Machine Learning for Clouds and Climate (Invited Chapter for the AGU
Geophysical Monograph Series “Clouds and Climate”)
Abstract
Key Points: • Machine learning (ML) helps model the interaction between
clouds and climate using large datasets. • We review
physics-guided/explainable ML applied to cloud-related processes in the
climate system. • We also provide a guide to scientists who would like
to get started with ML. Abstract: 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-used semiempirical models. Here, we review ML tools, including
interpretable and physics-guided ML, and outline how they can be applied
to cloud-related processes in the climate system, including radiation,
microphysics, convection, and cloud detection , classification,
emulation, and uncertainty quantification. We additionally provide a
short guide to get started with ML and survey the frontiers of ML for
clouds and climate .