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Activity Number: 271 - Statistical Modeling and Uncertainty Quantification for Atmospheric Remote Sensing Retrievals
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #310981
Title: Accounting for Model Discrepancy in CO2 Retrievals
Author(s): Jenny Brynjarsdottir*
Companies: Case Western Reserve University
Keywords: Model error; OCO-2; CO2
Abstract:

The Orbiting Carbon Observatory 2 (OCO-2) collects space-based measurements of atmospheric CO2. The CO2 measurements are indirect as the instrument observes radiances (reflected sunlight) over a range of wavelengths and a physical model is inverted, via Bayes Theorem, to estimate CO2 concentration in the atmosphere. This inference is in fact an estimation of physical parameters, aka calibration, which can be both biased and over confident when model error is present but not accounted for. The OCO-2 mission addresses this problem in a few different ways, e.g. with a post-inference bias correction procedure based on ground measurements. This talk will discuss methods to account for structured and informative model error directly in the inversion procedure to lessen bias and provide more reliable uncertainty estimates. This is joint work with Jonathan Hobbs and Amy Braverman at JPL.


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

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