Mr. Brice T. Womack Profile

Brice T. Womack

at Northrop Grumman Corp

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Publications (1)

Proceedings Article | 24 September 2007 Paper

Simulation for the design of next-generation global Earth observing systems

Michael Seablom, Stephen Talabac, Glenn Higgins, Brice Womack

Proceedings Volume 6684, 668413 (2007) https://doi.org/10.1117/12.735746

KEYWORDS: Sensors, Data modeling, Atmospheric modeling, Systems modeling, Control systems, Environmental sensing, Computer architecture, Space operations, LIDAR, Satellites

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Under a recently-funded NASA Earth Science Technology Office (ESTO) award we are now designing, and will eventually implement, a sensor web architecture that couples future Earth observing systems with atmospheric, chemical, and oceanographic models and data assimilation systems. The end product will be a "sensor web simulator" (SWS), based upon the proposed architecture, that would objectively quantify the scientific return of a fully functional modeldriven meteorological sensor web. Our proposed work is based upon two previously-funded ESTO studies that have yielded a sensor web-based 2025 weather observing system architecture, and a preliminary SWS software architecture that had been funded by NASA's Revolutionary Aerospace Systems Concept (RASC) and other technology awards. Sensor Web observing systems have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable meteorological features and events. A revolutionary architectural characteristic that could substantially reduce meteorological forecast uncertainty is the use of targeted observations guided by advanced analytical techniques (e.g., prediction of ensemble variance). Simulation is essential: investing in the design and implementation of such a complex observing system would be very costly and almost certainly involve significant risk. A SWS would provide information systems engineers and Earth scientists with the ability to define and model candidate designs, and to quantitatively measure predictive forecast skill improvements. The SWS will serve as a necessary trade studies tool to: evaluate the impact of selecting different types and quantities of remote sensing and in situ sensors; characterize alternative platform vantage points and measurement modes; and to explore potential rules of interaction between sensors and weather forecast/data assimilation components to reduce model error growth and forecast uncertainty. We will demonstrate key SWS elements using a proposed future lidar wind measurement mission as a use case.

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