A Statistical Forecast Model for Atlantic Seasonal Hurricane Activity Based on the NCEP Dynamical Seasonal Forecast

Abstract

Abstract A hybrid dynamical–statistical model is developed for predicting Atlantic seasonal hurricane activity. The model is built upon the empirical relationship between the observed interannual variability of hurricanes and the variability of sea surface temperatures (SSTs) and vertical wind shear in 26-yr (1981–2006) hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The number of Atlantic hurricanes exhibits large year-to-year fluctuations and an upward trend over the 26 yr. The latter is characterized by an inactive period prior to 1995 and an active period afterward. The interannual variability of the Atlantic hurricanes significantly correlates with the CFS hindcasts for August–October (ASO) SSTs and vertical wind shear in the tropical Pacific and tropical North Atlantic where CFS also displays skillful forecasts for the two variables. In contrast, the hurricane trend shows less of a correlation to the CFS-predicted SSTs and vertical wind shear in the two tropical regions. Instead, it strongly correlates with observed preseason SSTs in the far North Atlantic. Based on these results, three potential predictors for the interannual variation of seasonal hurricane activity are constructed by averaging SSTs over the tropical Pacific (TPCF; 5°S–5°N, 170°E–130°W) and the Atlantic hurricane main development region (MDR; 10°–20°N, 20°–80°W), respectively, and vertical wind shear over the MDR, all of which are from the CFS dynamical forecasts for the ASO season. In addition, two methodologies are proposed to better represent the long-term trend in the number of hurricanes. One is the use of observed preseason SSTs in the North Atlantic (NATL; 55°–65°N, 30°–60°W) as a predictor for the hurricane trend, and the other is the use of a step function that breaks up the hurricane climatology into a generally inactive period (1981–94) and a very active period (1995–2006). The combination of the three predictors for the interannual variation, along with the two methodologies for the trend, is explored in developing an empirical forecast system for Atlantic hurricanes. A cross validation of the hindcasts for the 1981–2006 hurricane seasons suggests that the seasonal hurricane forecast with the TPCF SST as the only CFS predictor is more skillful in inactive hurricane seasons, while the forecast with only the MDR SST is more skillful in active seasons. The forecast using both predictors gives better results. The most skillful forecast uses the MDR vertical wind shear as the only CFS predictor. A comparison with forecasts made by other statistical models over the 2002–07 seasons indicates that this hybrid dynamical–statistical forecast model is competitive with the current statistical forecast models.