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Short-term forecasts of convective initiation (CI) and early storm development are essential for providing decision makers adequate warning to mitigate aviation safety, lightning, flash flood, and other storm-related hazards. Such hazards have a particularly high impact in the Gulf of Mexico region, where convection is prevalent year-round, workers are exposed on ships and oil platforms and helicopter transportation is common. Operational weather prediction models are currently poor at pinpointing locations and timing of CI, while extrapolation techniques that work well for pre-existing storms do not apply to CI. The present study will fuse operational satellite and model data using artificial intelligence (AI) techniques to create improved CI forecasts over both land and water in the Gulf of Mexico. Results will be immediately utilized by the Corpus Christi Weather Forecast Office (WFO), and this effort will serve as a feasibility study that, if successful, will lead to improvements in Convective Nowcast Oceanic (CNO) and Global Turbulence decision support systems, currently under development under NASA funding for use in the World Area Forecast System (WAFS). The proposed study will utilize Tropical Rainfall Measuring Mission (TRMM) and CloudSat to characterize storms to define "truth" for tuning and verification. Moderate Resolution Imaging Spectroradiometer (MODIS) land temperature gradients and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sea surface temperatures will be analyzed as potential predictor fields, as will Weather Research and Forecasting Rapid Refresh (WRF-RR) and/or Global Forecast System (GFS) model data, both of which assimilate NASA satellite data products. AI techniques will be employed to identify new data for incorporation into the SATellite Convection AnalySis and Tracking (SATCAST) algorithm, to optimize SATCAST and to create a probabilistic predictive model of early storm development. Synergy with NASA-funded research on CI, lightning initiation, verification, oceanic nowcasting, and global turbulence prediction will be exploited.

Project PI: John Mecikalski/University of Alabama in Huntsville

University of Alabama in Huntsville, NSSTC, 320 Sparkman Drive, Huntsville, Alabama 35805-1912.

Phone: (256)961-7046

Email: john.mecikalski@nsstc.uah.edu

http://www.aip.org/dbis/stories/2009/19068.html

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Started: Sep 27, 2010

Last Activity: Jan 04, 2011

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