气候变化领域集成服务门户(CCPortal): Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data

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DOI	10.1029/2019MS001705
	Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data
	Weyn J.A.; Durran D.R.; Caruana R.
发表日期	2019
ISSN	19422466
起始页码	2680
结束页码	2693
卷号	11 期号:8
英文摘要	We develop elementary weather prediction models using deep convolutional neural networks (CNNs) trained on past weather data to forecast one or two fundamental meteorological fields on a Northern Hemisphere grid with no explicit knowledge about physical processes. At forecast lead times up to 3 days, CNNs trained to predict only 500-hPa geopotential height easily outperform persistence, climatology, and the dynamics-based barotropic vorticity model, but do not beat an operational full-physics weather prediction model. These CNNs are capable of forecasting significant changes in the intensity of weather systems, which is notable because this is beyond the capability of the fundamental dynamical equation that relies solely on 500-hPa data, the barotropic vorticity equation. Modest improvements to the CNN forecasts can be made by adding 700- to 300-hPa thickness to the input data. Our best performing CNN does a good job of capturing the climatology and annual variability of 500-hPa heights and is capable of forecasting realistic atmospheric states at lead times of 14 days. Although our simple models do not perform better than an operational weather model, machine learning warrants further exploration as a weather forecasting tool; in particular, the potential efficiency of CNNs might make them attractive for ensemble forecasting. © 2019. The Authors.
英文关键词	deep learning; machine learning; neural network; weather prediction
语种	英语
scopus关键词	Climatology; Deep learning; Deep neural networks; Learning systems; Machine learning; Neural networks; Vorticity; Barotropic vorticity equations; Barotropic vorticity model; Convolutional neural network; Geo-potential heights; Historical weather datum; Meteorological fields; Weather prediction; Weather prediction model; Weather forecasting; algorithm; annual variation; artificial neural network; data set; ensemble forecasting; machine learning; Northern Hemisphere; 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/156876
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.. Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data[J],2019,11(8).
APA	Weyn J.A.,Durran D.R.,&Caruana R..(2019).Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data.Journal of Advances in Modeling Earth Systems,11(8).
MLA	Weyn J.A.,et al."Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data".Journal of Advances in Modeling Earth Systems 11.8(2019).