气候变化领域集成服务门户(CCPortal): Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

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DOI	10.1029/2020MS002109
	Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere
	Weyn J.A.; Durran D.R.; Caruana R.
发表日期	2020
ISSN	19422466
卷号	12 期号:9
英文摘要	We present a significantly improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an off-line volume-conservative mapping to a cubed-sphere grid, improvements to the CNN architecture and the minimization of the loss function over multiple steps in a prediction sequence. The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short- to medium-range forecasting, our model significantly outperforms persistence, climatology, and a coarse-resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high-resolution state-of-the-art operational NWP system. Our data-driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top-of-atmosphere solar forcing. Although it currently does not compete with operational weather forecasting models, our data-driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large-ensemble forecasting. ©2020. The Authors.
语种	英语
scopus关键词	Convolution; Convolutional neural networks; Deep neural networks; Spheres; Atmospheric variables; Data-driven model; Ensemble forecasting; Natural boundary condition; Numerical weather prediction models; Top of atmospheres; Weather forecasting model; Weather prediction; Weather forecasting; artificial neural network; boundary condition; climate prediction; climate variation; complexity; machine learning; seasonal variation; weather forecasting
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156635
作者单位	Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; Microsoft Research, Redmond, WA, United States
推荐引用方式 GB/T 7714	Weyn J.A.,Durran D.R.,Caruana R.. Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere[J],2020,12(9).
APA	Weyn J.A.,Durran D.R.,&Caruana R..(2020).Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere.Journal of Advances in Modeling Earth Systems,12(9).
MLA	Weyn J.A.,et al."Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere".Journal of Advances in Modeling Earth Systems 12.9(2020).