Quantifying cloud development from geostationary observations

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 they are responsible for half of the solar radiation reflected into space.
Contrary to spaceborne, polar-orbiting observations which are of great detail at fixed time we focus on spaceborne time-resolved measurements of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation. We present an innovative method to track warm low-level clouds. The method widely used in experimental fluid mechanics and known as particle image velocimetry (PIV) [1, 2] relies on basic pattern matching. The principle of pattern matching is usually referred to as cross-correlation. It tells us something about displacements and enables the reconstruction of cloud trajectories. Thereby, we quantify cloud development and in combination with the CLAAS-2 dataset [3] we characterize temporal changes of cloud properties.

References
[1] Keane, R. D., Adrian, R. J.: Theory of cross-correlation analysis of PIV images. Applied Scientific Research 49, 191–215 (1992). DOI: 10.1007/BF00384623

[2] Tropea, C., Alexander, L., Yarin, L., (Eds.), F.: Handbook of experimental fluid mechanics. Springer (2007)

[3] 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