Blending machine learning and physics-based approaches for weather and climate: a typology

TL;DR

This paper classifies and discusses various approaches to integrating machine learning with physics-based models for weather and climate prediction, highlighting their benefits, limitations, and strategic uses.

Contribution

It provides a structured typology of blended modelling approaches, facilitating development, implementation, and strategic decision-making in next-generation prediction systems.

Findings

Blended approaches combine ML speed with physics-based robustness.

The typology helps classify and guide development of integrated models.

Framework supports strategic planning and transition to advanced prediction systems.

Abstract

The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we "blend" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approaches and discusses some of the strategic benefits that come with them. It can be used not just to classify modelling systems, but also identify routes to gradual, incremental or wholesale development and implementation of new and emerging capabilities. These approaches provide a practical path to innovation by combining the speed and adaptability of machine learning with the robustness, trust, and interpretability of physics-based systems. By adopting a structured vocabulary and outlining the benefits and limitations of each approach, this framework supports informed decision-making and strategic planning, and can be used by the wider community to navigate the transition to next-generation prediction systems.