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Remote Sensing
Volume 14
Issue 16
10.3390/rs14163844
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Article

Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China

by
Yanglian Li
1,
Tianyi Fan
1,*,
Chuanfeng Zhao
2,
Xin Yang
1,
Ping Zhou
1 and
Keying Li
1
1
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(16), 3844; https://doi.org/10.3390/rs14163844
Submission received: 4 July 2022 / Revised: 1 August 2022 / Accepted: 3 August 2022 / Published: 9 August 2022
(This article belongs to the Special Issue Remote Sensing of Air Pollution)
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Abstract

Aerosols modify cloud properties and influence the regional climate. The impacts of aerosols on clouds differ for various cloud types, but their long-term relationships have not been fully characterized on a cloud regime basis. In this study, we quantified the cloud regime-dependent relationship between aerosol optical depth (AOD) and cloud properties over China using Moderate-Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2019. Daily clouds in each 1° by 1° grid were categorized into seven cloud regimes based on the “k-means” clustering algorithm. Overall, the cloud height increased, the cloud thickness and liquid water path increased, and the total cloud cover decreased for all cloud regimes during the study period. Linear correlations between AOD and cloud properties were found within stratocumulus, deep convective, and high cloud regimes, showing consistency with the classic aerosol–cloud interaction paradigms. Using stepwise multivariable linear regression, we found that the meteorological factors dominated the variation of cloud top pressure, while AOD dominated the variation of total cloud cover for most cloud regimes. There are regional differences in the main meteorological factors affecting the cloud properties.
Keywords: aerosol–cloud interaction; cloud regimes; k-means clustering aerosol–cloud interaction; cloud regimes; k-means clustering
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MDPI and ACS Style

Li, Y.; Fan, T.; Zhao, C.; Yang, X.; Zhou, P.; Li, K. Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China. Remote Sens. 2022, 14, 3844. https://doi.org/10.3390/rs14163844

AMA Style

Li Y, Fan T, Zhao C, Yang X, Zhou P, Li K. Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China. Remote Sensing. 2022; 14(16):3844. https://doi.org/10.3390/rs14163844

Chicago/Turabian Style

Li, Yanglian, Tianyi Fan, Chuanfeng Zhao, Xin Yang, Ping Zhou, and Keying Li. 2022. "Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China" Remote Sensing 14, no. 16: 3844. https://doi.org/10.3390/rs14163844

APA Style

Li, Y., Fan, T., Zhao, C., Yang, X., Zhou, P., & Li, K. (2022). Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China. Remote Sensing, 14(16), 3844. https://doi.org/10.3390/rs14163844

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