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Abstract: The continental-scale reorganization of atmospheric circulation that accompanies the onset of India’s summer monsoon has been studied for over a century, and the progressive spatial expansion of rainfall that occurs during this transition has great consequence for more than a billion people. Smallholder farmers in India make consequential decisions about planting based on their expectations of when this transition will occur, and they benefit from multiple weeks of advance notice of the onset date. However, no weeks-ahead forecast of the timing of this rainfall transition in regions across India has been available to farmers or shown to be skillful. Here, we describe the development and deployment of a new forecasting system for regional, agriculturally relevant monsoon onset that blends artificial intelligence (AI) weather prediction models with a new statistical model for the probability of first-occurrence events in a season. A novel feature of this statistical-AI system is its ability to account for the likelihood of dry periods beyond the lead-time horizon of the AI model output. We describe the methods used to deploy this AI-based system in real time, and discuss the successful dissemination of probabilistic onset date forecasts by SMS to 38 million farmers in the summer of 2025.

About the speaker: William Boos is a professor at the University of California, Berkeley in the Department of Earth and Planetary Science, where he specializes in atmospheric dynamics. His research focuses on understanding the mechanisms that govern extreme weather phenomena, planetary wind patterns, and monsoons, which are continental-scale atmospheric circulations that deliver water to billions of people in Earth’s tropics. He is also a Faculty Scientist at the Lawrence Berkeley National Laboratory. Before moving to Berkeley, he was on the faculty at Yale and a postdoctoral fellow at Harvard. He received his Ph.D. from MIT and his undergraduate degrees from the State University of New York at Binghamton.

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