Abstract
Abstract. 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.
Highlights
Space-based, airborne, and surface-based Earth observing platforms can remotely retrieve or measure aerosol abundance
Satellite retrieval issues include determining the aerosol optical depth (AOD) for very high aerosol loading episodes, cloud adjacency effects, land– water mask depiction, surface reflectance, highly varying topography, and aerosol type assumptions (Levy et al, 2010, 2013; Omar et al, 2013). With each of these measurement platforms, uncertainties exist with AOD; these concerns are minimized with AOD measurements from surfacebased Sun photometry such as from the federated Aerosol Robotic Network (AERONET)
When the algorithm removes a TS reading or the TS measurement is missing, an assessment is made on the instrument temperature response based on ±15 ◦C of the NCEP/NCAR reanalysis temperature for the date and location to determine whether the temperature characterization coefficient for a specific wavelength would result in a change of AOD by more than 0.02
Summary
Space-based, airborne, and surface-based Earth observing platforms can remotely retrieve or measure aerosol abundance. Satellite retrieval issues include determining the AOD for very high aerosol loading episodes, cloud adjacency effects, land– water mask depiction, surface reflectance, highly varying topography, and aerosol type assumptions (Levy et al, 2010, 2013; Omar et al, 2013) With each of these measurement platforms, uncertainties exist with AOD; these concerns are minimized with AOD measurements from surfacebased Sun photometry such as from the federated Aerosol Robotic Network (AERONET). The number of AERONET sites has increased to more than 600 sites in the network as of 2018 and the labor-intensive effort of quality controlling hundreds of thousands of measurements manually had resulted in a significant delay of quality-assured data (Level 2.0) in the AERONET Version 2 database With these issues at hand, the cloud-screening quality control procedure as well as all other aspects of the AERONET processing algorithm including instrument temperature characterization, ancillary data set updates, and further quality control automation were reassessed. The AERONET Version 2 and Version 3 database results are analyzed for the entire data set as well as for selected sites
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