Accurate medium-range global weather forecasting with 3D neural networks.

ABSTRACT: Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states¹. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods² have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors ar

SUBMITTER: Bi K 

PROVIDER: S-EPMC10356604 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature