This proposal requests funding to leverage NASA assets to continue optimizing a GOES convective initiation (CI) 0-1 hr nowcasting algorithm for performance across various ""convective regime"" types, and to transition the algorithm into a fielded FAA decision support system (DSS). The goal is to use satellite data to enhance predictability of the timing, location and growth rate of CI by more succinctly defining the characteristics of convective cloud development. The focus will be on nowcasting the first occurrence of >35 dBZ echoes within convective clouds, -- i.e. CI, across various thermodynamic environments that generate convection (i.e. ""regimes""). The hypothesis is that this enhanced predictability of CI will lead to more accurate forecasts of the onset and intensity of hazardous convective-scale weather events that impact aviation across large sections of the U.S., as demonstrated through DSS forecasts. Examples of convective regime-types include dry mountainous environments, and humid tropical conditions. Currently, the GOES CI algorithm suffers from low predictive skill scores due to its lack of tuning to the different convective environments present across the continental U.S. at any given time. NASA assets are optimizing the GOES-based CI method via the in-line convective-regime ""training"" information they provide via a multi-parameter database, statistical look-up table approach. In particular, NASA's CloudSat Cloud Profiling Radar (CPR), the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Cloud-Aerosol Lidar, and the MODerate resolution Infrared Spectrometer (MODIS; aboard the Aqua satellite) observations (along with numerical weather prediction model thermodynamic fields) improve the accuracy of the GOES CI algorithm as atmospheric conditions unique to various convective regimes are accounted for (i.e. cloud-top height, cloud-top temperature, and an estimate of cloud-top glaciation). The CI algorithm will be incorporated into the Corridor Integrated Weather System (CIWS) DSS, which provides automated 0-2 hr convective weather forecasts in real-time to FAA decision makers. Through this effort, detections of early storm development and growth rates will be enhanced within CIWS. Specifically, satellites are the primary source of CI (pre-radar echo) information, representing a powerful capability to improve fine-scale convective-scale forecasts, and therefore the utility of the DSS. FAA benchmark activities will quantify the value of the NASA satellite-enhanced CI algorithm within CIWS, through scoring software and evaluation of CIWS-user feedback. The satellite analysis will be validated for selected events with data from dual-polarimetric Doppler radar for improved microphysical observations (graupel, freezing altitudes and ice distributions), at the University of Alabama in Huntsville.