Using Bayesian model averaging to improve hurricane track forecasts

I study whether Bayesian composite forecasting can produce improved track forecasts for hurricanes. Using data on hurricanes back to 2005, the first step is to find a set of storms most similar to the one to have its track forecast. Then, the performance of ten hurricane forecasting models on those similar storms is used to calculate the weights that will be placed on each of these models. These weights are used to form a Bayesian composite forecast of the track for the hurricane of interest, rather than the currently, more standard, simple average utilized by the National Hurricane Center (NHC). On a small selection of recent hurricanes, the performance of the Bayesian composite forecast tracks are compared to the individual model forecasts and the NHC official forecasts. In most of our cases, the Bayesian composite forecast is more accurate than the NHC forecast.