What happened
The Laude Institute awarded a $10 million grant to a University of Chicago team to build the UChicago–Laude AI Almanac, a free, open-source platform for AI weather forecasting. The project aims to enable meteorological agencies in low- and middle-income countries to generate, evaluate, and deploy AI forecasts without requiring supercomputers or dense observation networks. The team, led by professors including Rebecca Willett and Nobel laureate Michael Kremer, has already supported government ministries in delivering AI forecasts to tens of millions of farmers in Africa and South Asia, reducing the time needed to adapt forecasting models from months to a single day of expert review.
The Laude Institute has awarded a $10 million grant to a University of Chicago team to develop new AI weather forecasting frameworks and a supporting software platform. The project, known as the UChicago–Laude AI Almanac, is designed to be a free, open-source tool that allows meteorological agencies, governments, and NGOs to generate and deploy AI weather forecasts tailored to their specific geographic and decision-making needs.
The team is led by Prof. Rebecca Willett, Prof. Pedram Hassanzadeh, Prof. Ian Foster, and Nobel laureate Prof. Michael Kremer. Their approach focuses on blending existing AI and physics-based forecasts to address specific human-centered decisions, such as monsoon onset for farmers, rather than just general meteorological accuracy. The team has reported reducing the time required to and adapt forecasting models from four to five months of expert labor to just one day.
Prior to this grant, the UChicago Human-Centered Weather Forecasts initiative supported government agricultural ministries in Africa and South Asia from 2024 to 2026. These efforts delivered AI weather forecasts via text message to tens of millions of smallholder farmers, helping them make informed planting decisions and providing early warnings for unusual weather patterns like stalled seasonal rains.
The new platform aims to remove barriers such as the need for specialized technical expertise, supercomputing infrastructure, or dense observational networks. It will draw on the experience of Globus, a non-profit research data management platform, to ensure reliable and accessible large-scale research computing for meteorological agencies.
Why it matters
This grant addresses a critical gap in global weather infrastructure, where traditional physics-based models are often inaccessible or less accurate for tropical regions with sparse data. By creating an open-source, scalable platform that focuses on human-centered decision-making rather than just meteorological accuracy, the project aims to make AI weather tools accessible to the millions of people in low-resource settings who rely on accurate forecasts for agriculture and public health. The initiative represents a significant step in democratizing advanced AI capabilities for public good, potentially serving as a model for other sectors where market mechanisms fail to provide essential tools to those who need them most.
Traditional weather forecasting systems were developed for mid-latitude climates with dense observation networks and supercomputers, leaving tropical regions with less accurate and less actionable forecasts. This project specifically targets that gap by using AI to work with sparse observational data and modest computing resources.
The platform shifts the focus from meteorological accuracy to practical utility, asking whether a forecast helps a specific person make a better decision in a specific place. This human-centered approach is crucial for sectors like agriculture and public health, where the timing of weather events directly impacts livelihoods and safety.
By making the platform open-source and free, the initiative aims to accelerate the translation of AI-driven weather forecasting into capabilities for meteorological agencies that need it most. This could significantly expand access to high-quality, actionable weather information for hundreds of millions of people in low- and middle-income countries.
The project also serves as a potential model for other areas of AI application where philanthropy can step in to provide essential tools that the market cannot affordably supply, such as public health and education.
Interactive Mechanism: How It Actually Works
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What to watch next
Monitor the rollout of the UChicago–Laude AI Almanac platform and its adoption by meteorological agencies in the 14 countries currently participating in the companion training program. Watch for the expansion of this initiative to 30 countries by 2027 and the specific types of weather hazards, such as compound climate extremes, that the new AI frameworks will be designed to predict. Additionally, observe how the platform’s open-source nature influences the broader development of AI weather tools in the global south.
The expansion of the companion AIM for Scale training program, which is currently preparing government meteorologists from 14 countries to use these tools, with a goal of reaching 30 countries by 2027.
The specific new AI weather forecasting frameworks that will be embedded in the UChicago–Laude AI Almanac and how they perform in predicting various weather and climate hazards, including compound climate extremes.
The adoption of the open-source platform by meteorological agencies in different regions and its impact on the reliability and accessibility of local weather forecasts.
The collaboration between the UChicago team and organizations such as the OpenAI Foundation, the Gates Foundation, and development banks in scaling the delivery of these AI tools to a broader audience.