Probabilistic Prediction of Late-Time Nuclear Clouds

Abstract

Developments, applications, and evaluation of the SCIPUFF (Second- order Closure Integrated PUFF) model are presented. The model uses turbulence closure theory to relate dust cloud dispersion to measurable statistics of the velocity field, and also contains a prediction of the 'uncertainty' in a model prediction arising from the random velocity fluctuations. SCIPUFF has been extended to include a complete tensor description of the second-order spatial moments, allowing an accurate representation of the shear-induced distortion of a cloud. The model also incorporates observational velocity spectra information and an option to use a 'relative' diffusion prediction has been implemented. An extensive evaluation exercise has been conducted using the Across North America Tracer Experiment (ANATEX). Surface dose samples and short term aircraft samples were predicted and reasonably good agreement obtained. Some skill was also demonstrated in a prediction based on a 'climatological' average of the wind field, i.e., no detailed information about the flow. Dust cloud, Turbulence, Nuclear cloud, Probabilistic prediction, Atmospheric diffusion, Transport and diffusion, Uncertainty

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1994
Accession Number
ADA277271

Entities

People

  • Douglas S. Henn
  • R. I. Sykes
  • Stephen F. Parker

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Atmospheric Motion
  • Boundary Layer
  • Computational Fluid Dynamics
  • Computational Science
  • Data Science
  • Databases
  • Information Science
  • Layers
  • Mathematical Models
  • Meteorology
  • Stratified Fluids
  • Turbulence
  • Turbulent Mixing
  • Two Dimensional
  • United States
  • Wind Shear

Fields of Study

  • Physics

Readers

  • Aerosol Science/Aerosol Physics
  • Computational Modeling and Simulation
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers