Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …

Statistical postprocessing techniques are nowadays key components of the forecasting
suites in many national meteorological services (NMS), with, for most of them, the objective …

Postprocessing ensemble weather predictions to correct systematic errors has become a
standard practice in research and operations. However, only a few recent studies have …

Raw forecasts from numerical weather prediction models suffer from systematic bias and
cannot be directly used in applications such as hydrological forecasting. Statistical post …

Deep-learning (DL) postprocessing methods are examined to obtain reliable and accurate
probabilistic forecasts from single-member numerical weather predictions of integrated …

Reliable subseasonal forecasts of high summer temperatures would be very valuable for
society. Although state-of-the-art numerical weather prediction (NWP) models have become …

Neural networks (NN) have become an important tool for prediction tasks—both regression
and classification—in environmental science. Since many environmental-science problems …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

Many present-day statistical schemes for postprocessing weather forecasts, in particular
precipitation forecasts, rely on calibration using prescribed statistical models to relate …

Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This
work examines the application of a deep learning (DL) architecture, Unet, for postprocessing …