Machine learning (ML) algorithms are powerful tools to build models of clouds and climate
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …

A promising approach to improve cloud parameterizations within climate models and thus
climate projections is to use deep learning in combination with training data from storm …

Data mining and analysis are critical for preventing or mitigating natural hazards. However,
data availability in natural hazard analysis is experiencing unprecedented challenges due to …

Deep learning can accurately represent sub‐grid‐scale convective processes in climate
models, learning from high resolution simulations. However, deep learning methods usually …

Global storm-resolving models (GSRMs) have gained widespread interest because of the
unprecedented detail with which they resolve the global climate. However, it remains difficult …

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 …

Modern geodynamics is based on the study of a large set of models, with the variation of
many parameters, whose analysis in the future will require Machine Learning to be …

Atmospheric processes involve both space and time. This is why human analysis of
atmospheric imagery can often extract more information from animated loops of image …

This thesis delves into the improvement of cloud parameterizations in climate models
through machine learning trained on coarse-grained output from high-resolution …

Atmospheric processes involve both space and time. Thus, humans looking at atmospheric
imagery can often spot important signals in an animated loop of an image sequence not …