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Panel Session on Uncertainty in Atmospheric Transport and Dispersion Models

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Title: PowerPoint Presentation Author: Unknown User Last modified by: Paul Created Date: 12/31/2003 4:26:31 PM Document presentation format: On-screen Show – PowerPoint PPT presentation

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Title: Panel Session on Uncertainty in Atmospheric Transport and Dispersion Models


1
Panel Session on Uncertainty in Atmospheric
Transport and Dispersion Models
  • Moderator
  • Steven R. Hanna
  • Harvard School of Public Health
  • shanna_at_hsph.harvard.edu
  • for OFCM Session
  • GMU, Fairfax, VA 19 July 2005
  • P060 Hanna OFCM 19 July 05

2
Panel Plan
  • Moderator Steve Hanna
  • Rapporteurs Pat Hayes (DTRA) and Katherine
    Snead (EPA OAR)
  • Panelists (12 min each) - Dave Stauffer (Penn
    State), John Wyngaard (Penn State), Ian Sykes
    (Titan), Mike Brown (LANL), Joe Chang (HSI)
  • Discussion (50 min)
  • Wrap Up Bruce Hicks (NOAA) and Mark Miller
    (NOAA)

3
Sources of Model Uncertainty
  • Natural stochastic (turbulent) variations
  • Input data (e.g., wind speed observations by
    anemometers and by radiosondes) have errors or
    are unrepresentative
  • Physics assumptions in the model technical
    document are incorrect or inadequate or are
    inappropriate for the intended application
  • Model parameters (e.g., scaling constants) are
    uncertain
  • Coding/software errors
  • The users guide is unclear about which input data
    to use and what switches to set, causing
    different users to get different results
  • The model is best suited (tuned) for certain
    simple scenarios where field data are available.
    Uncertainties increase for source scenarios and
    met conditions that have not been so well
    studied.

4
Relation to uncertainty studies by other
disciplines
  • NRC, EPA, and others have a 20 year history of
    accounting for uncertainty in modeling but not
    usually air quality modeling
  • Books by experts (Hoffman, Cullen and Frey)
    usually focus on health risk models and other
    empirical models
  • Many approaches exist and have been tested and
    published
  • BUT Some of the approaches are less useful for
    atmospheric models, since our models are
    deterministic and not empirical

5
Two approaches to predicting uncertainty
  • Direct - The uncertainty (i.e., the pdf of C) is
    directly predicted by the model (e.g.,
    HPAC/SCIPUFF), which includes formulas for
    internal plume fluctuations and meandering.
  • External - The model does not directly predict
    the uncertainty. Instead the uncertainty is
    assumed to be caused by variations in inputs and
    model parameters and is estimated separately,
    through multiple model runs (ensembles),
    sensitivity studies, etc.

6
Overview of available external methods for
estimating uncertainty, ordered by complexity
  • Full Monte Carlo probabilistic (allows all inputs
    and model parameters to be simultaneously varied
    and correlations determined, but takes a lot of
    time, and may produce too much uncertainty)
  • Ensemble method (a subset of the MC method with a
    few model runs sufficient to capture spread)
  • Jackknife method (another subset of MC also
    called the leave-out-one method)
  • Response surface methods (fits orthogonal
    functions to MC outputs can be precalculated)
  • One-at-a-time (OAT) sensitivity studies (not good
    for nonlinear systems)

7
Rules of Thumb Based on Experience
  • Experts experiences suggest factor of two
    uncertainty in dispersion model predictions in
    best scenarios
  • Uncertainty increases to factor of 5 or 10 for
    poorly defined scenarios or complex terrain
    and/or met conditions
  • In- plume sC/C is about unity on the plume
    centerline for one-hour sampling times and is
    larger (factor of 5 to 10) on plume edges.
  • Etc.
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