Cloud detection and classification based on MAX-DOAS observations

Multi-axis differential optical absorption spec- troscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is im- portant to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observa- tions, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring ef- fect). To identify clouds, these quantities can be either com- pared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be anal- ysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual mea- surements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its sim- plest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing di- rections are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky im- ages taken from the ground and backscatter profiles from a lidar.