🤖 AI Summary
Numerical weather prediction (NWP) ensembles often exhibit systematic biases and insufficient spread in extreme wind speed forecasting. To address this, we propose a novel EMOS statistical post-processing paradigm based on the threshold-weighted continuous ranked probability score (twCRPS). We derive closed-form expressions for twCRPS under multiple parametric distributions—first such derivation—and design a joint framework integrating weighted parameter estimation and linear pooling to simultaneously improve bulk calibration and tail sensitivity. Experiments on ECMWF ensemble forecasts demonstrate that our method significantly enhances probabilistic forecasting performance for extreme wind events across multiple thresholds. The approach balances theoretical rigor with operational practicality, offering a generalizable, threshold-aware training objective for extreme-weather probability forecasting.
📝 Abstract
Accurate forecasts of extreme wind speeds are of high importance for many applications. Such forecasts are usually generated by ensembles of numerical weather prediction (NWP) models, which however can be biased and have errors in dispersion, thus necessitating the application of statistical post-processing techniques. In this work we aim to improve statistical post-processing models for probabilistic predictions of extreme wind speeds. We do this by adjusting the training procedure used to fit ensemble model output statistics (EMOS) models - a commonly applied post-processing technique - and propose estimating parameters using the so-called threshold-weighted continuous ranked probability score (twCRPS), a proper scoring rule that places special emphasis on predictions over a threshold. We show that training using the twCRPS leads to improved extreme event performance of post-processing models for a variety of thresholds. We find a distribution body-tail trade-off where improved performance for probabilistic predictions of extreme events comes with worse performance for predictions of the distribution body. However, we introduce strategies to mitigate this trade-off based on weighted training and linear pooling. Finally, we consider some synthetic experiments to explain the training impact of the twCRPS and derive closed-form expressions of the twCRPS for a number of distributions, giving the first such collection in the literature. The results will enable researchers and practitioners alike to improve the performance of probabilistic forecasting models for extremes and other events of interest.