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Activity Number:
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22
- Statistical Methods for Heterogeneous and Massive Remote Sensing Data
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #323704
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Title:
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Towards Accounting for Model Error in CO2 Retrievals from the OCO-2 Satellite
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Author(s):
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Jenny Brynjarsdottir* and Jonathan Hobbs and Amy Braverman
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Companies:
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Case Western Reserve University and Jet Propulsion Laboratory and Jet Propulsion Laboratory
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Keywords:
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Uncertainty Quantification ;
Remote sensing ;
Bayesian analysis
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Abstract:
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The Orbiting Carbon Observatory 2 (OCO-2) collects space-based measurements of atmospheric CO2. The CO2 measurements are indirect, the instrument observes radiances (reflected sunlight) over a range of wavelengths and a physical model is inverted to estimate the atmospheric CO2. This inference is in fact an estimation of physical parameters, 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 informative model error directly in the inversion procedure to lessen bias and provide more reliable uncertainty estimates.
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Authors who are presenting talks have a * after their name.
