AI's Role in Transforming Weather Forecasts for 38 Million Farmers

Published Date : 15/09/2025 

Discover how Google's NeuralGCM model, in collaboration with the University of Chicago, is providing advanced weather forecasts to Indian farmers, helping them make crucial decisions for planting and crop management. 

This summer, 38 million farmers in India received AI-powered forecasts of the start of the monsoon season, helping them make more informed decisions about when to plant their crops for the season. These forecasts were powered in part by NeuralGCM, a Google Research model, which combines traditional physics-based modeling with machine learning for improved simulation accuracy and efficiency.

For years, weather and climate models have been costly and complex, often requiring a supercomputer to run. Our teams at Google Research wanted to see if we could build these models more efficiently and more accurately, leading to the creation of NeuralGCM. Unlike traditional models that rely purely on hard-coded physics, this AI-driven model is trained on decades of historical weather data to infer patterns and learn from past events, while also using physics. Crucially, it’s designed to be flexible and efficient — it can run on a single laptop, making high-quality forecasting more accessible to the scientific community.

When we open-sourced NeuralGCM, we hoped the community would use this new tool to power their own innovative applications. The University of Chicago's Human-Centered Weather Forecasts Initiative did just that. They recognized that one of the most impactful, yet increasingly challenging, decisions for Indian farmers is when to put seeds in the ground. Hundreds of millions of smallholder farmers across the tropics depend on information about when the rainy season, known as the monsoon, will come each year. However, accurate forecasting of when the monsoon will begin, especially at long lead times and at local scales, has remained a century-old challenge.

By rigorously testing several AI weather models, the University of Chicago team found that NeuralGCM, when blended with other advanced models like the European Centre for Medium-Range Weather Forecasts (ECMWF)’s Artificial Intelligence/Integrated Forecasting System (AIFS) and historical data, was the right tool for the job. It accurately predicted the onset of the Indian monsoon up to a month in advance, even capturing an unusual dry spell in the progression of the monsoon.

The image on the left shows the average of 120 years of historical data (e.g., what was expected). The image in the middle is what was observed by the India Meteorological Department. On the right is what the AI forecast predicted 15 days ahead of time. Credit: The University of Chicago Institute for Climate and Growth’s Human-Centered Weather Forecasts Initiative.

In partnership with the Indian Ministry of Agriculture and Farmers’ Welfare, this pioneering blend of AI models created by the University of Chicago successfully delivered tailored, advanced forecasts via SMS to 38 million farmers this summer. This initiative helped farmers proactively adjust their planting decisions — when to plant, whether to buy more seeds, switch to different crops or simply wait — allowing them to adapt to an unusually delayed monsoon season.

Existing research from the University of Chicago shows that by providing an accurate forecast around a month in advance, farmers can align their decisions with the coming weather and improve their outcomes. The study found that advance forecasts lead to an almost doubling of their annual income. This project is a powerful example of how foundational AI technology, born from research, can serve real-world use cases, ultimately helping communities around the world build climate resilience. 

Frequently Asked Questions (FAQS): 

Q: What is NeuralGCM?

A: NeuralGCM is a Google Research model that combines traditional physics-based modeling with machine learning to improve weather and climate simulation accuracy and efficiency.

Q: How does NeuralGCM differ from traditional weather models?

A: Unlike traditional models that rely purely on hard-coded physics, NeuralGCM is trained on decades of historical weather data to infer patterns and learn from past events, while also using physics. It is also designed to be flexible and efficient, running on a single laptop.

Q: Who partnered with Google to use NeuralGCM for weather forecasting?

A: The University of Chicago's Human-Centered Weather Forecasts Initiative partnered with Google to use NeuralGCM for advanced weather forecasting, particularly for predicting the onset of the Indian monsoon.

Q: How many farmers benefited from the AI-powered forecasts?

A: This summer, 38 million farmers in India received AI-powered forecasts of the start of the monsoon season, helping them make more informed planting decisions.

Q: What was the impact of the AI forecasts on farmers' income?

A: Research from the University of Chicago shows that providing accurate forecasts around a month in advance can almost double farmers' annual income by helping them align their decisions with the coming weather. 

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