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

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.

AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season

“…In Version 2, when the raw count signal decreases below 10 for the 440‐nm channel, then all of the data for all channels in that spectral scan are eliminated to avoid significant bias in 440‐nm AOD from diffuse radiation in the instrument FOV (Sinyuk et al, ). In Version 3, a low signal check is applied to all channels independently and channels with sufficiently high signal (to avoid significant biases from diffuse in FOV) are retained when AE is sufficiently high (>1.2 for the 675‐ to 1,020‐nm range or >1.3 for 870–1,020 nm; Giles et al, ). Therefore, AOD values in the longer wavelengths are retained since for fine‐mode smoke the AOD deceases significantly as wavelength increases resulting in adequate signal for accurate AOD measurements in these wavelengths.…”

“…Finally, there is a cloud screening check in Version 3 for cirrus that does not utilize temporal variance for cloud detection but rather relies on the sky radiances within 6° scattering angle of the Sun. If the angular slope in scattering angle of the measured sky radiances in the solar aureole is sufficiently steep then there likely are cirrus cloud crystals present (due to very strong forward scattering) and there is a threshold on this angular dependence for determining cirrus presence (Giles et al, ). This check is particularly important for the current study as Southeast Asian data have been shown to suffer from cirrus contamination (Chew et al, ).…”

VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

“…To accomplish this task, we follow similar procedures used in Hsu et al (2013) to infer surface reflectances by performing an atmospheric correction to the VIIRS TOA reflectances at blue (0.488 μm) and red (0.672 μm) bands using satellite measurements collocated with AERONET AOD data (to remove the aerosol contribution to the signal). The AERONET data used in this analysis are based upon the latest Version 3 Level 2 AERONET direct sun AOD (Giles et al, 2019), which included updates to calibration and cloud masking and, among other things, reduce issues where cirrus clouds are underscreened, and highly variable aerosol features over-screened, compared to previous AERONET data versions. As a result, 4 years (2012-2015) of cloud-screened VIIRS TOA reflectances and AERONET data over North America were compiled to develop the spectral relationship for each season.…”

Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land

“…This analysis uses the latest AERONET version 3 direct-Sun L2 (cloud-screened, post-deployment calibrated, and quality-assured; Smirnov et al, 2000) data products. Version 3 includes improvements to sensor characterization and cloud-aerosol discrimination over version 2 (Giles et al, 2019), particularly in the detection of stable optically thin cirrus cloud layers and rapidly evolving fine-mode aerosol plumes. The geolocation accuracy of some sites has also been improved.…”