A new way to predict forecast skill

Abstract

Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used. Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between “forecasted” and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.