Detection and forecast of convective clouds using MSG data for aviation support

Convective clouds are among the most dangerous meteorological phenomena for aviation because they are responsible of the presence of thunderstorms causing heavy rains, hailstorms, lightnings, wind shear, turbulence and icing phenomena. For this reason it is crucial to detect and early forecast them. In the present work different algorithms have been implemented for reaching this aim using Meteosat Second Generation satellite data and comparing brightness temperatures of two satellite images in different channels. In addition convective clouds have been simplified in order to report on board only the relevant information about hazardous areas to be avoided during the flight and to reduce the information weight. In order to provide forecasts in the following 24-48 hours, the algorithms developed have been applied to synthetic satellite images produced by the radiative transfer model RTTOV, a model simulating the radiances and brightness temperatures as they could be seen from satellite.