Abstract
We introduce a machine-learning (ML)-based weather simulator--called
"GraphCast"--which outperforms the most accurate deterministic operational
medium-range weather forecasting system in the world, as well as all previous
ML baselines. GraphCast is an autoregressive model, based on graph neural
networks and a novel high-resolution multi-scale mesh representation, which we
trained on historical weather data from the European Centre for Medium-Range
Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day
forecasts, at 6-hour time intervals, of five surface variables and six
atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree
latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer
resolution at the equator. Our results show GraphCast is more accurate than
ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the
2760 variable and lead time combinations we evaluated. GraphCast also
outperforms the most accurate previous ML-based weather forecasting model on
99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast
(35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike
traditional forecasting methods, ML-based forecasting scales well with data: by
training on bigger, higher quality, and more recent data, the skill of the
forecasts can improve. Together these results represent a key step forward in
complementing and improving weather modeling with ML, open new opportunities
for fast, accurate forecasting, and help realize the promise of ML-based
simulation in the physical sciences.
Description
GraphCast: Learning skillful medium-range global weather forecasting
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