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M. Steven Tracton

Abstract

Verification scores are presented to illustrate the general success of NMC forecasters in providing the best day 3,4, and 5 mean sea level pressure and 6–10-day mean 500-mb height fields given the operationally available array of often conflicting NWP model solutions. As a primer on NMC efforts to enhance the utility of the medium-range forecast guidance, a brief overview is provided on the rationale and expectations for ensemble prediction.

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M. Steven Tracton
and
Eugenia Kalnay

Abstract

On 7 December 1992 NMC began operational ensemble prediction. The ensemble configuration provides 14 independent forecasts every day, verifying on days 1 through 10. The ensemble members are generated through a combination of time lagging [Lagged-Average Forecasting] and a new method, Breeding of Growing Modes (Toth and Kalnay). In adopting the ensemble approach, NMC explicitly recognizes that forecasts are stochastic, not deterministic, in nature. There is no single solution, only an array of possibilities, and forecast ensembles provide a rational basis for assessing the range and likelihood of alternative scenarios.

Given the near saturation of computer resources at NMC, implementation of ensemble prediction required a trade-off between model resolution and multiple runs. Before 7 December 1992, NMC was producing a single global forecast through 10 days with the highest-resolution (T126) version possible of its medium-range forecast model. Now, based an experiments that showed no adverse impact upon the quality of forecasts, the T126 model run is truncated to T62 resolution beyond day 6. The computer savings are used to generate the balance of the ensemble members at the lower T62 resolution. While these complementary runs are, on the average, somewhat less skillful when considered individually, it is expected that ensemble averaging will increase skill levels. More importantly, we expect that ensemble prediction will enhance the utility of NWP by (a) providing a basis for the estimation of the reliability of forecasts, and (b) creating a quantitative foundation for probabilistic forecasting.

A major challenge of ensemble prediction is to condense the large amounts of information provided by ensembles into a user-friendly format that can be easily assimilated and used by forecasters. Some examples of output products relevant to operational forecast applications are illustrated. They include the display of each member of the ensemble, ensemble mean and dispersion fields, “clustering” of similar forecasts, and simple probability estimates.

While this implementation of ensemble prediction is relatively modest (ensembles of 14 members for the forecasts encompassing days 1 through 10), it does provide the basis for development of operational experience with ensemble forecasting, and for research directed toward maximizing the utility of NMC's numerical guidance.

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Zoltan Toth
,
Eugenia Kalnay
,
Steven M. Tracton
,
Richard Wobus
, and
Joseph Irwin

Abstract

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.

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