Foundation Models as Assistive Tools in Hydrometeorology: Opportunities, Challenges, and Perspectives

Abstract Most state‐of‐the‐art AI applications in hydrometeorology are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple functions to construct a single intelligent agent, as each function is enabled by a separate model trained on independent data sets. Foundation models (FMs), which can process diverse inputs and perform different tasks, present a substantial opportunity to overcome this challenge. In this commentary, we evaluate how three state‐of‐the‐art FMs, specifically GPT‐4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, perform across four key task types in hydrometeorology: data processing, event diagnosis, forecast and prediction, and decision‐making. The models perform well in the first two task types and offer valuable information for decision‐makers but still face challenges in generating reliable forecasts. Moreover, this commentary highlights the concerns regarding the use of FMs: hallucination, responsibility, over‐reliance, and openness. Finally, we propose that enhancing human‐AI collaboration and developing domain‐specific FMs could drive the future of FM applications in hydrometeorology. We also provide specific recommendations to achieve the perspectives.