We present a data-driven approach for forecasting global weather using graph neural networks. The system learns to step forward the current 3D atmospheric state by six hours, and multiple steps are chained together to produce skillful forecasts going out several days into the future. The underlying model is trained on reanalysis data from ERA5 or forecast data from GFS. Test performance on metrics such as Z500 (geopotential height) and T850 (temperature) improves upon previous data-driven approaches and is comparable to operational, full-resolution, physical models from GFS and ECMWF, at least when evaluated on 1-degree scales and when using reanalysis initial conditions. We also show results from connecting this data-driven model to live, operational forecasts from GFS.

翻译：该系统学会了将目前的3D大气状态向前推进6小时,并联结了多个步骤,以得出未来数天的熟练预测。基础模型经过培训,从ERA5或GFS的预测数据进行再分析。Z500(地热高度)和T850(温度)等指标的测试性能比以前的数据驱动方法有所改善,并且与GFS和ECMWF的操作性、完全分辨率、物理模型相仿,至少在对1度尺度进行评估和使用再分析初始条件时,我们还展示了将这一数据驱动模型与GFS的运行性预测联系起来的结果。