The T-re diagram algorithm of Rosenfeld et al. (2008) has been developed into an automated system, but with most application work done using Meteosat Second Generation (MSG) satellite imagery over Europe and Africa, as well as Advanced Very High Resolution Radiometer (AVHRR) and Visible Infrared Imaging Radiometer Suite (VIIRS) observations (which allow only limited views of convective scenes given the polar orbiting nature of these two satellites). The goal of this present study is to develop an automated T-re procedure over the U.S. with a focus on use of National Aeronautics and Space Administration (NASA) Langley Research Center Satellite ClOud and Radiative Property retrieval System (SATCORPS) GOES-14 cloud property fields, in combination with a convective cloud mask (Berendes et al. 2008) that specifically identifies convective clouds within a satellite scene. These GOES cloud properties are analogous to those that will be available operationally during the GOES-R era. The daytime cloud parameters were derived using the visible infrared shortwave infrared split window technique (VISST; Minnis et al. 2008, 2011a) developed for the Clouds and Earths Radiant Energy System (CERES) project (Wielicki et al. 1998). The methodology for developing the T-re profiles follows that in Mecikalski et al. (2006) in which meso-β scale (100-250 km) regions are searched centered on a cluster of convective clouds for re and cloud top T values. The ensemble of convective clouds within a region, of various sizes and depths, from the smallest to tallest cumulus cloud, develop then a vertical profile of T and re that are plotted as x- and y-axis data points.
During this presentation, T-re results from several days in 2012-2015 will be shown, with a emphasis placed on: (1) How these profiles change over 1- to 5-min time intervals, and (2) How these profiles both relate to predict the occurrence of severe weather (i.e. storms with large hail, high winds, tornadoes). Comparisons are made also between convective storm regimes or environments, and to Storm Prediction Center (SPC)-based severe weather reports.