Difference between global and regional aerosol model classifications and associated implications for spaceborne aerosol optical depth retrieval. (1st May 2023)

• Record Type: Journal Article
• Authors: Zhou, Pei; Wang, Yang; Liu, Jane; Xu, Linglin; Chen, Xiang; Zhang, Likun
• Is Part Of: Atmospheric environment. Volume 300(2023)
• Publication Date: 2023-05-01
• Subjects: Aerosol type classification -- AERONET -- AOD -- Radiative transfer simulation; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2023.119674
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract : Abstract: Aerosol model are generally adopted to describe the physical and optical characteristics of aerosols in different regions, and this is important for climate and environmental studies as well as satellite aerosol retrieval. However, current mainstream aerosol retrieval algorithms still lack differentiated and dynamic descriptions of aerosol models, and the aerosol optical depth (AOD) retrieval accuracy is hence largely compromised. Therefore, in this study, we aimed to investigate the difference between global and regional aerosol model classifications and the impact of these differences on satellite AOD retrieval. We developed global and regional (for China) aerosol models through the K-means cluster analysis method, and used the 6SV radiative transfer model based on these global and regional aerosol models to simulate air pollution events. The cluster analysis revealed that both global and regional aerosols can be clustered into five main aerosol types with different optical and physical parameters. The aerosol types include strongly absorbing, moderately absorbing, weakly absorbing, dust and coarse-fine mixed aerosols. These two aerosol models exhibited distinct seasonal variations. Comparing the clustering results obtained with the two models at five Aerosol Robotic Network (AERONET) sites, we found that the proportion of strongly absorbing aerosols in the global results is low. According to the China clustering results, a higher proportion of strongly absorbing aerosols occurred in autumn, winter, and early spring, and dust aerosols attained an increasing proportion in spring. Comparing the aerosol parameters with observational data, the regional aerosol model performed better than the global aerosol model. Simulations of AOD during a pollution episode and in different seasons at the Beijing-CAMS site with the 6SV radiative transfer model further confirmed that the regional aerosol model achieved a better simulation accuracy than the global model. The findings suggest that regional aerosol models can provide a useful reference for satellite remote sensing algorithm, climate change and air pollution assessment, and that the dynamic description of the aerosol model is a key parameter in satellite AOD retrieval algorithms. Highlights: Five aerosol models (global and for China) were derived from a cluster study based on Aerosol Robotic Network (AERONET) ground-based remote sensing measurements. The biases of the aerosol types classified via regional clustering, namely, strongly absorbing, moderately absorbing, weakly absorbing, and coarse-fine mixed aerosols, were lower than those of the aerosol types classified via global clustering, while the biases of dust type aerosols were higher based on regional clustering. In simulating AOD of an air pollution events and different seasons, the China clustering model performed better than the global clustering model and MODIS DT algorithm aerosol model parameters, indicating that aerosol models developed for different regions could improve the accuracy of satellite aerosol products. The regional aerosol model and the dynamic description of the aerosol model offer key parameters for satellite AOD retrieval algorithms and can achieve a better retrieval accuracy for most parameters.