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

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 algorit...

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Veröffentlicht in:Journal of geophysical research. Atmospheres 2018-12, Vol.123 (23), p.13,496-13,526
Hauptverfasser: Sayer, Andrew M., Hsu, N. Christina, Lee, Jaehwa, Kim, Woogyung V., Dubovik, Oleg, Dutcher, Steven T., Huang, Dong, Litvinov, Pavel, Lyapustin, Alexei, Tackett, Jason L., Winker, David M.
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container_end_page 13,526
container_issue 23
container_start_page 13,496
container_title Journal of geophysical research. Atmospheres
container_volume 123
creator Sayer, Andrew M.
Hsu, N. Christina
Lee, Jaehwa
Kim, Woogyung V.
Dubovik, Oleg
Dutcher, Steven T.
Huang, Dong
Litvinov, Pavel
Lyapustin, Alexei
Tackett, Jason L.
Winker, David M.
description 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 fr
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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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Atmospheres, 2018-12, Vol.123 (23), p.13,496-13,526</ispartof><rights>2018. American Geophysical Union. 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Atmospheres</title><description>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. Key Points Based on comparisons to AERONET, the AOD uncertainty is consistent with expectations Retrieved parameters related to aerosol particle size and type are also demonstrated to have skill SOAR shows similar regional and temporal patterns to other satellite data sets</description><subject>Aeronautics</subject><subject>aerosol</subject><subject>Aerosol optical depth</subject><subject>Aerosol properties</subject><subject>Aerosol Robotic Network</subject><subject>Aerosols</subject><subject>Air quality</subject><subject>Algorithms</subject><subject>Atmospheric particulates</subject><subject>Climate studies</subject><subject>Cloud cover</subject><subject>Clouds</subject><subject>Coastal processes</subject><subject>Coastal waters</subject><subject>Data</subject><subject>Datasets</subject><subject>Dust storms</subject><subject>Evaluation</subject><subject>Geophysics</subject><subject>Global aerosols</subject><subject>Ground truth</subject><subject>Haze</subject><subject>Imaging techniques</subject><subject>Infrared imaging</subject><subject>Infrared instruments</subject><subject>Infrared radiometers</subject><subject>Instruments</subject><subject>Meteorological satellites</subject><subject>Offsets</subject><subject>Optical analysis</subject><subject>Pixels</subject><subject>Radiometers</subject><subject>Radiometry</subject><subject>Remote sensing</subject><subject>Retrieval</subject><subject>Satellite data</subject><subject>Satellite instruments</subject><subject>Satellite-borne instruments</subject><subject>Satellites</subject><subject>Sciences of the Universe</subject><subject>Sea spray</subject><subject>Smoke</subject><subject>Spaceborne remote sensing</subject><subject>validation</subject><subject>VIIRS</subject><subject>Volcanic ash</subject><subject>Volcanic dust</subject><subject>water</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kU1OwzAQhSMEEhV0xwEssUMU7PinyTJqoT-qVCmFlp3lOI7qysTFcQvdcQTOyElwVVSxYjYzi-89zcyLoisE7xCM0_sYomTcDxNh9CRqxYilnSRN2elx7r6cR-2mWcFQCcSEklbk58LoUnhta2ArMJtmOZiPRvkMTLfKfX9-LYRXDmTK2cYakCvvtNoK0wBRl6Bna68-PFhov9Q18EsFBsYWwoBZkBmjvTpK-8KLoJfWlZfRWRUsVPu3X0TPjw9PvWFnMh2MetmkIwlhqNNlRFZIYRgzWsZF0kWxVFDKogtlhZUsFcOCUQYRllAKWhBRkFKiRKCCEVTgi-jm4LsUhq-dfhVux63QfJhNuK6bDYeYJSymcIsCfH2A186-bVTj-cpuXB324-F_kNE4TffU7YGS4arGqeroiyDf58D_5hBwfMDftVG7f1k-HuR9ShFC-AcDF4jw</recordid><startdate>20181216</startdate><enddate>20181216</enddate><creator>Sayer, Andrew M.</creator><creator>Hsu, N. Christina</creator><creator>Lee, Jaehwa</creator><creator>Kim, Woogyung V.</creator><creator>Dubovik, Oleg</creator><creator>Dutcher, Steven T.</creator><creator>Huang, Dong</creator><creator>Litvinov, Pavel</creator><creator>Lyapustin, Alexei</creator><creator>Tackett, Jason L.</creator><creator>Winker, David M.