Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project

Sayer, A. M.; Hsu, N. Christina; Lee, J.; Bettenhausen, C.; Kim, W. V.; Smirnov, A.

doi:10.1002/2017jd027412

Cited by 91 publications

(103 citation statements)

References 75 publications

(132 reference statements)

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“…With the addition of S‐NPP VIIRS data, the DB family now include aerosol products from AVHRR, SeaWiFS, MODIS, and VIIRS (https://deepblue.gsfc.nasa.gov/). While the DB products are only available over land for MODIS, the retrieval coverage has been extended to both land and ocean for VIIRS by combining with the SOAR algorithm (Sayer et al, , ). To continue improving the fidelity of the data products using the most up‐to‐date knowledge of aerosol sciences, several major changes have been made in the VIIRS Deep Blue algorithm compared to MODIS C6.…”

Section: Discussionmentioning

confidence: 99%

“…It is noted that in the SOAR over‐water data set, classification is based instead on the best fit choice from four aerosol optical models (Sayer et al, ). Three of these map into the background, dust, and mixed categories used in the over‐land algorithm, but there is currently no separation between smoke and nonsmoke fine‐mode dominated aerosols.…”

Section: Updates To the Viirs Db Algorithm Compared To Modis C6mentioning

confidence: 99%

“…It is noted that in the SOAR over-water data set, classification is based instead on the best fit choice from four aerosol optical models (Sayer et al, 2018). Three of these map into the background, dust, and mixed…”

Section: 1029/2018jd029688mentioning

confidence: 99%

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VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

et al. 2019

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A primary goal of the Deep Blue (DB) project is to create consistent long‐term aerosol data records, suitable for climate studies, using multiple satellite instruments. In order to continue Earth Observing System (EOS)‐era aerosol products into the Joint Polar Satellite System era, we have successfully ported the DB algorithm to process data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Although the basic structure of the VIIRS algorithm is similar to that for the Moderate Resolution Imaging Spectroradiometer (MODIS), many enhancements have been made compared to the MODIS collection 6 (C6) version. Most have also been implemented in the latest MODIS Collection 6.1 (C6.1). For example, a new smoke mask was developed based on the spectral curvature of measured reflectance to distinguish biomass burning smoke from weakly absorbing urban/industrial aerosols. Consequently, a new aerosol‐type flag was added into the VIIRS DB data set. In addition, new dust models have been developed to account for the nonsphericity of mineral dust. As a result, a discontinuity in the retrieved aerosol optical depth (AOD) of Saharan dust plumes seen in MODIS C6 products near the boundary between North Africa and the Atlantic has been much reduced. We have also evaluated the VIIRS and MODIS Terra/Aqua C6.1 AOD against Aerosol Robotic Network data. VIIRS and MODIS retrievals show similar performance; around 80% of matchups agree with Aerosol Robotic Network within the expected error of ±(0.05 + 20)%, indicating that DB can provide consistent AOD through the historical EOS and present Joint Polar Satellite System eras.

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Section: Discussionmentioning

confidence: 99%

Section: Updates To the Viirs Db Algorithm Compared To Modis C6mentioning

confidence: 99%

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VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

et al. 2019

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“…Some reviews of basic algorithm concepts which have been applied to various sensors types are provided by Kokhanovsky and de Leeuw () and Lenoble et al (). This study is concerned with the Satellite Ocean Aerosol Retrieval (SOAR) algorithm, which was developed initially for application to SeaWiFS (Sayer et al, ) but has since been applied to AVHRR (Hsu et al, ) and VIIRS (Sayer, Hsu, Bettenhausen, et al, ; Sayer et al, ) measurements. The motivation behind SOAR was to provide an overwater companion to the over‐land Deep Blue (DB) algorithm, which has been applied to SeaWiFS, MODIS, AVHRR, and VIIRS measurements (Hsu et al, , ), the combination together providing near‐global data coverage (for daytime scenes free from clouds and snow/ice).…”

Section: Introductionmentioning

confidence: 99%

“…The motivation behind SOAR was to provide an overwater companion to the over‐land Deep Blue (DB) algorithm, which has been applied to SeaWiFS, MODIS, AVHRR, and VIIRS measurements (Hsu et al, , ), the combination together providing near‐global data coverage (for daytime scenes free from clouds and snow/ice). Recently, Sayer et al () described updates and modifications to SOAR to create the recently released VIIRS DB/SOAR version 1 data set; the purpose of the present study is to provide an evaluation of the over‐water part of this new data set, which is freely available for download from the National Aeronautics and Space Administration Level 1 and Atmosphere Archive and Distribution System at https://ladsweb.nascom.nasa.gov.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.Plain Language Summary Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever and newer satellites often have improved capabilities compared to older ones. This paper evaluates an extension of an algorithm, originally designed to monitor aerosols from an older satellite instrument, to a new satellite instrument called Visible Infrared Imaging Radiometer Suite. The evaluation is performed by comparing to ground truth data which are part of the National Aeronautics and Space Administration's global Aerosol Robotic Network, as well as to other satellite-based aerosol data sets from different spaceborne instruments.

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Impacts of aerosols produced by biomass burning on the stratocumulus‐to‐cumulus transition in the equatorial Atlantic

Ajoku

Miller

Norris

2021

Atmospheric Science Letters

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The impact of aerosols produced by biomass burning on the stratocumulus-tocumulus transition (SCT) in the equatorial Atlantic is studied using satellitebased and reanalysis data for the month of June. The month of June is highlighted because it represents monsoon onset as well as the largest sea surface temperature gradient in the summer, which is the peak season of tropical African biomass burning. Boundary layer deepening and increasing temperatures put the location of the SCT within the Gulf of Guinea. Satellite retrievals indicate that the bulk of aerosols occur near 1,500 m in altitude, either above or below the boundary layer depending on latitudinal position. Changes in smoke loading over the Gulf of Guinea due to greater transport from southern Africa leads to increases in low-level cloud cover above cloud decks and decreases when mixed within the boundary layer. Further south, we find significant changes to temperature, cloud top height, tropospheric stability and moisture availability near maximum aerosol loading. In addition, changes in vertical velocity during dirty conditions further reinforce changes in tropospheric stability. These effects combine to shorten the SCT in space during increased aerosol loading episodes.

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Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project

Cited by 91 publications

References 75 publications

VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

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

Impacts of aerosols produced by biomass burning on the stratocumulus‐to‐cumulus transition in the equatorial Atlantic

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