Cloud detection and classification based on MAX-DOAS observations

• Verfasst von: Wagner, Thomas [VerfasserIn]
• Verfasst von: Apituley, A. [VerfasserIn]
• Verfasst von: Beirle, S. [VerfasserIn]
• Verfasst von: Dörner, S. [VerfasserIn]
• Verfasst von: Frieß, Udo [VerfasserIn]
• Verfasst von: Remmers, J. [VerfasserIn]
• Verfasst von: Shaiganfar, R. [VerfasserIn]
• Titel: Cloud detection and classification based on MAX-DOAS observations
• Verf.angabe: T. Wagner, A. Apituley, S. Beirle, S. Dörner, U. Friess, J. Remmers, and R. Shaiganfar
• E-Jahr: 2014
• Jahr: 19 May 2014
• Umfang: 32 S.
• Fussnoten: Gesehen am 30.09.2020
• Titel Quelle: Enthalten in: Atmospheric measurement techniques
• Ort Quelle: Katlenburg-Lindau : Copernicus, 2008
• Jahr Quelle: 2014
• Band/Heft Quelle: 7(2014), 5, Seite 1289-1320
• ISSN Quelle: 1867-8548
• DOI: doi:10.5194/amt-7-1289-2014
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1733974156

Abstract

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important 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 observations, 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 effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. 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 simplest 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 directions 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 images taken from the ground and backscatter profiles from a lidar.