</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-9149-1789</orcidid><orcidid>https://orcid.org/0000-0003-1105-5739</orcidid><orcidid>https://orcid.org/0000-0002-3983-8826</orcidid><orcidid>https://orcid.org/0000-0002-3919-2244</orcidid><orcidid>https://orcid.org/0000-0002-5029-476X</orcidid><orcidid>https://orcid.org/0000-0002-0445-4806</orcidid><orcidid>https://orcid.org/0000-0003-3482-6460</orcidid></search><sort><creationdate>20181216</creationdate><title>Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record</title><author>Sayer, Andrew M. ; Hsu, N. Christina ; Lee, Jaehwa ; Kim, Woogyung V. ; Dubovik, Oleg ; Dutcher, Steven T. ; Huang, Dong ; Litvinov, Pavel ; Lyapustin, Alexei ; Tackett, Jason L. ; Winker, David M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4461-764cf1e30265d2b8712ce0ccb70cf3ecde63a656013c0ca5b4ab4dc18a1b641b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Aeronautics</topic><topic>aerosol</topic><topic>Aerosol optical depth</topic><topic>Aerosol properties</topic><topic>Aerosol Robotic Network</topic><topic>Aerosols</topic><topic>Air quality</topic><topic>Algorithms</topic><topic>Atmospheric particulates</topic><topic>Climate studies</topic><topic>Cloud cover</topic><topic>Clouds</topic><topic>Coastal processes</topic><topic>Coastal waters</topic><topic>Data</topic><topic>Datasets</topic><topic>Dust storms</topic><topic>Evaluation</topic><topic>Geophysics</topic><topic>Global aerosols</topic><topic>Ground truth</topic><topic>Haze</topic><topic>Imaging techniques</topic><topic>Infrared imaging</topic><topic>Infrared instruments</topic><topic>Infrared radiometers</topic><topic>Instruments</topic><topic>Meteorological satellites</topic><topic>Offsets</topic><topic>Optical analysis</topic><topic>Pixels</topic><topic>Radiometers</topic><topic>Radiometry</topic><topic>Remote sensing</topic><topic>Retrieval</topic><topic>Satellite data</topic><topic>Satellite instruments</topic><topic>Satellite-borne instruments</topic><topic>Satellites</topic><topic>Sciences of the Universe</topic><topic>Sea spray</topic><topic>Smoke</topic><topic>Spaceborne remote sensing</topic><topic>validation</topic><topic>VIIRS</topic><topic>Volcanic ash</topic><topic>Volcanic dust</topic><topic>water</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sayer, Andrew M.</creatorcontrib><creatorcontrib>Hsu, N. Christina</creatorcontrib><creatorcontrib>Lee, Jaehwa</creatorcontrib><creatorcontrib>Kim, Woogyung V.</creatorcontrib><creatorcontrib>Dubovik, Oleg</creatorcontrib><creatorcontrib>Dutcher, Steven T.</creatorcontrib><creatorcontrib>Huang, Dong</creatorcontrib><creatorcontrib>Litvinov, Pavel</creatorcontrib><creatorcontrib>Lyapustin, Alexei</creatorcontrib><creatorcontrib>Tackett, Jason L.</creatorcontrib><creatorcontrib>Winker, David M.</creatorcontrib><collection>CrossRef</collection><collection>Meteorological &amp; 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Atmospheres</jtitle><date>2018-12-16</date><risdate>2018</risdate><volume>123</volume><issue>23</issue><spage>13,496</spage><epage>13,526</epage><pages>13,496-13,526</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><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. 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. Key Points Based on comparisons to AERONET, the AOD uncertainty is consistent with expectations Retrieved parameters related to aerosol particle size and type are also demonstrated to have skill SOAR shows similar regional and temporal patterns to other satellite data sets</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2018JD029465</doi><tpages>31</tpages><orcidid>https://orcid.org/0000-0001-9149-1789</orcidid><orcidid>https://orcid.org/0000-0003-1105-5739</orcidid><orcidid>https://orcid.org/0000-0002-3983-8826</orcidid><orcidid>https://orcid.org/0000-0002-3919-2244</orcidid><orcidid>https://orcid.org/0000-0002-5029-476X</orcidid><orcidid>https://orcid.org/0000-0002-0445-4806</orcidid><orcidid>https://orcid.org/0000-0003-3482-6460</orcidid><oa>free_for_read</oa></addata></record>
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2169-8996
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source Wiley Online Library Journals Frontfile Complete; Wiley Online Library Free Content; Alma/SFX Local Collection
subjects Aeronautics
aerosol
Aerosol optical depth
Aerosol properties
Aerosol Robotic Network
Aerosols
Air quality
Algorithms
Atmospheric particulates
Climate studies
Cloud cover
Clouds
Coastal processes
Coastal waters
Data
Datasets
Dust storms
Evaluation
Geophysics
Global aerosols
Ground truth
Haze
Imaging techniques
Infrared imaging
Infrared instruments
Infrared radiometers
Instruments
Meteorological satellites
Offsets
Optical analysis
Pixels
Radiometers
Radiometry
Remote sensing
Retrieval
Satellite data
Satellite instruments
Satellite-borne instruments
Satellites
Sciences of the Universe
Sea spray
Smoke
Spaceborne remote sensing
validation
VIIRS
Volcanic ash
Volcanic dust
water
title Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record
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