Prediction of the Onset of Heavy Rain Using SEVIRI Cloud Observations

Abstract

AbstractThunderstorms and strong precipitation events can be highly variable in space and time and therefore are challenging to forecast. Geostationary satellites are particularly well suited for studying their occurrence and development. This paper describes a methodology for tracking temporal trends in the development of these systems using a combination of a ground-based radar rainfall product and cloud fields derived from the Meteosat Second Generation?s (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Cloud microphysical and radiative properties and the cloud perimeter-to-area ratio are used to characterize the temporal evolution of 35 cases of isolated convective development. For synchronizing temporal trends between cases, two reference times are used: the time when precipitating clouds reach a rain intensity threshold and the time of the maximum of rain intensity during the cloud life cycle. A period of decreasing cloud perimeter-to-area ratio before heavy rainfall is observed for both synchronization techniques, suggesting this parameter could be a predictor of heavy rain occurrence. However, the choice of synchronization time does impact significantly the observed trend of cloud properties. An illustration of how this approach can be applied to cloud-resolving models is presented to evaluate their ability to simulate cloud processes.