气候变化领域集成服务门户(CCPortal): Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms

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DOI	10.1016/j.rse.2019.111616
	Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms
	Min M.; Li J.; Wang F.; Liu Z.; Menzel W.P.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	The cloud-top height (CTH) product derived from passive satellite instrument measurements is often used to make climate data records (CDR). CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) provides CTH parameters with high accuracy, but with limited temporal-spatial resolution. Recently, the Advanced Himawari Imager (AHI) onboard Japanese Himawari-8/-9, provides high temporal (every 10 min) and high spatial (2 km at nadir) resolution measurements with 16 spectral bands. This paper reports on a study to derive the CTH from combined AHI and CALIPSO using advanced machine learning (ML) algorithms with better accuracy than that from the traditional physical (TRA) algorithms. We find significant CTH improvements (1.54–2.72 km for mean absolute error, MAE) from four different machine learning algorithms (original MAE from TRA method is about 3.24 km based on CALIPSO data validation), particularly in high and optically thin clouds. In addition, we also develop a joint algorithm to combine optimal machine learning and traditional physical (TRA) algorithms of CTH to further reduce MAE to 1.53 km and enhance the layered accuracy (CTH < 18 km). While the ML-based algorithm improves CTH retrieval over the TRA algorithm, the lower or higher clouds still exhibit relatively large uncertainty. Combining both methods provides the better CTH than either alone. The combined approach could be used to process data from advanced geostationary imagers for climate and weather applications. © 2019
英文关键词	CALIPSO; Cloud top height; Himawari-8; Machine learning
语种	英语
scopus关键词	Geostationary satellites; Learning systems; Machine learning; CALIPSO; Climate data records; Cloud top heights; Cloud-aerosol lidar and infrared pathfinder satellite observations; Geostationary imagers; Himawari-8; Mean absolute error; Resolution measurements; Learning algorithms; accuracy assessment; algorithm; CALIPSO; cloud; error analysis; geostationary satellite; lidar; machine learning; measurement method; nadir; parameterization; satellite imagery; spatial resolution; spatiotemporal analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179470
作者单位	School of Atmospheric Sciences and Guangdong Province Key laboratory for Climate Change and Natural Disaster Studies, Sun Yat-Sen University and Southern Laboratory of Ocean Science and Engineering (Guangdong, Zhuhai), Zhuhai, 519082, China; Cooperative Institute for Meteorological Satellite Study (CIMSS), University of Wisconsin-Madison, Madison, WI, United States; Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites (LRCVES/CMA), National Satellite Meteorological Center, China Meteorological Administration (NSMC/CMA), Beijing, 100081, China
推荐引用方式 GB/T 7714	Min M.,Li J.,Wang F.,et al. Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms[J],2020,239.
APA	Min M.,Li J.,Wang F.,Liu Z.,&Menzel W.P..(2020).Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms.Remote Sensing of Environment,239.
MLA	Min M.,et al."Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms".Remote Sensing of Environment 239(2020).