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
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.