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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Sep 2018

Research article | 10 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

David M. Giles1,2, Alexander Sinyuk1,2, Mikhail S. Sorokin1,2, Joel S. Schafer1,2, Alexander Smirnov1,2, Ilya Slutsker1,2, Thomas F. Eck2,3, Brent N. Holben2, Jasper Lewis2,4, James Campbell5, Ellsworth J. Welton2, Sergey Korkin2,3, and Alexei Lyapustin2 David M. Giles et al.
  • 1Science Systems and Applications Inc. (SSAI), Lanham, MD 20706, USA
  • 2NASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
  • 3Universities Space Research Association (USRA), Columbia, MD 21046, USA
  • 4Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
  • 5Marine Meteorology Division, Naval Research Laboratory (NRL), Monterey, CA 93943, USA

Abstract. The Aerosol Robotic Network (AERONET) provides 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 semi-automatically 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 manually quality control 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 where 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 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 standard deviation. The high statistical agreement in multi-year 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.

David M. Giles et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
David M. Giles et al.
David M. Giles et al.
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Publications Copernicus
Short summary
Clouds or instrumental anomalies may perturb ground-based solar measurements used to calculate aerosol optical depth (AOD). This study presents a new algorithm of automated near real-time (NRT) quality controls with improved cloud screening for AERONET AOD measurements. Results from the new and old algorithms have excellent agreement for the highest quality AOD level, while the new algorithm provides higher quality NRT AOD for applications such as data assimilation and satellite evaluation.
Clouds or instrumental anomalies may perturb ground-based solar measurements used to calculate...