气候变化领域集成服务门户(CCPortal): A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density

CCPortal

DOI	10.1029/2020GL087140
	A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density
	Weng L.; Lei J.; Zhong J.; Dou X.; Fang H.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:6
英文摘要	In this study, we revisit the long-term trend of thermospheric density by using a Machine-Learning approach. Our Artificial Neural Network Model (ANNM) can better capture the variations in the satellite drag-derived densities than earlier empirical models, especially during extremely solar minimum period of 2007–2009. The long-term trends from ANNM are similar when density data during either 1967–2005 or 1967–2013 intervals are used in the calculation. In addition, our trend estimates relative to the ANNM are −1.5% to −2.0% per decade from 250 to 575 km without obvious solar activity dependence. The Machine-Learning approach provides a good way to give stable long-term trend estimates of thermospheric density. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Neural networks; Solar energy; Solar radiation; Artificial neural network methods; Artificial neural network modeling; Empirical model; Long-term trend; Machine learning approaches; Satellite drag; Solar activity; Thermospheric density; Machine learning; artificial neural network; empirical analysis; long-term change; machine learning; methodology; satellite altimetry; solar activity; thermosphere; trend analysis
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/170568
作者单位	CAS Key Laboratory of Geospace Environment, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China; College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China; CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China; Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei, China; Planetary Environmental and Astrobiological Research Laboratory, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
推荐引用方式 GB/T 7714	Weng L.,Lei J.,Zhong J.,et al. A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density[J],2020,47(6).
APA	Weng L.,Lei J.,Zhong J.,Dou X.,&Fang H..(2020).A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density.Geophysical Research Letters,47(6).
MLA	Weng L.,et al."A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density".Geophysical Research Letters 47.6(2020).