Maximizing the forecasting skill of an ensemble model

Abstract

An ensemble model integrates forecasts of different models (or different parametrizations of the same model) into one single ensemble forecast. This procedure has different names in the literature and is approached through different philosophies in theory and practice. Previous approaches often weighted forecasts equally or according to their individual skill. Here we present a more meaningful strategy by obtaining weights that maximize the skill of the ensemble. The procedure is based on a multivariate logistic regression and exposes some level of flexibility to emphasize different aspects of seismicity and address different end users. We apply the ensemble strategy to the operational earthquake forecasting system in Italy and demonstrate its superior skill over the best individual forecast model with statistical significance. In particular, we highlight that the skill improves when exploiting the flexibility of fitting the ensemble, for example using only recent and not the entire historical data.