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.

使用图神经网络的数据驱动方法预测全球天气，通过学习当前 3D 大气状态的六小时步长以及多步连接，可以产生数天后的精准预报，测试表现优于以前的数据驱动方法，并且与 GFS 和 ECMWF 的物理模型的全分辨率相当可比。

应用图神经网络预测全球天气

Forecasting Global Weather with Graph Neural Networks

General circulation models (GCMs) typically have a grid size of 25--200 km.
Parametrizations are used to represent diabatic processes such as radiative
transfer and cloud microphysics and account for sub-grid-scale motions and
variability. Unlike traditional approaches, neural networks (NNs) can readily
exploit recent observational datasets and global cloud-system resolving model
(CRM) simulations to learn subgrid variability. This article describes an NN
parametrization trained by coarse-graining a near-global CRM simulation with a
4~km horizontal grid spacing. The NN predicts the residual heating and
moistening averaged over (160 km)^2 grid boxes as a function of the
coarse-resolution fields within the same atmospheric column. This NN is coupled
to the dynamical core of a GCM with the same 160 km resolution. A recent study
described how to train such an NN to be numerically stable when coupled to
specified time-evolving advective forcings in a single column model, but
feedbacks between NN and GCM components cause spatially-extended simulations to
crash within a few days. Analyzing the linearized response of such an NN
reveals that it learns to exploit a strong synchrony between precipitation and
the atmospheric state above 10 km. Removing these variables from the NN's
inputs stabilizes the coupled simulations, which predict the future state more
accurately than a coarse-resolution simulation without any parametrizations of
sub-grid-scale variability, although the mean state slowly drifts.

本篇文章研究如何使用神经网络对大气环流系统的参数进行建模，从而更精确地预测系统的状态。