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Eugenia Kalnay and Amnon Dalcher


We have shown that it is possible to predict the skill of numerical weather forecasts—a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite data impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems.

When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when we used regional verifications, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

Although the period covered in this study is only one month long, it includes cases with wide variation of skill in each of the four regions considered. The method could be tested in an operational context using ensembles of lagged forecasts and longer time periods in order to test its applicability to different arms and weather regimes.

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Amnon Dalcher, Eugenia Kalnay, and Ross N. Hoffman


In this work we report the application of the lagged average forecasting (LAF) technique to operational forecasts of the ECMWF. The ECMWF data consist of two 100-day samples of 10-day forecasts of 500 mb geopotential height for winter 1980/81 and summer 1981. The LAF ensemble includes the latest operational forecast, and also forecasts for the same verification time stated one or more days earlier than the latest one. We focus on the following two issues: 1) Does ensemble averaging improve forecast skill and 2) Is the dispersion of the ensemble useful in predicting forecast skill. We used the LAF technique to produce 3, 5, 7, 8 and 9 day forecasts of the 500 mb height field. The results show the statistically filtered LAF is a marked improvement upon the operational forecast after 5 days. We find that on a global scale forecast skill is weakly correlated with the dispersion of the ensemble, as measured by the rms difference between the operational forecast and the statistically filtered LAF.

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