University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK
University of Manchester retrained NVIDIA Earth-2 CorrDiff and StormCast on Isambard-AI to forecast UK air pollution at 2-3 km resolution.
University of Manchester researchers led by professor David Topping adapted NVIDIA's Earth-2 generative AI frameworks to forecast air pollution across the UK. Earth-2 CorrDiff was retrained in two days on a single eight-GPU node of Isambard-AI (5,448 GH200 Grace Hopper Superchips, 21 exaflops) using a year of hourly simulated pollution data, producing a UK-wide model at 2-3 square kilometer resolution. The team added Earth-2 StormCast for time-dependent forecasts that ingest real air quality observations, and demonstrated the workflow runs on the DGX Spark desktop AI system. Open-source training data and workflows are planned so other countries and cities can build similar pollution models.
- Earth-2 CorrDiff retrained for UK air pollution in two days on Isambard-AI's 5,448 GH200 superchips.
- Model delivers UK-wide forecasts at 2-3 square kilometer resolution using hourly simulated pollution data.
- StormCast added for time-dependent forecasts that directly use air quality observations.
- Workflow runs inference on DGX Spark desktop systems, lowering cost of entry to a few thousand dollars.
- Open-source training data and workflows planned for other countries and regions.
From the original article
4 min read at blogs.nvidia.com
Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U. K. alone last year.
By Isha Salian · blogs.nvidia.com
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