论文标题
图形神经网络预测全球天气
Forecasting Global Weather with Graph Neural Networks
论文作者
论文摘要
我们提出了一种数据驱动的方法，用于使用图神经网络预测全球天气。该系统学会了将当前的3D大气状态前进六个小时，并将多个步骤链在一起，以产生未来几天的熟练预测。基础模型对来自ERA5的重新分析数据或GFS的预测数据进行了培训。在先前数据驱动的方法上，诸如Z500（地理位置高度）和T850（温度）等指标的测试性能可以提高，并且与操作，完整分辨率，GFS和ECMWF的物理模型相媲美，至少在对1度尺度以及使用重新分析初始条件时进行评估时。我们还显示了将该数据驱动模型连接到现场直播的GFS的实时预测的结果。
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.
