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
<p>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 quali...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/12/169/2019/amt-12-169-2019.pdf |
| _version_ | 1811338360213471232 |
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| author | D. M. Giles D. M. Giles A. Sinyuk A. Sinyuk M. G. Sorokin M. G. Sorokin J. S. Schafer J. S. Schafer A. Smirnov A. Smirnov I. Slutsker I. Slutsker T. F. Eck T. F. Eck B. N. Holben J. R. Lewis J. R. Lewis J. R. Campbell E. J. Welton S. V. Korkin S. V. Korkin A. I. Lyapustin |
| author_facet | D. M. Giles D. M. Giles A. Sinyuk A. Sinyuk M. G. Sorokin M. G. Sorokin J. S. Schafer J. S. Schafer A. Smirnov A. Smirnov I. Slutsker I. Slutsker T. F. Eck T. F. Eck B. N. Holben J. R. Lewis J. R. Lewis J. R. Campbell E. J. Welton S. V. Korkin S. V. Korkin A. I. Lyapustin |
| author_sort | D. M. Giles |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">+0.02</span> 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 <span class="inline-formula">+</span>0.002 with a <span class="inline-formula">±</span>0.02 SD (standard
deviation), yet monthly averages calculated using time-matched observations
in both databases were analyzed to compute an average difference of <span class="inline-formula">−0.002</span>
with a <span class="inline-formula">±0.004</span> 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.</p> |
| first_indexed | 2024-04-13T18:09:42Z |
| format | Article |
| id | doaj.art-d00b00bbbcdf44428e68cacfc772d221 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-13T18:09:42Z |
| publishDate | 2019-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-d00b00bbbcdf44428e68cacfc772d2212022-12-22T02:35:57ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482019-01-011216920910.5194/amt-12-169-2019Advancements 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) measurementsD. M. Giles0D. M. Giles1A. Sinyuk2A. Sinyuk3M. G. Sorokin4M. G. Sorokin5J. S. Schafer6J. S. Schafer7A. Smirnov8A. Smirnov9I. Slutsker10I. Slutsker11T. F. Eck12T. F. Eck13B. N. Holben14J. R. Lewis15J. R. Lewis16J. R. Campbell17E. J. Welton18S. V. Korkin19S. V. Korkin20A. I. Lyapustin21Science Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAScience Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAScience Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAScience Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAScience Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAScience Systems and Applications Inc. (SSAI), Lanham, MD 20706, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAUniversities Space Research Association (USRA), Columbia, MD 21046, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAJoint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USAMarine Meteorology Division, Naval Research Laboratory (NRL), Monterey, CA 93943, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USAUniversities Space Research Association (USRA), Columbia, MD 21046, USANASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA<p>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 <span class="inline-formula">+0.02</span> 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 <span class="inline-formula">+</span>0.002 with a <span class="inline-formula">±</span>0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of <span class="inline-formula">−0.002</span> with a <span class="inline-formula">±0.004</span> 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.</p>https://www.atmos-meas-tech.net/12/169/2019/amt-12-169-2019.pdf |
| spellingShingle | D. M. Giles D. M. Giles A. Sinyuk A. Sinyuk M. G. Sorokin M. G. Sorokin J. S. Schafer J. S. Schafer A. Smirnov A. Smirnov I. Slutsker I. Slutsker T. F. Eck T. F. Eck B. N. Holben J. R. Lewis J. R. Lewis J. R. Campbell E. J. Welton S. V. Korkin S. V. Korkin A. I. Lyapustin 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 Atmospheric Measurement Techniques |
| 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_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_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_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 |
| url | https://www.atmos-meas-tech.net/12/169/2019/amt-12-169-2019.pdf |
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