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.

翻译：当前大气科学领域最先进的人工智能应用大多基于经典的深度学习方法。然而，此类方法无法自动整合多个复杂流程以构建智能体，因为每个功能均由从独立气候数据集学习得到的单独模型实现。基础模型，尤其是多模态基础模型的出现，凭借其处理异构输入数据并执行复杂任务的能力，为克服这一挑战提供了重要机遇。在本报告中，我们旨在探讨一个核心问题——当前最先进的基础模型（即GPT-4o）如何执行各类大气科学任务。为此，我们开展了一项案例研究，将任务划分为四大类别：气候数据处理、物理诊断、预报与预测、适应与减缓。针对每类任务，我们全面评估了GPT-4o的性能并进行了具体讨论。我们希望本报告能为大气科学领域未来的人工智能应用与研究提供新的启示。