Storm tracking and monitoring using objective synoptic diagnosis and cluster identification from infrared Meteosat imagery: A case study

The present paper investigates the potential of combining image processing techniques based on cluster analysis of infrared (IR) Meteosat images with dynamic meteorological theory on synoptic systems. From this last point of view the highest probability of deep convective development is favoured whe re the overlapping of four mechanisms acting at synoptic scale is produced: upward quasi-geostrophic forcing, convergence of water vapour at low level s, convective instability in the lower troposphere and great convective ava ilable potential energy. Cloud tracking is performed over sequences of Mete osat IR images by using a shape parameterisation approach after appropriate filtering for non-significant clouds and automated identification of conve ctive systems. The integrated methodology is applied to the case study of t he heavy rainfall event which produced floods in the South of France and th e North of Italy on September 27-28(th), 1992. The analysis focuses on the monitoring and explanation of the zones most affected by heavy rainfall wit h the aim of investigating possible improvements of the predictive potentia l of cloud tracking and allowing identification of the areas which most len d themselves to flash floods for use in operational flood forecasting appli cations.