Forecasting Forecast Skill

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  • 1 NASA/Goddard Space Flight Center, Greenbelt, MD 20771
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Abstract

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.

Abstract

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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