A Potential High-Score Scheme for the Prediction of Atlantic Storm Activity

Abstract

A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding Janu- ary−February and April−May. The 2.5°×2.5° resolution reanalysis data from both the US National Center for En- vironmental Prediction/the National Center for Atmos- pheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied. The model was cross-validated using data from 1979−2002. The ATSN predictions from the two reanaly- sis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76, respectively. When the predictions of the two models were averaged, the ACC increased to 0.90 and the MAE decreased to 1.18, an exceptionally high score. Therefore, this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic