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

原文摘要

Devin Francoma*, Bruno Sansób, Vera Bulaevskayac, Donald Lucasd & Matthew Simpsonda Los Alamos National Laboratory, Los Alamos, NM; b University of California Santa Cruz, Santa Cruz, CA; c Climate Corporation, San Francisco, CA; d Lawrence Livermore National Laboratory, Livermore, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.These materials were reviewed for reproducibility.This work was authored as part of the Contributor’s official duties as an Employee of the United Stated Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such words under U.S. Law.CONTACT Devin Francom dfrancom@lanl.gov PO Box 1663, MS F600, LANL, Los Alamos, NM 87545.AbstractAn 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. 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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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