气候变化领域集成服务门户(CCPortal): Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

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DOI	10.1016/j.atmosres.2021.105767
	Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm
	Hu J.; Rosenfeld D.; Zhu Y.; Lu X.; Carlin J.
发表日期	2021
ISSN	0169-8095
卷号	261
英文摘要	The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. This study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain. © 2021 Elsevier B.V.
英文关键词	Cloud top phase; Geostationary Satellite; Machine learning
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236639
作者单位	Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, NOAA/OAR National Severe Storms Laboratory, Norman, OK, United States; Department of Atmospheric Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel; School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China; Joint International Research Laboratory of Atmospheric and Earth System Sciences, Institute for Climate and Global Change Research, Nanjing University, China; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
推荐引用方式 GB/T 7714	Hu J.,Rosenfeld D.,Zhu Y.,et al. Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm[J],2021,261.
APA	Hu J.,Rosenfeld D.,Zhu Y.,Lu X.,&Carlin J..(2021).Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm.Atmospheric Research,261.
MLA	Hu J.,et al."Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm".Atmospheric Research 261(2021).