Storm Tracking and Monitoring Using Objective Synoptic Diagnosis and Cluster Identification from Infrared Meteosat Imagery: A Case Study

Summary

 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 where the overlapping of four mechanisms acting at synoptic scale is produced: upward quasi-geostrophic forcing, convergence of water vapour at low levels, convective instability in the lower troposphere and great convective available potential energy. Cloud tracking is performed over sequences of Meteosat IR images by using a shape parameterisation approach after appropriate filtering for non-significant clouds and automated identification of convective systems. The integrated methodology is applied to the case study of the heavy rainfall event which produced floods in the South of France and the 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 with the aim of investigating possible improvements of the predictive potential of cloud tracking and allowing identification of the areas which most lend themselves to flash floods for use in operational flood forecasting applications.