气候变化领域集成服务门户(CCPortal): A Machine Learning-Based Global Atmospheric Forecast Model

CCPortal

DOI	10.1029/2020GL087776
	A Machine Learning-Based Global Atmospheric Forecast Model
	Arcomano T.; Szunyogh I.; Pathak J.; Wikner A.; Hunt B.R.; Ott E.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:9
英文摘要	The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir computing-based, low-resolution, global prediction model. The model is designed to take advantage of the massively parallel architecture of a modern supercomputer. The forecast performance of the model is assessed by comparing it to that of daily climatology, persistence, and a numerical (physics-based) model of identical prognostic state variables and resolution. Hourly resolution 20-day forecasts with the model predict realistic values of the atmospheric state variables at all forecast times for the entire globe. The ML model outperforms both climatology and persistence for the first three forecast days in the midlatitudes, but not in the tropics. Compared to the numerical model, the ML model performs best for the state variables most affected by parameterized processes in the numerical model. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Climatology; Machine learning; Numerical models; Parallel architectures; Supercomputers; FORECAST model; Forecast performance; Global predictions; Low resolution; Physics-based; Reservoir Computing; State variables; Weather prediction; Weather forecasting; atmospheric dynamics; climate modeling; computer; global climate; global perspective; machine learning; persistence; weather forecasting
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170386
作者单位	Department of Atmospheric Sciences, Texas A&M University, College Station, TX, United States; Department of Physics, University of Maryland, College Park, MD, United States; Department of Mathematics, University of Maryland, College Park, MD, United States; Department of Physics and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
推荐引用方式 GB/T 7714	Arcomano T.,Szunyogh I.,Pathak J.,et al. A Machine Learning-Based Global Atmospheric Forecast Model[J],2020,47(9).
APA	Arcomano T.,Szunyogh I.,Pathak J.,Wikner A.,Hunt B.R.,&Ott E..(2020).A Machine Learning-Based Global Atmospheric Forecast Model.Geophysical Research Letters,47(9).
MLA	Arcomano T.,et al."A Machine Learning-Based Global Atmospheric Forecast Model".Geophysical Research Letters 47.9(2020).