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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dc.contributor.authorGiles, David M.
dc.contributor.authorSinyuk, Alexander
dc.contributor.authorSorokin, Mikhail G.
dc.contributor.authorSchafer, Joel S.
dc.contributor.authorSmirnov, Alexander
dc.contributor.authorSlutsker, Ilya
dc.contributor.authorEck, Thomas F.
dc.contributor.authorHolben, Brent N.
dc.contributor.authorLewis, Jasper R.
dc.contributor.authorCampbell, James R.
dc.contributor.authorWelton, Ellsworth J.
dc.contributor.authorKorkin, Sergey
dc.contributor.authorLyapustin, Alexei I.
dc.date.accessioned2019-02-13T17:17:52Z
dc.date.available2019-02-13T17:17:52Z
dc.date.issued2019-01-11
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThe AERONET and MPLNET projects at NASA GSFC are supported by the Earth Observing System Project Science Office cal–val, Radiation Sciences Program at NASA headquarters, and various field campaigns. NCEP Reanalysis data are obtained routinely from the US National Weather Service Climate Prediction Center. We would like to thank Edward Celarier for several discussions and providing the OMI NO2 monthly climatology. Fred Espenak and Chris O'Byrne (NASA GSFC) provided solar and lunar eclipse predictions and the Eclipse Explorer software.en_US
dc.description.urihttps://amt.copernicus.org/articles/12/169/2019/en_US
dc.format.extent41 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2eykc-7hyo
dc.identifier.citationGiles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. 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, Atmos. Meas. Tech., 12, 169-209, https://doi.org/10.5194/amt-12-169-2019, 2019.en_US
dc.identifier.urihttps://doi.org/10.5194/amt-12-169-2019
dc.identifier.urihttp://hdl.handle.net/11603/12785
dc.language.isoen_USen_US
dc.publisherCopernicus Publications on behalf of the European Geosciences Unionen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC GESTAR II
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain*
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectaerosol robotic network (AERONET)en_US
dc.subjectaerosol optical depth (AOD)en_US
dc.subjectcloud-screening methodologyen_US
dc.titleAdvancements 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) measurementsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-9801-1610
dcterms.creatorhttps://orcid.org/0000-0002-4263-9262
dcterms.creatorhttps://orcid.org/0000-0003-4690-3232

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