Research of classification of cloud and aerosol using multi-axis differential optical absorption spectroscopy

Abstract

The classification of cloud and aerosol by means of multi-axis differential optical absorption spectroscopy (MAX-DOAS) is studied in this paper. Firstly, the characters of variation of color index (CI), radiance, and O4 air mass factor (AMF) are analyzed in the following kinds of weather cases, i.e. clear and low aerosol load, clear and high aerosol load, broken cloud, continuous and thin cloud as well as continuous and thick clouds. We found that the CI consecutively decreases with the growing up of optical depth of cloud and aerosol. And the speedy temporal variation of CI is always going along with the occurrence of broken cloud. For the case of continuous cloud, the CIs of observations for all the elevation angles are similar to each other. At the same time, the thick cloud case normally causes radiance dropping and O4 AMF growing up strongly. Based on these characters, the scheme of cloud classification for MAX-DOAS is built. Using this scheme, the classification results for the MAX-DOAS observations in the period from 1 June 2012 to 30 October 2012 are analyzed statistically. The occurrence probabilities of the broken cloud and thin continuous cloud are the two largest weather kinds. The percentage of the broken cloud in all the observations is 66%, and that of the thin continuous cloud case is 14.3%. For these two kinds of weathers, the mean NO2 tropospheric vertical column densities (VCD) are respectively 35% and 66% larger than the value for the clear and low aerosol. Meanwhile, the standard deviation, which represents the stability of the measured NO2 VCD is two times larger than that of the clear and low aerosol cases. In the weather of thick continuous cloud, suddenly appearing of peak and valley are often observed. In conclusion, the real time classification of cloud and aerosol is very important and valuable in analyzing of MAX-DOAS data and the guarantee of data quality.

Technical note: Evaluation of profile retrievals of aerosols and trace gases for MAX-DOAS measurements under different aerosol scenarios based on radiative transfer simulations

“…Recent research on MAX-DOAS has focused on the following aspects: (1) profile inversion algorithms (Hönninger and Platt, 2002;Wagner et al, 2004;Frieß et al, 2006Frieß et al, , 2011Hay, 2010;Vlemmix et al, 2011;Yilmaz, 2012;Hartl and Wenig, 2013;Holla, 2013;Wang et al, 2013a, b;Zielcke, 2015;Bösch et al, 2018;Friedrich et al, 2019;Frieß et al, 2019); (2) longterm observation of trace gases and aerosols (e.g., Irie et al, 2008a;Roscoe et al, 2010;Li et al, 2013;Ma et al, 2013;Pinardi et al, 2013;Hendrick et al, 2014;Kanaya et al, 2014;Chan et al, 2015;Tian et al, 2018Tian et al, , 2019Wang et al, 2017a); (3) cloud identification and data correction (Gielen et al, 2014;Wagner et al, 2014Wagner et al, , 2016; and (4) satellite and model data validation (e.g., Halla et al, 2011;Ma et al, 2013;Pinardi et al, 2013;Chan et al, 2015;De Smedt et al, 2015;Jin et al, 2016;Drosoglou et al, 2017;Wang et al, 2017b;Boersma et al, 2018;Liu et al, 2018). In this study, we focus on the first aspect.…”