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06/24/22 Applied Physics Laborator y 1 APL MURI Kickoff

APL MURI Kickoff

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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. - PowerPoint PPT Presentation

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Page 1: APL MURI Kickoff

04/22/23 Applied Physics Laboratory 1

APL MURI Kickoff

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APL MURI TEAM Bob Miyamoto David Jones Jim Pitton Keith Kerr Mark Krueger

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Key Strengths of the Team Navy/domain experience Related research projects

MHSII EVIS DRI

Visualization, Intelligent Agents, Engineering Statistics

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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

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Navy METOC & Uncertainty “Model of the Day?” Tropical Cyclone forecasts EFS at FNMOC

Ship Routing Long Range Temp Forecast

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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

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Examples of Navy Uncertainty Products

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FNMOC Ensemble Temp Mean

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FNMOC 2m Temp Plume

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FNMOC Gale Probability

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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

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FNMOC COAMPS

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UW Ensemble Mean

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Next Steps Cognitive Task Analysis at

Whidbey Island Norfolk

Collaborative work with NPMOC, NLMOC, NPMOF, & FNMOC

Visualization research >>>

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Exploratory Software Prototype Requirements Design Prototype Development Iterative Refinement Implementation Analysis

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Tentative Requirements User-based framework Analytic & Geospatial visualization

tools Collaborative, interactive exploration Cross-platform availability Easy extensibility Suitable for broad range of expertise

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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

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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

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VisAD Creations (courtesy VMET)Terrain and Wind Vectors

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VisAD Creations (Courtesy VMET)

Time Series Wind Fields

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VisAD Creations (courtesy VMET)

MM5 output – wind and temp fields