Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison

The aerosol fine-mode fraction (FMF) is an important optical parameter of aerosols, and the FMF is difficult to accurately retrieve by traditional satellite remote sensing methods. In this study, FMF retrieval was carried out based on the multiangle polarization data of Polarization and Anisotropy o...

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Bibliographic Details
Published in:Atmospheric Measurement Techniques
Main Authors: Y. Zhang, Z. Li, Z. Liu, Y. Wang, L. Qie, Y. Xie, W. Hou, L. Leng
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2021
Subjects:
Environmental engineering
TA170-171
Earthwork. Foundations
TA715-787
Aerosol Robotic Network
Online Access:https://doi.org/10.5194/amt-14-1655-2021
https://doaj.org/article/17c9774743894d728de6ca8fc4171078
Description
Summary:The aerosol fine-mode fraction (FMF) is an important optical parameter of aerosols, and the FMF is difficult to accurately retrieve by traditional satellite remote sensing methods. In this study, FMF retrieval was carried out based on the multiangle polarization data of Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from Lidar (PARASOL), which overcame the shortcomings of the FMF retrieval algorithm in our previous research. In this research, FMF retrieval was carried out in China and compared with the AErosol RObotic NETwork (AERONET) ground-based observation results, Moderate Resolution Imaging Spectroradiometer (MODIS) FMF products, and Generalized Retrieval of Aerosol and Surface Properties (GRASP) FMF results. In addition, the FMF retrieval algorithm was applied, a new FMF dataset was produced, and the annual and quarterly average FMF results from 2006 to 2013 were obtained for all of China. The research results show that the FMF retrieval results of this study are comparable with the AERONET ground-based observation results in China and the correlation coefficient ( r ), mean absolute error (MAE), root mean square error (RMSE), and the proportion of results that fall within the expected error (Within EE) are 0.770, 0.143, 0.170, and 65.01 %, respectively. Compared with the MODIS FMF products, the FMF results of this study are closer to the AERONET ground-based observations. Compared with the FMF results of GRASP, the FMF results of this study are closer to the spatial variation in the ratio of PM 2.5 to PM 10 near the ground.

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