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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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 |
doi_str_mv | 10.1029/2018JD029465 |
format | Article |
fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_insu_03686250v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2160652991</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4461-764cf1e30265d2b8712ce0ccb70cf3ecde63a656013c0ca5b4ab4dc18a1b641b3</originalsourceid><addsrcrecordid>eNp9kU1OwzAQhSMEEhV0xwEssUMU7PinyTJqoT-qVCmFlp3lOI7qysTFcQvdcQTOyElwVVSxYjYzi-89zcyLoisE7xCM0_sYomTcDxNh9CRqxYilnSRN2elx7r6cR-2mWcFQCcSEklbk58LoUnhta2ArMJtmOZiPRvkMTLfKfX9-LYRXDmTK2cYakCvvtNoK0wBRl6Bna68-PFhov9Q18EsFBsYWwoBZkBmjvTpK-8KLoJfWlZfRWRUsVPu3X0TPjw9PvWFnMh2MetmkIwlhqNNlRFZIYRgzWsZF0kWxVFDKogtlhZUsFcOCUQYRllAKWhBRkFKiRKCCEVTgi-jm4LsUhq-dfhVux63QfJhNuK6bDYeYJSymcIsCfH2A186-bVTj-cpuXB324-F_kNE4TffU7YGS4arGqeroiyDf58D_5hBwfMDftVG7f1k-HuR9ShFC-AcDF4jw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2160652991</pqid></control><display><type>article</type><title>Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record</title><source>Wiley Online Library Journals Frontfile Complete</source><source>Wiley Online Library Free Content</source><source>Alma/SFX Local Collection</source><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.</creator><creatorcontrib>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.</creatorcontrib><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><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1029/2018JD029465</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>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</subject><ispartof>Journal of geophysical research. Atmospheres, 2018-12, Vol.123 (23), p.13,496-13,526</ispartof><rights>2018. American Geophysical Union. All Rights Reserved.</rights><rights>Copyright</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4461-764cf1e30265d2b8712ce0ccb70cf3ecde63a656013c0ca5b4ab4dc18a1b641b3</citedby><cites>FETCH-LOGICAL-c4461-764cf1e30265d2b8712ce0ccb70cf3ecde63a656013c0ca5b4ab4dc18a1b641b3</cites><orcidid>0000-0001-9149-1789 ; 0000-0003-1105-5739 ; 0000-0002-3983-8826 ; 0000-0002-3919-2244 ; 0000-0002-5029-476X ; 0000-0002-0445-4806 ; 0000-0003-3482-6460</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2018JD029465$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2018JD029465$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://insu.hal.science/insu-03686250$$DView record in HAL$$Hfree_for_read</backlink></links><search><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><title>Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record</title><title>Journal of geophysical research. 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 & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sayer, Andrew M.</au><au>Hsu, N. Christina</au><au>Lee, Jaehwa</au><au>Kim, Woogyung V.</au><au>Dubovik, Oleg</au><au>Dutcher, Steven T.</au><au>Huang, Dong</au><au>Litvinov, Pavel</au><au>Lyapustin, Alexei</au><au>Tackett, Jason L.</au><au>Winker, David M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record</atitle><jtitle>Journal of geophysical research. 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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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 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T20%3A08%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Validation%20of%20SOAR%20VIIRS%20Over%E2%80%90Water%20Aerosol%20Retrievals%20and%20Context%20Within%20the%20Global%20Satellite%20Aerosol%20Data%20Record&rft.jtitle=Journal%20of%20geophysical%20research.%20Atmospheres&rft.au=Sayer,%20Andrew%C2%A0M.&rft.date=2018-12-16&rft.volume=123&rft.issue=23&rft.spage=13,496&rft.epage=13,526&rft.pages=13,496-13,526&rft.issn=2169-897X&rft.eissn=2169-8996&rft_id=info:doi/10.1029/2018JD029465&rft_dat=%3Cproquest_hal_p%3E2160652991%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2160652991&rft_id=info:pmid/&rfr_iscdi=true |