Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency between multisensor satellite aerosol products

Abstract. The Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert.

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Publication Details

Journal
Atmospheric chemistry and physics
Published
2025-07-14
DOI
https://doi.org/10.5194/acp-25-7403-2025
Citations
1
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
1.11

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article

Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency between multisensor satellite aerosol products

Jun Wang, Xin Xi, Zhendong Lu, Alexei Lyapustin et al.
1 citations
Atmospheric chemistry and physics
Atmospheric aerosols and clouds
1.11
article

Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency between multisensor satellite aerosol products

Jun Wang, Xin Xi, Zhendong Lu, Alexei Lyapustin, James A. Limbacher, A. M. Sayer, R. C. Levy, Omar Torres, C. Ahn, István László, Sabur F. Abdullaev, Hongqing Liu, Ralph A. Kahn, Yujie Wang, Jaehwa Lee
article en
1 citations

Abstract

Abstract. The Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert.

Atmospheric chemistry and physicsVol. 25(13)
Michigan Technological University (US), University of Iowa (US), Goddard Space Flight Center (US), National Oceanic and Atmospheric Administration (US), University of Colorado Boulder (US), Academy of Sciences of the Republic of Tajikistan (TJ), NOAA National Environmental Satellite Data and Information Service (US), Laboratory for Atmospheric and Space Physics (US), IM Systems (United States) (US), NOAA Center for Satellite Applications and Research (US), Earth System Science Interdisciplinary Center (US), Science Systems and Applications (United States) (US), Science and Technology Corporation (United States) (US), University of Maryland, College Park (US), University of Maryland, Baltimore County (US)
National Aeronautics and Space Administration
Openalex Percentile: Top 22%
Atmospheric aerosols and clouds
1.11
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