Statistical postprocessing of medium-range weather forecasts is an important component of
modern forecasting systems. Since the beginning of modern data science, numerous new …

By the end of 2021, the renewable energy share of the global electricity capacity reached
38.3% and the new installations were dominated by wind and solar energy, showing global …

Generative machine learning methods for multivariate ensemble postprocessing Page 1
The Annals of Applied Statistics 2024, Vol. 18, No. 1, 159–183 https://doi.org/10.1214/23-AOAS1784 …

Statistical post-processing of systematic errors is required for numerical weather predictions
to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with …

Artificial intelligence (AI)-based data-driven weather forecasting models have experienced
rapid progress over the last years. Recent studies, with models trained on reanalysis data …

Rank and probability integral transform histograms are established tools to assess the
calibration of probabilistic forecasts. They not only check whether a forecast is calibrated, but …

Since the start of the operational use of ensemble prediction systems, ensemble-based
probabilistic forecasting has become the most advanced approach in weather prediction …

Although, by now, ensemble-based probabilistic forecasting is the most advanced approach
to weather prediction, ensemble forecasts still suffer from a lack of calibration and/or display …

To be able to produce accurate and reliable predictions of visibility has crucial importance in
aviation meteorology, as well as in water‐and road transportation. Nowadays, several …

A common issue faced by the downscaled regional ensemble prediction systems is the
under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under …