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Activity Number: 309 - Bayesian Modeling in Physical Sciences and Engineering
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328975 Presentation
Title: Bayesian Estimation of Pollutant Emissions Using Multiscale Data
Author(s): Cosmin Safta* and Ray Bambha and Hope Michelsen
Companies: Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories
Keywords: hierarchical Bayesian inversion; multiscale data; structural errors; random fields

Methane is a powerful greenhouse gas and understanding the relative importance of methane released from anthropogenic sources is very important for developing and improving emission models. Reconciling atmospheric measurements with inventory-based estimates for various emissions sectors remains a great challenge due to large discrepancies between models. Current approaches for measuring regional emissions yield highly uncertain estimates because of the sparsity of measurement sites (ground-based and satellites) and the presence of multiple simultaneous sources. We present a hierarchical Bayesian framework to estimate surface fluxes based on atmospheric concentration measurements and a Lagrangian transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport). Structural errors in the transport model and emission databases are estimated with the help of multi-scale measurements. Spatial-temporal discrepancies are modeled as spectral random fields with stochastic components parameterized as polynomial chaos expansions. We conduct the analyses at a regional scales that are based on similar geographical and meteorological conditions.

Authors who are presenting talks have a * after their name.

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