NMME-based hybrid prediction of Atlantic hurricane season activity

Abstract

A hybrid dynamical–statistical model is pursued for prediction of Atlantic seasonal hurricane activity driven by output of the North American Multimodel Ensemble (NMME). This is an updated version of a proven multiple linear regression method conditioned on forecast vertical wind shear from the Climate Forecast System and observed sea surface temperatures (SSTs). The method pursued for prediction utilizes August–October (ASO) Main Development Region (MDR; 10–20°N, 20–80°W) vertical wind shear and observed North Atlantic (NATL; 55–65°N, 30–60°W) SST averaged over the 3 months preceding the forecast in conjunction with the full hurricane climatology. NMME forecasts improve upon representations relative to individual members. The NMME multi-model mean better reproduces vertical wind shear distributions over the MDR and captures the observed relationships between SST and vertical wind shear with hurricane trend and interannual variability despite occasionally poor reproductions by individual members. Cross-validation reveals the multi-model average of the hybrid model outputs from the individual NMME members yields forecast errors 10–30% less than the individual members, while correlations with observed hurricane-related activity typically improve. The NMME methodology is shown to be competitive with official outlooks from Colorado State University and the National Oceanic and Atmospheric Administration over recent years.