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 Section Description

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Last Updated: Tuesday, 15-Jul-2008 11:04:58 PDT

On-Demand Systems (Code 7542)
Section Head: 7542@nrlmry.navy.mil

The On-Demand Systems Section performs applied research and development on advanced workstations and small cluster for data management, data fusion, and data assimilation systems that can be forward deployed or centralized for reachback operations to provide highly tailored environmental support for naval operations.

The On-Demand Systems Section is working in these main areas:
  • Development of an On-Demand Analysis and Prediction Capability.
    • Adapt the NRL Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) for workstation/cluster use.
    • Develop the graphical user interface, database interface, back-end post processing, and web-based visualization techniques to support COAMPS-On Scene (COAMPS-OS®;).
    • Develop a high-resolution data assimilation system for COAMPS® and COAMPS-OS®;using weather radar, satellite, UAV and aircraft data.
    • Implement and test advanced numerical methods to improve COAMPS® depiction of tactically relevant meteorological parameters.
    • Extend Rapid Enviromental Assessment capability to include coupled air/ocean systems.
    • SEDRIS
    • ATCF
Linux cluster used to run COAMPS-OS
  • Development of data management techniques for the Naval Integrated Tactical Environmental System (NITES 2000) .
    • Tactical Environmental Data Server (TEDS) .
    • Data decoding and ingest capabilities (e.g., radar data).
    • Data quality control and distribution techniques.
    • Technical support to the TEDServices working group.

  • Development of a high-resolution, automated NOWCAST system to characterize the environment in near real-time.
    • Use data fusion technology to analyze all available data while providing common environmental situational awareness in support of strike warfare, ship self defense, and chemical/biological hazard assessments.
    • Improve the ability to diagnose environmental impacts and weather prediction model performance using advanced visualization techniques.


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