Development of an Automated Cloud Temperature–Particle Effective Radius Procedure Emphasizing the Use of 1-min GOES–R Super Rapid Scan Observations For Severe Storm Analysis

Since 2012, periodic collections of 1-min resolution super rapid scan operations for GOES-R (SRSOR) GOES-14 observations were recorded for significant weather events over the U.S. Use of 1-min observations allows us to evaluate aspects of growing convective clouds that were not possible with lower time resolution (5- or 15-min) data, as available from other existing geostationary satellites. As shown in Lensky and Rosenfeld (2006) and Rosenfeld et al. (2008), and very recently in Mecikalski et al. (2016), the microstructure of cloud particles at cloud top has been found to relate strongly to the intensity of a convective cloud’s updraft, owing to the fact that cloud particles grow slower over the shorter time periods of updraft growth. So, a very strong updraft of ≥40 ms^–1 that reaches the equilibrium level in 10’s of minutes contains generally smaller particles as compared to more slowly growing clouds. Along these lines, small cloud particles results in a delay in the formation of precipitation within a cloud, or specifically when particles reach sizes of at least 14 µm in diameter. Cloud top glaciation will also be delayed when a cloud is composed of small particles, versus when larger particles are present, in the presence of more homogeneous pollution. These updraft speed—cloud top particle size—glaciation temperature relationships have lead Rosenfeld et al. to develop so-called temperature (T)—particle effective radius (r_e) diagrams, that map the local cloud ensemble as a response to cloud growth rates and hence to instability and cloud/updraft organization.

The T-r_e 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-r_e 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-r_e 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 r_e 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 r_e that are plotted as x- and y-axis data points.

During this presentation, T-r_e 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.