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Validation of SOAR VIIRS Over-Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record

Abstract : This study validates aerosol properties retrieved using a Satellite Ocean Aerosol Retrieval (SOAR) algorithm applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements, from Version 1 of the VIIRS Deep Blue data set. SOAR is the over-water complement to the over-land Deep Blue algorithm and has two processing paths: globally, 95% of pixels are processed with the full retrieval algorithm, while the 5% of pixels in shallow or turbid (mostly coastal) waters are processed with a backup algorithm. Aerosol Robotic Network (AERONET) data are used to validate and compare the midvisible (550 nm) aerosol optical depth (AOD), Ångström exponent (AE), and fine mode fraction of AOD at 550 nm (FMF). AOD uncertainty is shown to be approximately ±(0.03 + 10%) for the full and ±(0.03 + 15%) for the backup algorithms, with a small positive median bias around 0.02. When AOD is below about 0.2, the AE and FMF have small negative offsets from AERONET around -0.15 and -0.04, respectively. For higher AOD, AE is less offset and the magnitudes of differences versus AERONET are about ±0.2 and ±0.14, respectively. Aerosol-type classifications provided by SOAR are found to be reasonable, matching optical-based classifications from AERONET over 80% of the time. Spatial and temporal patterns of AOD and AE are also compared with those of other contemporary over-water satellite aerosol data sets; dependent on region, the satellite data sets show varying levels of consistency, with SOAR broadly in-family, and the largest discrepancies in regions with persistent heavy cloud cover.
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Submitted on : Friday, June 3, 2022 - 10:05:48 AM
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JGR Atmospheres - 2018 - Sayer...
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Andrew M. Sayer, N. Â. Christina Hsu, Jaehwa Lee, Woogyung V. Kim, Oleg Dubovik, et al.. Validation of SOAR VIIRS Over-Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record. Journal of Geophysical Research: Atmospheres, 2018, 123, pp.13,496-13,526. ⟨10.1029/2018JD029465⟩. ⟨insu-03686250⟩



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