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 …

The last few years have witnessed the emergence of data-driven weather forecast models
capable of competing with–and, in some respects, outperforming–physics-based numerical …

The forecast accuracy of machine learning (ML) weather prediction models is improving
rapidly, leading many to speak of a “second revolution in weather forecasting.” With …

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to
physics-based numerical models for medium-range weather forecasting. Several …

Abstract Machine learning (ML)‐based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …

Constraining a numerical weather prediction (NWP) model with observations via 4D
variational (4D-Var) data assimilation is often difficult to implement in practice due to the …

Data-driven medium-range weather forecasts have recently outperformed classical
numerical weather prediction models, with Pangu-Weather (PGW) being the first …

Information about the weather has profound implications for numerous sectors, including
agriculture, energy, and disaster management. Accurate and reliable predictions are …