Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements
The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality contro...
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article Verlagsveröffentlichung Giles, David M. Sinyuk, Alexander Sorokin, Mikhail G. Schafer, Joel S. Smirnov, Alexander Slutsker, Ilya Eck, Thomas F. Holben, Brent N. Lewis, Jasper R. Campbell, James R. Welton, Ellsworth J. Korkin, Sergey V. Lyapustin, Alexei I. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
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article Verlagsveröffentlichung |
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The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases. |
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Article in Journal/Newspaper |
author |
Giles, David M. Sinyuk, Alexander Sorokin, Mikhail G. Schafer, Joel S. Smirnov, Alexander Slutsker, Ilya Eck, Thomas F. Holben, Brent N. Lewis, Jasper R. Campbell, James R. Welton, Ellsworth J. Korkin, Sergey V. Lyapustin, Alexei I. |
author_facet |
Giles, David M. Sinyuk, Alexander Sorokin, Mikhail G. Schafer, Joel S. Smirnov, Alexander Slutsker, Ilya Eck, Thomas F. Holben, Brent N. Lewis, Jasper R. Campbell, James R. Welton, Ellsworth J. Korkin, Sergey V. Lyapustin, Alexei I. |
author_sort |
Giles, David M. |
title |
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
title_short |
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
title_full |
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
title_fullStr |
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
title_full_unstemmed |
Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements |
title_sort |
advancements in the aerosol robotic network (aeronet) version 3 database – automated near-real-time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth (aod) measurements |
publisher |
Copernicus Publications |
publishDate |
2019 |
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https://doi.org/10.5194/amt-12-169-2019 https://noa.gwlb.de/receive/cop_mods_00003581 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00003539/amt-12-169-2019.pdf https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.pdf |
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Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
Atmospheric Measurement Techniques -- http://www.bibliothek.uni-regensburg.de/ezeit/?2505596 -- http://www.atmospheric-measurement-techniques.net/ -- 1867-8548 https://doi.org/10.5194/amt-12-169-2019 https://noa.gwlb.de/receive/cop_mods_00003581 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00003539/amt-12-169-2019.pdf https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.pdf |
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https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
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CC-BY |
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https://doi.org/10.5194/amt-12-169-2019 |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00003581 2023-05-15T13:06:08+02:00 Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements Giles, David M. Sinyuk, Alexander Sorokin, Mikhail G. Schafer, Joel S. Smirnov, Alexander Slutsker, Ilya Eck, Thomas F. Holben, Brent N. Lewis, Jasper R. Campbell, James R. Welton, Ellsworth J. Korkin, Sergey V. Lyapustin, Alexei I. 2019-01 electronic https://doi.org/10.5194/amt-12-169-2019 https://noa.gwlb.de/receive/cop_mods_00003581 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00003539/amt-12-169-2019.pdf https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.pdf eng eng Copernicus Publications Atmospheric Measurement Techniques -- http://www.bibliothek.uni-regensburg.de/ezeit/?2505596 -- http://www.atmospheric-measurement-techniques.net/ -- 1867-8548 https://doi.org/10.5194/amt-12-169-2019 https://noa.gwlb.de/receive/cop_mods_00003581 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00003539/amt-12-169-2019.pdf https://amt.copernicus.org/articles/12/169/2019/amt-12-169-2019.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2019 ftnonlinearchiv https://doi.org/10.5194/amt-12-169-2019 2022-02-08T23:00:30Z The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases. Article in Journal/Newspaper Aerosol Robotic Network Niedersächsisches Online-Archiv NOA Atmospheric Measurement Techniques 12 1 169 209 |