This paper describes the use of a machine learning data fusion methodology to support development of an automated
short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is
combined with observations of current storms and derived indications of the onset of rapid change. Predictor data
include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land
surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and
morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic
to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of
predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help
determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation
Administration's Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated
Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln
Laboratory and the NOAA Earth System Research Laboratory's Global Systems Division. CoSPA is scheduled to
become part of the NextGen Initial Operating Capability by 2012.
Satellite-based brightness temperature observations are used in a wide range of applications for monitoring weather
systems over land and especially over water, including short-term prediction of the evolution of weather systems.
Results are presented from an evaluation of three extrapolation-based nowcasting procedures to predict satellitebased
brightness temperatures up to 3 hours into the future. Analyses are based on using METEOSAT-8 Spinning
Enhanced Visible and Infrared Imager (SEVIRI) data as a proxy for the Advanced Baseline Imager (ABI) to be
flown on the next-generation National Oceanic and Atmospheric Administration (NOAA) Geostationary
Operational Environmental Satellite (GOES)-R series.