Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis–Gibbs Sampling

Abstract

A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australian and the South Pacific Ocean regions and sub-regions. It has been found by the new approach that indices which describe tropical cyclone inter-annual variability such as the Dipole Mode Index (DMI) and the El Niño Modoki Index (EMI) are among the most important predictors used by the selected models. The core computational method underlying the proposed approach is a new stochastic search algorithm that we have developed, and is named Metropolis–Gibbs random scan (MGRS). By applying MGRS to minimize AICc over all candidate models, a set of the most important predictors are identified which can form a small number of optimal Poisson regression models. These optimal models are then averaged to improve their overall predictability. Results from our case study of tropical cyclone seasonal forecasting show that the MGRS-AICc method performs significantly better than the commonly used step-wise AICc method.