Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record

Abstract

This study validates aerosol properties retrieved using a Satellite Ocean Aerosol Retrieval (SOAR) algorithm applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements, from Version 1 of the VIIRS Deep Blue data set. SOAR is the over-water complement to the over-land Deep Blue algorithm and has two processing paths: globally, 95% of pixels are processed with the full retrieval algorithm, while the 5% of pixels in shallow or turbid (mostly coastal) waters are processed with a backup algorithm. Aerosol Robotic Network (AERONET) data are used to validate and compare the midvisible (550 nm) aerosol optical depth (AOD), Ångström exponent (AE), and fine mode fraction of AOD at 550 nm (FMF). AOD uncertainty is shown to be approximately ±(0.03 + 10%) for the full and ±(0.03 + 15%) for the backup algorithms, with a small positive median bias around 0.02. When AOD is below about 0.2, the AE and FMF have small negative offsets from AERONET around −0.15 and −0.04, respectively. For higher AOD, AE is less offset and the magnitudes of differences versus AERONET are about ±0.2 and ±0.14, respectively. Aerosol-type classifications provided by SOAR are found to be reasonable, matching optical-based classifications from AERONET over 80% of the time. Spatial and temporal patterns of AOD and AE are also compared with those of other contemporary over-water satellite aerosol data sets; dependent on region, the satellite data sets show varying levels of consistency, with SOAR broadly in-family, and the largest discrepancies in regions with persistent heavy cloud cover.Plain Language Summary Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever and newer satellites often have improved capabilities compared to older ones. This paper evaluates an extension of an algorithm, originally designed to monitor aerosols from an older satellite instrument, to a new satellite instrument called Visible Infrared Imaging Radiometer Suite. The evaluation is performed by comparing to ground truth data which are part of the National Aeronautics and Space Administration's global Aerosol Robotic Network, as well as to other satellite-based aerosol data sets from different spaceborne instruments.

Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data

“…The main metrics used here to evaluate the AAC retrievals, which are as often used in AOD validation exercises, including DB (e.g. Sayer et al, 2018bSayer et al, , 2019, are as follows.…”

Constraining global aerosol emissions using POLDER/PARASOL satellite remote sensing observations

“…Atmospheric aerosol emission inventories are often used to drive chemical transport model (CTM) simulations of aerosol distributions on regional and global scales (Boucher, 2015;Brasseur and Jacob, 2017;Granier et al, 2011). Satelliteretrieved columnar aerosol optical depth (AOD) is directly related to light extinction due to the presence of aerosols; hence, satellite-retrieved columnar AOD is widely used to evaluate the spatial and temporal variability of aerosols simulated from CTMs (e.g., Chin et al, 2002;Ginoux et al, 2006;Kinne et al, 2006Kinne et al, , 2003Liu et al, 2012;Ocko and Ginoux, 2017;Pozzer et al, 2015;Schulz et al, 2006;Tegen et al, 2019). A general agreement has been shown for columnar AOD between model simulations and satellite observations in the Aerosol Comparisons between Observations and Models (AeroCom) "Experiment A" multimodel assessments .…”

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

“…Using AOD observations from a set of 12 AERONET sites as reference, they concluded that no current technique uniformly performs best but that UNC values reported by MISR perform well at most sites. These recent studies by Sayer et al (2020) and Witek et al (2019) are the main motivations for comparing the reported UNC between V22 and V23 of the MISR aerosol product. Figure 8 compares the distributions of average MISR V22 and V23 UNC using retrievals from 2012.…”

“…They found that MISR-reported UNC realistically represent retrieval errors and the statistical behavior is similar to that of a Gaussian distribution. Sayer et al (2020) comprehensively reviewed current prognostic and diagnostic methods used for quantifying uncertainty in satellite AOD retrievals, outlined a general framework for evaluating their accuracy, and used the proposed approach to compare products from several satellite instruments, including MISR. Using AOD observations from a set of 12 AERONET sites as reference, they concluded that no current technique uniformly performs best but that UNC values reported by MISR perform well at most sites.…”