On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study
<|reference_start|>On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study: 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.<|reference_end|>
@article{zhang2024on,
    title={On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation
  Models: A Case Study},
    author={Lujia Zhang, Hanzhe Cui, Yurong Song, Chenyue Li, Binhang Yuan and
  Mengqian Lu},
    journal={arXiv preprint arXiv:2407.17842},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17842},
    primaryClass={cs.LG cs.AI}
}