Journal Article

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Gentine, Pierre, Ko, Joseph, Loftus, Kaitlyn, Lier Walqui, Marcus, Buch, Jatan, Hu, Arthur Z., Singer, Clare E., Morrison, Hugh, Lamb, Kara D., Powell, Margaret

2026

Overview

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales. Plain Language Summary Cloud microphysics refers to the microscale processes that impact liquid droplets and ice crystals in clouds and precipitation. Though small in scale, these processes need to be represented in weather and climate models because they impact the large‐scale evolution of clouds, precipitation, and the Earth's energy balance. Because of uncertainty in both cloud microphysical processes and in how these processes should be represented in models, they are a major source of uncertainty in current weather and climate models. The recent application of machine learning (ML) methods to atmospheric model development holds significant promise to address current limitations in modeling cloud microphysics, and thus improve atmospheric models. Here we discuss both the challenges and opportunities in applying ML methods to cloud microphysics. Key Points Cloud microphysics remains a major source of uncertainty in weather and climate models, and new paradigms are needed to address this challenge Machine learning (ML) holds promise for both bottom‐up and top‐down microphysics scheme development ML can address physical process and model representation uncertainty, but some uncertainty is inherently irreducible

• Publisher: John Wiley & Sons, Inc
• Subject: Aerosols / Artificial intelligence / Atmospheric models / bulk microphysics schemes / Climate / Climate and weather / Climate models / Cloud microphysics / cloud parameterizations / Clouds / Crystals / Datasets / Ice / Ice crystals / Machine learning / Microphysics / Parameterization / Physical sciences / Physics / Precipitation / Radiation / Reynolds number / Scaling / Simulation / Uncertainty / Weather forecasting