Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but does not directly use historical weather data to improve the underlying model. Here, we introduce “GraphCast,” a machine learning-based method trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25° resolution globally, in under one minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems.
The dominant approach for weather forecasting today is “numerical weather prediction” (NWP), which involves solving the governing equations of weather using supercomputers.
[…]NWP methods are improved by highly trained experts innovating better models, algorithms, and approximations, which can be a time-consuming and costly process.Machine learning-based weather prediction (MLWP) offers an alternative to traditional NWP, where forecast models can be trained from historical data, including observations and analysis data.[…]In medium-range weather forecasting, i.e., predicting atmospheric variables up to 10 days ahead, NWP-based systems like the IFS are still most accurate. The top deterministic operational system in the world is ECMWF’s High RESolution forecast (HRES), a configuration of IFS which produces global 10-day forecasts at 0.1° latitude/longitude resolution, in around an hour[…]Here we introduce an MLWP approach for global medium-range weather forecasting called “GraphCast,” which produces an accurate 10-day forecast in under a minute on a single Google Cloud TPU v4 device, and supports applications including predicting tropical cyclone tracks, atmospheric rivers, and extreme temperatures.[…]A single weather state is represented by a 0.25° latitude/longitude grid[…]GraphCast is implemented as a neural network architecture, based on GNNs in an “encode-process-decode” configuration (13, 17), with a total of 36.7 million parameters (code, weights and demos can be found at https://github.com/deepmind/graphcast).[…]During model development, we used 39 years (1979–2017) of historical data from ECMWF’s ERA5 (21) reanalysis archive.[…]Of the 227 variable and level combinations predicted by GraphCast at each grid point, we evaluated its skill versus HRES on 69 of them, corresponding to the 13 levels of WeatherBench (8) and variables (23) from the ECMWF Scorecard (24)[…]We find that GraphCast has greater weather forecasting skill than HRES when evaluated on 10-day forecasts at a horizontal resolution of 0.25° for latitude/longitude and at 13 vertical levels.[NOTE HRES has a resolution of 0.1°][…]We also compared GraphCast’s performance to the top competing ML-based weather model, Pangu-Weather (16), and found GraphCast outperformed it on 99.2% of the 252 targets they presented (see supplementary materials section 6 for details).[…]GraphCast’s forecast skill and efficiency compared to HRES shows MLWP methods are now competitive with traditional weather forecasting methods[…]With 36.7 million parameters, GraphCast is a relatively small model by modern ML standards, chosen to keep the memory footprint tractable. And while HRES is released on 0.1° resolution, 137 levels, and up to 1 hour time steps, GraphCast operated on 0.25° latitude-longitude resolution, 37 vertical levels, and 6 hour time steps, because of the ERA5 training data’s native 0.25° resolution, and engineering challenges in fitting higher resolution data on hardware.[…]Our approach should not be regarded as a replacement for traditional weather forecasting methods, which have been developed for decades, rigorously tested in many real-world contexts, and offer many features we have not yet explored. Rather our work should be interpreted as evidence that MLWP is able to meet the challenges of real-world forecasting problems and has potential to complement and improve the current best methods.[…]
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