Accurate weather forecasting isn’t just about knowing whether to pack an umbrella; it’s a critical tool for safeguarding lives, protecting our environment, and making informed decisions across vital sectors like agriculture, energy, and public health.
AI-Powered Forecasts Are Revolutionizing Weather Prediction
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New AI models are dramatically improving weather prediction, offering faster, more accurate insights for businesses and communities alike.
- AI weather models are reducing computational costs and improving forecast accuracy.
- The Israel Meteorological Service is already seeing a 90% reduction in compute time with AI.
- Energy companies and financial institutions are leveraging these advancements for better risk assessment.
- Open-source models are accelerating innovation in the weather enterprise.
AI weather tool provider Brightband, a member of the Lasting Futures initiative, is now running Earth-2 Medium Range to issue daily global forecasts. “The revolution of new AI weather tools for forecasting is very exciting and continues to gather speed with new models like NVIDIA Earth-2 Medium Range,” said Julian Green, cofounder and CEO of Brightband. “Brightband is among the first to run Earth-2 Medium Range operationally, and the model being open source speeds up innovation, allowing easier comparison and improvements by other members of the weather enterprise.”
What’s the biggest benefit of these new AI weather models? They substantially reduce the time and resources needed to generate accurate forecasts, opening up possibilities for more frequent and detailed predictions.
Weather Forecasting Gets a Boost
The Israel Meteorological Service is utilizing Earth-2 CorrDiff in its operations and plans to integrate Earth-2 Nowcasting to produce high-resolution forecasts up to eight times daily. This enhanced capability will empower decision-makers to respond more effectively to extreme weather events while together lowering computational expenses.
“NVIDIA Earth-2 models give us a 90% reduction in compute time at 2.5-kilometer resolution compared with running a classic numerical weather prediction model without AI on a CPU cluster,” said Amir Givati, director of the Israel Meteorological Service. “After a recent rainstorm, our AI model trained with CorrDiff was the best of all our operational models for a six-hour verification of accumulated precipitation.”
The Weather Company is evaluating Earth-2 Nowcasting for localized severe-weather applications, and NWS is evaluating the new models to enhance its operational workflows.
Energy and grid Operations See Improvements
TotalEnergies is assessing Earth-2 Nowcasting to refine its short-term risk awareness and decision-making processes. “NVIDIA Earth-2 represents a major step forward in how advanced weather intelligence can be operationalized at scale,” said Emmanuel Le Borgne, climate and weather forecast product manager at TotalEnergies. “Models like Earth-2 Nowcasting are groundbreaking for our business because they improve short-term risk awareness and decision-making in energy systems were minutes and local impacts matter.”
Eni is rigorously testing Earth-2 models, including FourCastNet and CorrDiff, to downscale predictions and generate probabilistic, high-resolution forecasts of weather and gas demand weeks in advance. GCL, a major Chinese solar material producer, is already running NVIDIA Earth-2 models operationally for its photovoltaic prediction system, achieving more accurate predictions at a lower cost and improving the accuracy of its power generation forecasts.
Southwest Power Pool, in collaboration with Hitachi, is employing Earth-2 Nowcasting and FourCastNet3 to enhance intraday and day-ahead wind forecasting, bolstering grid reliability and enabling more informed operational decisions.
Financial Impact Assessment Gains Precision
S&P Global Energy is leveraging NVIDIA Earth-2 CorrDiff to transform climate data into localized insights for risk assessment. Global insurance group AXA is using FourCastNet to generate thousands of hypothetical hurricane scenarios as part of its research and development program, focusing on model evaluation and benchmarking.
