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Activity Number: 419
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #320345 View Presentation
Title: Remote Sensing Retrievals for Atmospheric Carbon Dioxide: Quantifying Uncertainty in the Presence of Nonlinearity and Nuisance Parameters
Author(s): Jonathan Hobbs* and Amy Braverman and Jenny Brynjarsdottir and Noel Cressie and Dejian Fu and Robert Granat and Michael Gunson and Joaquim Teixeira
Companies: Jet Propulsion Laboratory and Jet Propulsion Laboratory and Case Western Reserve University and University of Wollongong and Jet Propulsion Laboratory and Jet Propulsion Laboratory and Jet Propulsion Laboratory and University of California at Los Angeles
Keywords: Uncertainty quantification ; Bayesian inference ; Carbon cycle ; Remote sensing ; Atmospheric science
Abstract:

Earth-orbiting satellites that monitor atmospheric greenhouse gases, such as NASA's Orbiting Carbon Observatory-2 (OCO-2), collect measurements of reflected sunlight at fine spatial and temporal resolution. The atmospheric constituent of interest, such as carbon dioxide (CO2) concentration, is estimated from these observations using a retrieval algorithm, which typically involves a mathematical representation of the transfer of radiation through the atmosphere and its interaction with gas molecules and particles in the atmosphere and Earth's surface. These additional nuisance constituents are not perfectly known in this retrieval problem, but errors in their representation can be correlated with errors in CO2. We illustrate the impact of these interference errors and their relationship with the nonlinearity of the physical model and with uncertainty in retrieval algorithm inputs through a simulation framework.


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