrometeorological Prediction Center and the Climate Prediction Center of NCEP. The products include the ensemble and cluster means, standard deviations, and probabilities of different events. One of the most widely used products is the “spaghetti” diagram where a single map contains all 17 ensemble forecasts, as depicted by a selected contour level of a field, for example, 5520 m at 500-hPa height or 50 m s−1 windspeed at the jet level. With the aid of the above graphical displays and also by objective verification, the authors have established that the ensemble can provide valuable information for both the short and extended range. In particular, the ensemble can indicate potential problems with the high-resolution control that occurs on rare occasions in the short range. Most of the time, the “cloud” of the ensemble encompasses the verification, thus providing a set of alternate possible scenarios beyond that of the control. Moreover, the ensemble provides a more consistent outlook for the future. While consecutive control forecasts verifying on a particular date may often display large “jumps” from one day to the next, the ensemble changes much less, and its envelope of solutions typically remains unchanged. In addition, the ensemble extends the practical limit of weather forecasting by about a day. For example, significant new weather systems (blocking, extratropical cyclones, etc.) are usually detected by some ensemble members a day earlier than by the high-resolution control. Similarly, the ensemble mean improves forecast skill by a day or more in the medium to extended range, with respect to the skill of the control. The ensemble is also useful in pointing out areas and times where the spread within the ensemble is high and consequently low skill can be expected and, conversely, those cases in which forecasters can make a confident extended-range forecast because the low ensemble spread indicates high predictability. Another possible application of the ensemble is identifying potential model errors. A case of low ensemble spread with all forecasts verifying poorly may be an indication of model bias. The advantage of the ensemble approach is that it can potentially indicate a systematic bias even for a single case, while studies using only a control forecast need to average many cases.