Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared Imagery: Methodology and Validation

Abstract

Abstract The purpose of this study is to develop and validate an algorithm for tracking and forecasting radiative and morphological characteristics of mesoscale convective systems (MCSs) through their entire life cycles using geostationary satellite thermal channel information (10.8 μm). The main features of this system are the following: 1) a cloud cluster detection method based on a threshold temperature (235 K), 2) a tracking technique based on MCS overlapping areas in successive images, and 3) a forecast module based on the evolution of each particular MCS in previous steps. This feature is based on the MCS’s possible displacement (considering the center of the mass position of the cloud cluster in previous time steps) and its size evolution. Statistical information about MCS evolution during the Wet Season Atmospheric Mesoscale Campaign (WETAMC) of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) was used to obtain area expansion mean rates for different MCSs according to their lifetime durations. This nowcasting tool was applied to evaluate the MCS displacement and size evolution over the Del Plata basin in South America up to 120 min with 30-min intervals. The Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) technique’s performance was evaluated based on the difference between the forecasted and observed images. This evaluation shows good agreement between the observed and forecast size and minimum temperature for shorter forecast lead times, but tends to underestimate MCS size (and overestimate the minimum temperature) for larger forecast lead times.