Decision-oriented benchmarking to transform AI weather forecast access: Application to the Indian monsoon
🤖 AI Summary
This study addresses the gap in current AI weather forecasting evaluation, which predominantly emphasizes meteorological metrics while overlooking the real-world decision-making needs of users in low-income regions during high-impact weather events. The authors propose the first decision-oriented evaluation framework that integrates meteorology, artificial intelligence, and social science, embedding stakeholder requirements from rainfed agricultural communities in India into the AI forecast assessment pipeline. Leveraging open-source AI models and combining deterministic and probabilistic metrics, the framework enables out-of-sample, regional-scale prediction of the monsoon onset—a critical indicator for agricultural planning. Validated in practice, it successfully underpinned the government’s 2025 rollout of AI-driven monsoon forecasts to 38 million Indian farmers, accurately capturing rare multi-week stagnation events during monsoon progression and thereby advancing AI models from technical performance toward tangible societal impact.
Application Category
📝 Abstract
Artificial intelligence weather prediction (AIWP) models now often outperform traditional physics-based models on common metrics while requiring orders-of-magnitude less computing resources and time. Open-access AIWP models thus hold promise as transformational tools for helping low- and middle-income populations make decisions in the face of high-impact weather shocks. Yet, current approaches to evaluating AIWP models focus mainly on aggregated meteorological metrics without considering local stakeholders'needs in decision-oriented, operational frameworks. Here, we introduce such a framework that connects meteorology, AI, and social sciences. As an example, we apply it to the 150-year-old problem of Indian monsoon forecasting, focusing on benefits to rain-fed agriculture, which is highly susceptible to climate change. AIWP models skillfully predict an agriculturally relevant onset index at regional scales weeks in advance when evaluated out-of-sample using deterministic and probabilistic metrics. This framework informed a government-led effort in 2025 to send 38 million Indian farmers AI-based monsoon onset forecasts, which captured an unusual weeks-long pause in monsoon progression. This decision-oriented benchmarking framework provides a key component of a blueprint for harnessing the power of AIWP models to help large vulnerable populations adapt to weather shocks in the face of climate variability and change.
Problem
Research questions and friction points this paper is trying to address.
AI weather prediction
decision-oriented benchmarking
Indian monsoon
weather shocks
climate adaptation
Innovation
Methods, ideas, or system contributions that make the work stand out.
decision-oriented benchmarking
AI weather prediction
monsoon forecasting
agricultural decision support
climate adaptation
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