Evaluation of aerosol microphysical, optical and radiative properties measured with a multiwavelength photometer

ATMOSPHERIC MEASUREMENT TECHNIQUES(2022)

引用 1|浏览16
暂无评分
摘要
An evaluation of aerosol microphysical, optical and radiative properties measured with a multiwavelength photometer named CW193 was performed in this study. The instrument has a highly integrated design, smart control performance and is composed of three parts (the optical head, robotic drive platform and stents system). Based on synchronous measurements, the CW193 products were validated using reference data from the AERONET CE318 photometer. The results show that the raw digital counts from CW193 agree well with the counts from AERONET (R > 0.989), with daily average triplets of around 1.2 % to 3.0 % for the ultraviolet band and less than 2.0 % for the visible and infrared bands. Good aerosol optical depth agreement (R > 0.997, 100 % within expected error) and root mean square error (RMSE) values ranging from 0.006 (for the 870 nm band) to 0.016 (for the 440 nm band) were obtained, with the relative mean bias (RMB) ranging from 0.922 to 1.112 and the aerosol optical depth bias within +/- 0.04. The maximum deviation of the peak value for fine-mode particles varied from about 8.9 % to 77.6 %, whereas the variation for coarse-mode particles was about 13.1 % to 29.1 %. The deviation vari ations of the single scattering albedo were approximately 0.1 %-1.8 %, 0.6 %-1.9 %, 0.1 %-2.6 % and 0.8 %-3.5 % for the 440, 675, 870 and 1020 nm bands, respectively. For the aerosol direct radiative forcing, deviations of approximately 4.8 %-12.3 % were obtained at the earth's surface and 5.4 %-15.9 % for the top of the atmosphere. In addition, the water vapor retrievals showed satisfactory accuracy, characterized by a high R value (similar to 0.997), a small RMSE (similar to 0.020) and a good expected error distribution (100 % within expected error). The water vapor RMB was about 0.979, and the biases mostly varied within +/- 0.04, whereas the mean values were concentrated within +/- 0.02.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要