Physics-informed machine learning: case studies for weather and climate modelling
Karthik Kashinath, Mohamed Elhafiz Mustafa, Adrian Albert, Jinlong Wu, Chung-Hsiang Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Rui Wang, Ashesh Chattopadhyay, Aakanksha Singh, A. Manepalli, Dragos B. Chirila, Rose Yu, Robin Walters, Brian White, Heng Xiao, Hamdi A. Tchelepi, Philip Marcus, Anima Anandkumar, Pedram Hassanzadeh, Prabhat
Abstract
Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories. Through 10 case studies, we show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes. The accomplishments of these studies include greater physical consistency, reduced training time, improved data efficiency, and better generalization. Finally, we synthesize the lessons learned and identify scientific, diagnostic, computational, and resource challenges for developing truly robust and reliable physics-informed ML models for weather and climate processes. This article is part of the theme issue 'Machine learning for weather and climate modelling'.