Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing

Abstract

onhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.