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Atmosphere 2018, 9(8), 288;

Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia

School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
Author to whom correspondence should be addressed.
Received: 8 June 2018 / Revised: 18 July 2018 / Accepted: 19 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue Radiative Transfer in the Earth Atmosphere)
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Several long-term monitoring of aerosol datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra/Aqua, Multi-angle Imaging SpectroRadiometer (MISR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) were used to derive the dust aerosol optical depth (DOD) in Central Asia based on the Angstrom exponent parameter and/or the particle shape. All sensors agree very well on the interannual variability of DOD. The seasonal analysis of DOD and dust occurrences identified the largest dust loading and the most frequent dust occurrence in the spring and summer, respectively. No significant trend was found during the research period in terms of both DOD and the dust occurrence. Further analysis of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol products on a case-by-case basis in most dust months of 2007 suggested that the vertical structure is varying in terms of the extension and the dust loading from one event to another, although dust particles of most episodes have similar physical characteristics (particle shape and size). Our analysis on the vertical structure of dust plumes, the layer-integrated color ratio and depolarization ratio indicates a varied climate effect (e.g., the direct radiative impact) by mineral dust, dependent on the event being observed in Central Asia. View Full-Text
Keywords: dust aerosol; Central Asia; remote sensing; seasonal variation; inter-annual variability dust aerosol; Central Asia; remote sensing; seasonal variation; inter-annual variability

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Li, L.; Sokolik, I.N. Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia. Atmosphere 2018, 9, 288.

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