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# NVIDIA Earth-2

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NVIDIA Blog Oct 22, 2026
Weather forecasts move to ten-minute updates: Europe's StormScope turns satellite cloud images into regional nowcasts
NVIDIA has added StormScope EU to its Earth-2 family of open models, a regional version trained on European weather and climate data that uses EUMETSAT satellite observations to predict evolving cloud conditions across 16 Meteosat channels at 10-minute intervals. A global forecast can show a storm taking shape, but decisions about solar farms, shipping routes or insured property depend on what happens locally. At GTC Berlin, the model runs in a live pipeline with Earthmover's data platform, updating as new observations arrive and serving results as map tiles so visitors can explore European predictions without downloading whole datasets.
NVIDIA has added StormScope EU to its Earth-2 family of open models, a regional version trained on European weather and climate data that uses EUMETSAT satellite observations to predict evolving cloud conditions across 16 Meteosat channels at 10-minute intervals. A global forecast can show a storm taking shape, but decisions about solar farms, shipping routes or insured property depend on what happens locally. At GTC Berlin, the model runs in a live pipeline with Earthmover's data platform, updating as new observations arrive and serving results as map tiles so visitors can explore European predictions without downloading whole datasets.
NVIDIA University of Manchester
NVIDIA Earth-2 StormScope EU CorrDiff
NVIDIA Research Sep 16, 2026
University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK
Manchester physicists David Topping and Hao Zhang, working with the NVIDIA Earth-2 team, moved generative models originally built for weather into air quality forecasting: they trained the Earth-2 CorrDiff generative downscaling model on a year of hourly UK chemistry-climate simulation data, completing training in two days on a single eight-GPU node of Isambard-AI to produce a UK-wide pollution model at 2-3 square kilometer resolution, added Earth-2 StormCast for time-dependent forecasts that directly use air quality observations, demonstrated inference and smaller training runs on the DGX Spark desktop AI system, and plan to release open source training data and workflows so other countries and regions can train their own models.
Manchester physicists David Topping and Hao Zhang, working with the NVIDIA Earth-2 team, moved generative models originally built for weather into air quality forecasting: they trained the Earth-2 CorrDiff generative downscaling model on a year of hourly UK chemistry-climate simulation data, completing training in two days on a single eight-GPU node of Isambard-AI to produce a UK-wide pollution model at 2-3 square kilometer resolution, added Earth-2 StormCast for time-dependent forecasts that directly use air quality observations, demonstrated inference and smaller training runs on the DGX Spark desktop AI system, and plan to release open source training data and workflows so other countries and regions can train their own models.
NVIDIA University of Manchester
Air Quality Forecasting NVIDIA Earth-2 Generative Downscaling NVIDIA DGX Spark

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