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Feature Tracking Process Analysis

Feature Tracking Process Analysis. Barron Henderson, William Vizuete, and Harvey Jeffries. 5th Annual CMAS Conference Friday Center October 16-18, 2006 http://ftpozone.sph.unc.edu. Outline. History and Functionality Recent Enhancements Potential Applications.

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Feature Tracking Process Analysis

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  1. Feature Tracking Process Analysis Barron Henderson, William Vizuete, and Harvey Jeffries 5th Annual CMAS Conference Friday Center October 16-18, 2006 http://ftpozone.sph.unc.edu

  2. Outline • History and Functionality • Recent Enhancements • Potential Applications

  3. Photochemical Grid Models: Explaining the Unexplainable • As Oreskes (1994) and later Beck (2002) have demonstrated, atmospheric models are “open systems” that have “essentially unknowable” inputs • Can have a wide variety of inputs • Generated by different groups • Minimum level of detail • Come from models with their own uncertainty • Easily suffer from compensating errors • Getting the ‘right answer’ for the ‘wrong reasons’ • Model Performance Evaluations • Process Analysis

  4. z x y Process Analysis Quantifies Model Processes • 1994 - Jeffries, H. E., and Shawn Tonnesen. A Comparison of two Photochemical Reaction Mechanisms Using Mass Balance and Process Analysis. Atmospheric Environment 28 (18):2991-3003. • In model algorithm: • Process Rates • Reaction Rates • Time-Step Averaged • Post Processor • Extraction • Aggregation y x

  5. z x y UT/UNC Collaboration Adds Variable Mixing Height • 2005 - Vizuete, William. Implementation of Process Analysis in a Three-Dimensional Air Quality Model, Chemical Engineering, University of Texas - Austin, Austin. • Time Variable Mixing Height • Convex Shapes z x

  6. Outline • History and Functionality • Recent Enhancements • Potential Applications

  7. Enhancement 1: Converted Process Analysis to Python • Python Based Process Analysis • Urban Airshed Model (UAM) File Interfaces • Spatial Aggregation • Entrain and Detrainment • Increased Volume Shape Flexibility • Automated Mixing Height Identification

  8. z x y Enhancement 2: Allow for Spatially Variable Mixing Height z cells

  9. Enhancement 3: Enable Focus Volume to Follow Ozone Peak 11 12 13 14

  10. z x y Enhancements Require New Algorithms for En(De)trainment • Vertical • Simultaneous Entrainment and Detrainment z x • Horizontal • Simultaneous Entrainment and Detrainment y x

  11. Outline • History and Functionality • Recent Enhancements • Potential Applications

  12. Potential Applications • Features of Interest • Concentration Peaks • Chemical Plumes • Impacts of Mega-cities on surroundings • Transcontinental Chemical Transport • Wildfires • Airplane Observations • Any Moving Feature! • Moving Process Analysis allows us to quantify transported and local processes and their interactions

  13. Acknowledgments • Funding From: • 8 Hour Ozone Coalition • HARC H60 “Regional Transport Modeling for East Texas” - Jay Olaguer, Project Officer • Thanks to: • Jim Smith and TCEQ for providing CAMx ready files • Dr. Kimura at UT for his work on the previous versions of Process Analysis • Dr. Byeong-Uk Kim at Georgia Dept. of Natural Resources • The rest of the UNC MAQ Lab Group

  14. 11 12 13 14 Questions? http://ftpozone.sph.unc.edu/

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