Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …

Climate models are essential to understand and project climate change, yet long‐standing
biases and uncertainties in their projections remain. This is largely associated with the …

Parameterization of subgrid‐scale processes is a major source of uncertainty in global
atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less …

The most useful weather prediction for the public is near the surface. The processes that are
most relevant for near-surface weather prediction are also those that are most interactive …

Abstract Machine learning (ML) algorithms can be used in Earth system models (ESMs) to
emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the …

Accurate modeling of weather and climate is critical for taking effective action to combat
climate change. Predicted and observed quantities such as precipitation, clouds, aerosols …