- Nvidia Earth-2 accelerates weather forecasting and reduces computational costs significantly
- Earth-2 includes CorrDiff, FourCastNet3, Medium Range, Nowcasting, Global Data Assimilation, and the PhysicsNeMo framework
- Energy companies rely on Earth-2 to improve grid reliability and photovoltaic predictions
Nvidia has unveiled its new Earth-2 family of open AI models, which it says could transform weather forecasting and climate prediction as we know it.
The Nvidia Earth-2 family includes CorrDiff, FourCastNet3, Medium Range, Nowcasting, Global Data Assimilation, and the PhysicsNeMo framework for training and fine-tuning AI-physics models.
These models integrate high-resolution data from satellites, radar, and weather stations to provide continuous estimates of atmospheric conditions.
High-resolution modeling for rapid forecasts
Earth-2 uses generative AI to accelerate every stage of forecasting, from processing observational data to generating global and localized storm predictions.
CorrDiff uses a generative AI architecture to downscale coarse continental predictions into high-resolution regional forecasts, producing results up to 500x faster than traditional methods.
FourCastNet3 delivers accurate forecasts for wind, temperature, and humidity, surpassing conventional ensemble models while providing predictions up to 60x faster.
The system also integrates models from the European Centre for Medium-Range Weather Forecasts, Microsoft, and Google, enabling users to combine multiple approaches within a single framework.
Nvidia’s PhysicsNeMo allows AI-physics models to train and fine-tune at scale, offering flexibility for both operational forecasting and scientific research.
Earth-2 Global Data Assimilation produces initial atmospheric conditions in seconds on GPUs rather than hours on supercomputers, enabling faster integration into downstream models.
Organizations across research, energy, and government sectors already use these AI tools to improve forecast accuracy and reduce computational costs.
The Israel Meteorological Service already uses CorrDiff and plans to deploy Nowcasting for high-resolution predictions up to eight times daily.
Energy companies such as TotalEnergies, Eni, and GCL are testing Earth-2 to improve grid operations, short-term risk awareness, and photovoltaic forecasting.
Brightband and meteorologists in Taiwan use Earth-2 CorrDiff and Medium Range to deliver accurate global and local forecasts, and The Weather Company is now evaluating Nowcasting for ultra-short-term local storm predictions.
These AI tools reduce computational demand, with some models reporting a 90% reduction in compute time compared with classic methods on CPU clusters.
The open source availability of Earth-2 on platforms like Hugging Face and GitHub allows researchers, enterprises, and startups to fine-tune forecasts for local conditions.
By combining multiple models and AI tools, organizations can generate probabilistic and actionable insights that inform decisions in agriculture, energy, disaster response, and insurance risk evaluation.
“Philosophically, scientifically, it’s a return to simplicity…We’re moving away from hand-tailored niche AI architectures and leaning into the future of simple, scalable, transformer architectures,” said Mike Pritchard, director of climate simulation at Nvidia.
“This provides the fundamental building blocks used by everyone in the ecosystem — national meteorological services, financial service firms, energy companies — anyone who wants to build and refine weather forecasting models.”
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