Rajat Masiwal, Colin Aitken, Adam Marchakitus, Mayank Gupta, Katherine Kowal, Hamid A. Pahlavan, Tyler Yang, Y. Qiang Sun, Michael Kremer, Amir Jina, William R. Boos, Pedram Hassanzadeh
This paper introduces a decision-oriented framework for evaluating AI weather prediction models, using Indian monsoon forecasting as a case study to benefit rain-fed agriculture and support millions of farmers with actionable forecasts.
The study presents a new way to evaluate AI-based weather prediction models by focusing on how they can help people, especially in regions vulnerable to severe weather. The researchers used this approach to improve forecasts for the Indian monsoon, which is crucial for farmers who rely on rain for their crops. By providing more accurate and timely predictions, the framework aims to help millions of farmers prepare for weather changes. This method could be a game-changer for using AI to support communities in adapting to climate change.
