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