A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing. (15th February 2019)

• Record Type: Journal Article
• Authors: Zhang, Zhaoyang; Fan, Meng; Wu, Weiling; Wang, Zifeng; Tao, Minghui; Wei, Jing; Wang, Quan
• Is Part Of: Atmospheric environment. Volume 199(2019)
• Page Start: 127
• Page End: 135
• Publication Date: 2019-02-15
• Subjects: Aerosol Optical Depth -- AERONET -- Himawari-8 -- High temporal; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.11.023
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: High temporal aerosol optical depth (AOD) is useful for inversing modelling of emissions and for comprehensively understanding the climatology of aerosol. Geostationary satellite has been used to derive AOD over land and ocean. In this study, a novel simplified aerosol retrieval algorithm for Advanced Himawari-8 imager (SARAHI) is developed to retrieve hourly AOD over Beijing from a new-generation geostationary meteorological satellite—Himawari-8, which carries the Advanced Himawari Imager (AHI). The algorithm is based on a simplified radiative transfer model and surface reflectance derived from one-month AHI apparent data. Measurements from AErosol RObotic NETwork (AERONET) sites and JAXA Himawari Monitor Version 2.0 Aerosol Retrieval product (L2ARP2.0) are used to evaluate the performance of AOD retrievals from SARAHI algorithm. There are 73.5%, 78.2%, 74.7% of AOD retrievals falling in the Expected error lines at Beijing_CAMS, Beijing_RADI, and Xianghe sites, respectively. The correlation coefficient is larger than 0.92 with small root mean square error (RMSE) of 0.134–0.164. The retrievals can also reflect the temporal variations of AOD very well. The results indicate that the SARAHI AOD algorithm can derive the regional AOD accurately and is better than the L2ARP2.0 AOD. Highlights: A simplified hourly aerosol retrieval algorithm (SARAHI) is presented. The algorithm performs better accuracy than the AHI products. The hourly AOD from the novel algorithm can reflect the temporal variability of AOD very well. The results in this study can be used to derive the high temporal surface PM2.5 and PM10 mass concentration.