In climate science and meteorology, high-resolution local precipitation (rain and snowfall)
predictions are limited by the computational costs of simulation-based methods. Statistical …

Improving ensemble forecasts is a complex process which involves proper scores such as
the continuous ranked probability score (CRPS). A homogeneous Gaussian (hoG) model is …

The continuous ranked probability score (crps) is the most commonly used scoring rule in
the evaluation of probabilistic forecasts for real-valued outcomes. To assess and rank …

To account for uncertainties, forecasts for future events are commonly expressed in terms of
probability distributions over the set of possible outcomes. To evaluate the quality of such …

Probabilistic forecasting of distribution tails (ie, quantiles below 0.05 and above 0.95) is
challenging for non-parametric approaches since data for extreme events are scarce. A poor …

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) …

Statistical analysis of extremes can be used to predict the probability of future extreme
events, such as large rainfalls or devastating windstorms. The quality of these forecasts can …

Motivated by the growing interest in sound forecast evaluation techniques with an emphasis
on distribution tails rather than average behaviour, we investigate a fundamental question …

Averages of proper scoring rules are often used to rank probabilistic forecasts. In many
cases, the individual terms in these averages are based on observations and forecasts from …

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the
distribution are used for inference. Extreme observations are specified as values that exceed …