On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study

TL;DR

This paper explores the potential of foundation models, like GPT-4o, to perform complex atmospheric science tasks, highlighting their advantages over traditional deep learning methods.

Contribution

It provides a comprehensive case study evaluating GPT-4o across various atmospheric science tasks, demonstrating its capabilities and potential for future research.

Findings

GPT-4o effectively handles climate data processing and physical diagnosis.

The model shows promise in forecasting and climate adaptation tasks.

Foundation models can unify multiple atmospheric science functionalities.

Abstract

Most state-of-the-art AI applications in atmospheric science are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple complicated procedures to construct an intelligent agent, since each functionality is enabled by a separate model learned from independent climate datasets. The emergence of foundation models, especially multimodal foundation models, with their ability to process heterogeneous input data and execute complex tasks, offers a substantial opportunity to overcome this challenge. In this report, we want to explore a central question - how the state-of-the-art foundation model, i.e., GPT-4o, performs various atmospheric scientific tasks. Toward this end, we conduct a case study by categorizing the tasks into four main classes, including climate data processing, physical diagnosis, forecast and prediction, and adaptation and mitigation. For each task, we comprehensively evaluate the GPT-4o's performance along with a concrete discussion. We hope that this report may shed new light on future AI applications and research in atmospheric science.