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For farmers, every planting decision carries risks, especially with the increasing impacts of climate change. One of the most significant risks is weather, which can devastate crop yields and livelihoods. For example, a delayed monsoon can force a rice farmer in South Asia to replant or switch crops entirely, resulting in lost time and income.
Access to reliable, timely weather forecasts can help farmers prepare for weeks ahead, determining the best planting times or fertilizer needs, leading to better crop yields and lower costs.
However, accurate weather forecasts remain out of reach in many low- and middle-income countries due to high technology costs and infrastructure demands of traditional forecasting models.
A new wave of AI-powered weather forecasting models can change this. These models deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with timely, localized information about changing rainfall patterns.
The challenge is getting this technology where it’s needed.
### Why AI Forecasting Matters Now
Physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast future conditions, but they require expensive computing infrastructure. This makes them unaffordable for most developing countries.
Moreover, these models have historically focused on temperate, high-income regions and paid less attention to tropical areas where many low- and middle-income countries are located.
In 2022, industry and university researchers developed deep learning models that can generate accurate short- and medium-range forecasts globally up to two weeks ahead. These models run several orders of magnitude faster than physics-based models and can operate on laptops instead of supercomputers. Newer models like Pangu-Weather and GraphCast have matched or even outperformed leading physics-based systems for some predictions, such as temperature.
### Tailoring Forecasts for Real-World Decisions
While AI weather models offer impressive technical capabilities, their impact depends on how well they are calibrated to local weather conditions, benchmarked against real-world agricultural needs, and aligned with farmers’ actual decision-making processes. For example, forecasting what and when to plant or predicting drought.
To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide. Groups like AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers’ specific requirements.
International development institutions and the World Meteorological Organization are also working to expand access to these models in low- and middle-income countries.
### A New Era in Climate Adaptation
AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. However, technology alone won’t change lives. With support, low- and middle-income countries can build the capacity to generate, evaluate, and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services.
