Machine learning for postprocessing ensemble forecasts of wind gusts with a focus on European winter storms

Abstract

<p>Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings, e.g. in European winter storms. First, we provide a comprehensive review and systematic comparison of several statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, then we assess the performance of selected methods within winter storms. The methods can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The different approaches are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts as well as estimating locally adaptive neural networks leads to significant improvements in forecast skill. Assessing the performance of EMOS and neural network-based postprocessing for selected winter storms, we find that the networks better adapt to the extreme conditions than the statistical benchmark and thus yield a superior predictive performance. However, results suggest that the performance can still be further improved, e.g. via regime-dependent postprocessing.</p>