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Open AccessArticle

Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint

1
School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Korea
2
NASA Langley Research Center, Hampton, VA 23666, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 251; https://doi.org/10.3390/rs12020251
Received: 26 November 2019 / Revised: 27 December 2019 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Section Atmosphere Remote Sensing)
Lidar ratio for dust aerosols is retrieved from a synergetic use of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Total Attenuated Backscatter coefficients and the Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depths (AODs) for 5 years from 2007 to 2011. MODIS AODs from the Dark Target (DT) algorithm over ocean and from the Deep Blue (DB) algorithm over land are used as a constraint for the retrieval. The dust lidar ratio is retrieved larger over land (46.6 ± 36.3 sr) than ocean (39.5 ± 16.8 sr) and shows distinct regional variation. Lidar ratio for Saharan dust (49.5 ± 36.8 sr) is larger than Arabian dust (42.5 ± 26.2 sr). Lidar ratios for dust aerosols transported to Mediterranean Sea (44.4 ± 15.9 sr), Mid Atlantic (40.3 ± 12.4 sr), and Arabian Sea (37.5 ± 12.1 sr) show lower values relative to their source regions. Retrieved dust lidar ratios for Taklamakan and Gobi Deserts region (35.0 ± 31.1 sr) and Australia (35.4 ± 34.4 sr) are slightly lower than the above-mentioned regions. AOD comparison between CALIOP and MODIS shows that the CALIOP AOD is biased low. When including clear air AOD for CALIOP, however, AODs from two sensors become more comparable. View Full-Text
Keywords: lidar ratio; dust; aerosol optical depth (AOD); CALIOP; MODIS lidar ratio; dust; aerosol optical depth (AOD); CALIOP; MODIS
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Kim, M.-H.; Kim, S.-W.; Omar, A.H. Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint. Remote Sens. 2020, 12, 251.

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