Quantifying Cloud Development and Lifetime of Individual Clouds from Geostationary Observations

Abstract:

Clouds and their interaction with short- and longwave radiation represent one of the major uncertainties in our understanding of global climate change. The presence of clouds, particularly of bright low-level water clouds, doubles the Earth’s albedo and clouds are responsible for half of the solar radiation reflected into space. Here, we present an innovative method to track warm low-level clouds formed in the trade wind zone centered around Canary Islands. We use time-resolved geostationary measurements with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation. The Particle Image Velocimetry method (PIV; Raffel et al., 2007; Adrian and Westerweel, 2011) in combination with the CLAAS-2 data set (CM SAF CLoud property dAtAset using SEVIRI - Edition 2; Benas et al., 2017) is used to quantify cloud development and to characterize temporal changes of cloud properties. The PIV method is widely used in experimental fluid mechanics and relies on basic pattern matching. The principle of pattern matching is usually referred to as cross-correlation. This cross-correlation gives information about the displacement of identified features and enables - in our application - the retrieval of cloud trajectories.

References:

Raffel, M., Willert, C. E., Wereley, S., Kompenhans, J.: Particle Image Velocimetry: A Practical Guide. Cambridge University Press, Berlin (2007). DOI 10.1007/978-3-540-72308-0

Adrian, R. J., Westerweel, J.: Particle Image Velocimetry. Cambridge University Press (2010)

Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2. Earth System Science Data 9(2), 415–434 (2017). DOI 10.5194/essd-9-415-2017