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Activity Number: 423
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320282
Title: Bayesian Uncertainty Quantification for CO2 Retrieval
Author(s): Jenny Brynjarsdottir* and Jonathan Hobbs and Amy Braverman
Companies: Case Western Reserve University and Jet Propulsion Laboratory and Jet Propulsion Laboratory

NASA's OCO-2 mission collects space-based measurements of atmospheric CO2. Data are collected with high spatial and temporal resolution and the data product includes both an estimate of column averaged CO2 dry air mole fraction (XCO2) and an estimate of uncertainty. The OCO-2 instrument measures reflected sunlight and uses a physical model and Bayes Theorem to estimate XCO2. However, computational shortcuts are taken to obtain an estimate of the posterior mode (X.hat) and posterior variance (S.hat). Even thought the forward model is not linear, users usually treat the posterior distribution as Gaussian with mean X.hat and variance S.hat. Also, uncertainty due to several uncertain parameter inputs and model discrepancy is not taken into account. A UQ group within the OCO-2 mission has developed a test-bed, where a surrogate model (simplified, but physically realistic) can be used to study various aspects of the retrieval. In this talk we will discuss a few test-bed experiments, such as full exploration of the posterior via MCMC, effects of model discrepancy and parameter uncertainty, and how our results relate to the actual OCO-2 data product.

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

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