Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

Abstract

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health.

AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)

“…Each aerosol subtype is associated with extinction-to-backscatter ratio (i. e., lidar ratio), which is considered a key parameter in retrieval algorithm and determined based on observation, modeling, and cluster analysis of a multiyear dataset obtained from AEWRONET measurements (Omar et al, 2009;Winker et al, 2009;Kim et al, 2018;Young et al, 2018). Several studies showed that the aerosol subtypes are reliable (Rupakheti et al, 2019;Ali et al, 2020;Qiu et al, 2021) and highly consistent with those classified by AERONET measurements (Mielonen et al, 2009;Burton et al, 2013;Falah et al, 2022). In addition, the V4.2 algorithm revised the lidar ratio for aerosol subtypes and introduced new aerosol subtypes (dusty marine), improving aerosol optical properties retrieval (Kim et al, 2018).…”

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

“…Satellite remote sensing has emerged as an important means for aerosol monitoring and research due to its ability to provide a rapid response and largearea, long-term series monitoring. In the past few decades, several scholars have exerted efforts to explore the properties of aerosols from satellite images and have accomplished numerous achievements [23][24][25][26]. The famous Dark Target (DT) algorithm is a widely used aerosol retrieval algorithm that uses the linear relationship between the visible band and mid-infrared band to estimate the surface reflectance of red and blue bands and retrieves AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) images according to the lookup table [27].…”

Integrated Remote Sensing Observations of Radiative Properties and Sources of the Aerosols in Southeast Asia: The Case of Thailand

“…The CALIOP retrieval algorithm reports seven tropospheric aerosol subtypes by considering the aerosol Lidar ratio at 532 nm and 1064 nm. The aerosol subtypes are clean marine, dust, polluted continental/smoke, clean continental, polluted dust, elevated smoke, and dusty marine [17]. In this study, the CALIOP aerosol vertical profiles were retrieved for the areas close to (±1 • ) of the five locations (see Table 1), which were the Chiang Mai Meteorological Station (in the North) for the years 2010-2016, Silpakorn University (Central) for the years 2010-2019, Nong Khai (Northeast) for the years 2015-2019, Ubon Ratchathani (Northeast) for the years 2010-2019, and the Songkhla Meteorological Station (South) for the years 2010-2019.…”