Overview
Research introduces a typology for integrated modeling approaches that merge machine learning (ML) with physics-based models in the realm of weather and climate prediction. This framework aims to classify existing modeling systems and guide the development and implementation of new capabilities in the field. The integration of these two distinct approaches seeks to leverage their respective benefits while mitigating individual challenges, potentially accelerating the uptake of emerging scientific insights in a practical and reliable manner.
Research Context
The field of weather and climate prediction is undergoing a transformation driven by the integration of machine learning techniques with established physics-based models. Both ML-based and physics-based approaches possess distinct advantages and limitations when applied independently. The concept of "blending" these approaches is explored as a method to combine the speed and adaptability offered by machine learning with the robustness, trustworthiness, and interpretability inherent in physics-based systems. This integration is presented as a practical avenue for innovation, designed to support informed decision-making and strategic planning.
The strategic benefits of such blended approaches are discussed within the paper. The developed typology intends to provide a structured vocabulary for the community, facilitating navigation during the transition towards next-generation prediction systems. It also identifies pathways for incremental, gradual, or wholesale advancements in modeling capabilities.
Approach
The approach involves developing a typology that categorizes different ways machine learning and physics-based modeling systems can be integrated. This typology serves as a tool for classifying various blended modeling systems. Additionally, it identifies potential routes for the development and implementation of new capabilities, ranging from gradual evolution to more comprehensive changes. The framework aims to outline the benefits and limitations associated with each approach within the typology.
Findings
The research establishes that integrating machine learning with traditional physics-based models has the potential to accelerate the pull-through of emerging science in a trusted and practical way in weather and climate prediction. A diverse array of choices exists for blending ML with established physics-based modeling systems to optimize benefits. The developed typology offers a means not only to classify these modeling systems but also to identify routes for gradual, incremental, or wholesale development and implementation of new capabilities.
These blended approaches are characterized as providing a practical path to innovation. This path is achieved by combining the speed and adaptability inherent in machine learning with the robustness, trust, and interpretability of physics-based systems. The framework, through its structured vocabulary and discussion of benefits and limitations, aims to support informed decision-making and strategic planning for the wider community as it transitions to next-generation prediction systems.
Why This Matters
This typology offers a structured framework for understanding and implementing combined machine learning and physics-based models in weather and climate prediction. It provides guidance for the scientific community, enabling informed decisions and strategic planning for developing future prediction systems. The framework can help in integrating emerging scientific advancements into practical applications more efficiently and reliably.