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 …

In September 2019, a workshop was held to highlight the growing area of applying machine
learning techniques to improve weather and climate prediction. In this introductory piece, we …

Benchmark datasets and benchmark problems have been a key aspect for the success of
modern machine learning applications in many scientific domains. Consequently, an active …

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

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

The rise of machine learning (ML) has significantly advanced the field of coastal
oceanography. This review aims to examine the existing deficiencies in numerical …

Artificial intelligence (AI) and machine learning (ML) have become important tools for
environmental scientists and engineers, both in research and in applications. Although …

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

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts
into reliable probabilistic forecast distributions. In this study, we examine the use of …

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 …