  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.
