  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.
