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APL MURI Kickoff

APL MURI Kickoff. APL MURI TEAM. Bob Miyamoto David Jones Jim Pitton Keith Kerr Mark Krueger. Key Strengths of the Team. Navy/domain experience Related research projects MHSII EVIS DRI Visualization, Intelligent Agents, Engineering Statistics. APL Role in MURI Team.

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APL MURI Kickoff

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  1. APL MURI Kickoff Applied Physics Laboratory

  2. APL MURI TEAM • Bob Miyamoto • David Jones • Jim Pitton • Keith Kerr • Mark Krueger Applied Physics Laboratory

  3. Key Strengths of the Team • Navy/domain experience • Related research projects • MHSII • EVIS • DRI • Visualization, Intelligent Agents, Engineering Statistics Applied Physics Laboratory

  4. APL Role in MURI Team • Provide relevance to Navy needs • Coordinate interaction w/ Navy Orgs. • Develop task-based visualizations • Integrate component research efforts • Facilitate interaction among MURI participants Applied Physics Laboratory

  5. Navy METOC & Uncertainty • “Model of the Day?” • Tropical Cyclone forecasts • EFS at FNMOC • Ship Routing • Long Range Temp Forecast Applied Physics Laboratory

  6. Navy METOC & Uncertainty • Based on my experience as the Operations Officer at FNMOC • Forecasters have limited understanding • Confusing products • Not designed for forecaster • Great potential still not fully realized Applied Physics Laboratory

  7. Examples of Navy Uncertainty Products Applied Physics Laboratory

  8. FNMOC Ensemble Temp Mean Applied Physics Laboratory

  9. FNMOC 2m Temp Plume Applied Physics Laboratory

  10. FNMOC Gale Probability Applied Physics Laboratory

  11. Areas to investigate • How forecasters deal with uncertainty • Uses of uncertainty information by METOC customers • Easier ways to create uncertainty products • Better visualization techniques • Verification Applied Physics Laboratory

  12. FNMOC COAMPS Applied Physics Laboratory

  13. UW Ensemble Mean Applied Physics Laboratory

  14. Next Steps • Cognitive Task Analysis at • Whidbey Island • Norfolk • Collaborative work with NPMOC, NLMOC, NPMOF, & FNMOC • Visualization research >>> Applied Physics Laboratory

  15. Exploratory Software Prototype • Requirements • Design • Prototype Development • Iterative Refinement • Implementation Analysis Applied Physics Laboratory

  16. Tentative Requirements • User-based framework • Analytic & Geospatial visualization tools • Collaborative, interactive exploration • Cross-platform availability • Easy extensibility • Suitable for broad range of expertise Applied Physics Laboratory

  17. Implementation Tools (1) • Java Language • Cross-platform • Sophisticated network (web) model • Can “wrap” models in other languages • Inference Engine • Easy to tailor level of expertise • Can both bound and sequence operations Applied Physics Laboratory

  18. Implementation Tools (2) • VisAD for Visualization • Model-View-Controller architecture • Remote display and collaboration • Sophisticated data model • High-level scripting language (Jython) • Supports specialized “toolkit” development • Currently used within meteorological community Applied Physics Laboratory

  19. VisAD Creations (courtesy VMET)Terrain and Wind Vectors Applied Physics Laboratory

  20. VisAD Creations (Courtesy VMET) Time Series Wind Fields Applied Physics Laboratory

  21. VisAD Creations (courtesy VMET)MM5 output – wind and temp fields Applied Physics Laboratory

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