3 research outputs found
GraphCast: Learning skillful medium-range global weather forecasting
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.Comment: Main text: 21 pages, 8 figures, 1 table. Appendix: 15 pages, 5
figures, 2 table
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather
forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to
accelerate progress in data-driven weather modeling. WeatherBench 2 consists of
an open-source evaluation framework, publicly available training, ground truth
and baseline data as well as a continuously updated website with the latest
metrics and state-of-the-art models:
https://sites.research.google/weatherbench. This paper describes the design
principles of the evaluation framework and presents results for current
state-of-the-art physical and data-driven weather models. The metrics are based
on established practices for evaluating weather forecasts at leading
operational weather centers. We define a set of headline scores to provide an
overview of model performance. In addition, we also discuss caveats in the
current evaluation setup and challenges for the future of data-driven weather
forecasting
WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
Abstract WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting