Aerosol and Cloud Classifications Derived from MAX-DOAS Measurements in Urban North China and their Comparisons to Multiple Remote Sensing Datasets

Multi-Axis Differential Optical Absorption Spectroscopy, MAX-DOAS for short, is a thriving ground-based passive remote sensing technique, which retrieves the vertical characteristics of aerosol and trace gases in the lower atmosphere using scattered sunlight measured from different axis angles. Clouds have obvious influence on atmospheric radiative transfer process and thus affect the inversion of vertical distribution, making it essential to study and classify the cloud properties. In this study, a cloud identification and classification algorithm script was developed based on several key quantities derived from MAX-DOAS observations, like radiance, color index and the absorption of oxygen dimer $\text{O}_{\mathbf {4}}$ et al. The algorithm was applied to two-month’s MAX-DOAS observations in southern urban Beijing $( 39.81 ^{\circ}\mathrm {N}, 116.47 ^{\circ}\mathrm {E})$, the megacity in North China, in summer 2017. A cloud classification dataset was created with relatively high time resolution. Aerosol profiles, near surface aerosol extinction and AOD (aerosol optical density) were derived as well by applying PriAM methods of MPIC. The results were compared systematically to several remote sensing techniques, like MODIS, sun photometer and Millimeter wave cloud radar, which have rarely been done before. General consistency and good agreement were achieved under respective aerosol and cloud scenarios, assuring the reliability of the cloud identification and classification algorithm script and the dependable capability of MAX-DOAS to provide aerosol and cloud information. This further indicates that more thorough studies should be carried out to diminish the influence of aerosol and cloud and improve the retrieval accuracy of vertical column densities and profiles from MAX-DOAS in the future.