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Microsoft has released an open-source machine learning tool designed to correct systematic biases in existing weather models, extending forecast reliability from two weeks to six weeks. Unlike resource-intensive standalone simulations or GPU-dependent generative AI, this approach runs efficiently on standard CPUs, offering a cost-effective solution for agencies with limited computing power. By addressing data gaps and compounding errors in both physics-based and historical pattern-matching methods, the tool aims to significantly improve planning accuracy for agriculture and emergency response.
Microsoft has a new machine learning tool to improve weather forecasts

Old-school physics-based weather forecasting and even new AI models typically become less reliable beyond two weeks, a problem Microsoft claims to have solved using machine learning. Researchers with the company say they've found a new, cost-effective way to make more accurate weather forecasts two to six weeks in advance - allowing farmers and emergency responders to plan ahead with greater certainty. The findings were published today in the journal Nature, and the tool is now open-source software. Microsoft joins Google, Nvidia, and other companies developing AI tools for weather forecasting. Instead of creating a new standalone weather … Read the full story at The Verge.
Old-school physics-based weather forecasting and even new AI models typically become less reliable beyond two weeks, a problem Microsoft claims to have solved using machine learning. Researchers with the company say they’ve found a new, cost-effective way to make more accurate weather forecasts two to six weeks in advance — allowing farmers and emergency responders to plan ahead with greater certainty. The findings were published today in the journal…
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