Current GOES Imagers observe the continental U.S. every 15 min, a resolution that has proven to be too coarse for identifying key indicators of impending severe weather. This has inhibited effective integration of satellite observations and their derived products into severe storm forecast operations. Analysis of GOES 1-min super-rapid scan operations (SRSO) imagery indicates that severe storms develop quite rapidly, from small cumulus to a tornadic supercell in as little as one hour. The presence of overshooting cloud tops and associated ice cloud detrainment in the lower stratosphere, and rapid changes in 1) cloud-top temperature during storm initiation, 2) mature storm updraft intensity, and 3) cloud-top divergence and vorticity near the updraft region are indicators of a severe storm that can be automatically detected by satellite-based algorithms up to 1 hour in advance of severe weather. These satellite imager-based indicators can be best recognized within SRSO data, considering that periods of rapid updraft intensification and internal storm processed responsible for tornado genesis and other severe weather typically can occur over a very short time period (~10 mins)
Using a fusion of SRSO satellite imagery and derived products, 4-D volumes of dual polarimetric radar data and products, and ground based GOES-R GLM proxies, we seek to answer the following and many other fundamental science questions: 1) How will severe weather indicators derived from satellite imager-based datasets complement new lightning-based and other well-established and newly-emerging radar-based severe weather identification/forecasting techniques?, 2) Is it possible to distinguish between tornadic and non-tornadic severe storms from satellite observations?, and 3) What quantitative value do SRSO observations offer to the weather forecasting and research communities? This presentation will highlight progress during the first year of this NASA-supported research effort.