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      Comparison of AOD from CALIPSO, MODIS, and Sun Photometer under Different Conditions over Central China

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          Abstract

          Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) provides three-dimensional information on aerosol optical properties across the globe. However, the performance of CALIPSO aerosol optical depth (AOD) products under different air quality conditions remains unclear. In this research, three years of CALIPSO level 2 AOD data (November 2013 to December 2017) were employed to compare with the Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 columnar AOD products and ground-based sun photometer measurements for the same time period. To investigate the effect of air quality on AODs retrieved from CALIPSO, the AODs obtained from CALIPSO, MODIS, and sun photometer were inter-compared under different air quality conditions over Wuhan and Dengfeng. The average absolute bias of AOD between CALIPSO and sun photometer was 0.22 ± 0.21, 0.11 ± 0.07, and 0.14 ± 0.13 under clean, moderate, and polluted weather, respectively. The result indicates that the CALIPSO AOD were more reliable under moderate and polluted days. Moreover, the deviation of AOD between CALIPSO and sun photometer was largest (0.23 ± 0.21) in the autumn season, and lowest (0.13 ± 0.12) in the winter season. The results show that CALIPSO AOD products were more applicable to regions and seasons with high aerosol concentrations.

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          Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms

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            Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols

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              The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm

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                Author and article information

                Contributors
                yym863@whu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 July 2018
                3 July 2018
                2018
                : 8
                : 10066
                Affiliations
                [1 ]ISNI 0000 0001 2331 6153, GRID grid.49470.3e, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, ; Wuhan, China
                [2 ]ISNI 0000 0001 2170 761X, GRID grid.459426.8, Collaborative Innovation Center for Geospatial Technology, ; Wuhan, 430079 China
                Article
                28417
                10.1038/s41598-018-28417-7
                6030172
                29968814
                34ebe3ae-f794-4357-b179-da0f94486994
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 January 2018
                : 21 June 2018
                Funding
                Funded by: National Key Research and Development Program of China (2017YFC0212600)
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