214 Quantifying the temporal variability of convective cloud using SEVIRI data

Wednesday, 9 July 2014
Sarah A. Taylor, University of Oxford, Oxford, United Kingdom; and P. Stier and B. White

Convective clouds are one of the fundamental building blocks of tropical weather and climate. They vary on spatial scales ranging from a few km (individual clouds) to 1000s of km (mesoscale convective systems), and on timescales from minutes, through diurnal, to seasonal scales.

High temporal resolution observations can both improve theoretical understanding of convective clouds, and provide a useful test of a model's ability to capture their various scales of variability. The diurnal cycle in particular is associated with large variations in solar forcing, and the ability of models to represent this cycle tests their ability to represent radiative transfer and surface heat exchanges, as well as boundary layer, convective and cloud processes.

In contrast to the static picture provided by polar-orbiting satellites, the geostationary SEVIRI instrument provides continuous, high resolution (15 min, 3-11 km), observations of cloud properties over a large area. It has the additional advantage of providing coverage of tropical Africa, where the amplitude of the diurnal cycle of convection is strongest, and ground based observations are sparse.

In this study, the CLAAS (Cloud Property Dataset Using SEVIRI) product, developed by CM-SAF is used to quantify the temporal variability of convective cloud in the region of the Congo basin. The diurnal cycle of cloud is quantified by season and by cloud regime (determined through application of a k-means clustering algorithm) for the period of 2004 to 2011. The amplitude of the diurnal mode of variation and the local solar time of minimum cloud top temperature are calculated by fitting a sine curve to a data series of seasonal mean cloud top temperature.

In contrast to the Eulerian view described above, the continuous nature of SEVIRI's observations also makes it possible to take a Lagrangian approach, by investigating the evolution of individual convective clouds and systems. The Cb-TRAM (Cumulonimbus Tracking and Monitoring) algorithm is used to track convective cloud plumes, and hence to investigate the evolution of variables such as cloud top height over the lifecycle of individual convective clouds.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner