skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Inferring Atmospheric Release Characteristics in a Large Computer Experiment Using Bayesian Adaptive Splines

Journal Article · · Journal of the American Statistical Association
 [1];  [2];  [3];  [4];  [4]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, Santa Cruz, CA (United States)
  3. Climate Corporation, San Francisco, CA (United States)
  4. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
+ Show Author Affiliations

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. Here, we use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
AC52-07NA27344; 89233218CNA000001
OSTI ID:
1822600
Alternate ID(s):
OSTI ID: 1494474
Report Number(s):
LLNL-JRNL-732282; LA-UR-18-21836; 883246
Journal Information:
Journal of the American Statistical Association, Vol. 114, Issue 528; ISSN 0162-1459
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 9 works
Citation information provided by
Web of Science

References (35)

Multivariate Adaptive Regression Splines journal March 1991
A calibration and data assimilation method using the Bayesian MARS emulator journal February 2013
Local Gaussian process approximation for large computer experiments text January 2015
Spline-Based Emulators for Radiative Shock Experiments With Measurement Error journal June 2013
Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA journal January 2015
Evaluation of a Puff Dispersion Model in Complex Terrain journal March 1992
Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions journal November 2011
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling journal September 2008
Computer Model Calibration Using High-Dimensional Output journal June 2008
Local Gaussian Process Approximation for Large Computer Experiments text January 2015
Modularization in Bayesian analysis, with emphasis on analysis of computer models journal March 2009
Mixtures of g Priors for Bayesian Variable Selection journal March 2008
Bayesian calibration of computer models journal August 2001
Spline Regression in the Presence of Categorical Predictors: SPLINE REGRESSION IN THE PRESENCE OF CATEGORICAL PREDICTORS journal September 2014
Design and analysis of computer experiments journal December 2010
Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology journal December 2011
BART: Bayesian additive regression trees journal March 2010
Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA text January 2015
A Framework for Validation of Computer Models journal May 2007
Calibrating a large computer experiment simulating radiative shock hydrodynamics journal September 2015
Bayesian Data Analysis book November 2013
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination journal January 1995
Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA. text January 2015
Design and Analysis of Computer Experiments journal November 1989
Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant journal January 2017
An Adaptive Metropolis Algorithm journal April 2001
Efficient sampling schemes for Bayesian MARS models with many predictors journal April 2005
Local Gaussian Process Approximation for Large Computer Experiments journal April 2015
Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2 journal January 2005
Gaussian predictive process models for large spatial data sets journal September 2008
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates journal February 2001
Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors journal August 2008
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors journal August 2019
BART: Bayesian additive regression trees text January 2008
Calibrating a large computer experiment simulating radiative shock hydrodynamics text January 2014

Linked Research (3)

Similar Records

Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
model calibration
inverse problem
multivariate emulation
sensitivity analysis
categorical inputs
functional outputs
uncertainty quantification
atmospheric dispersion models