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Activity Number:
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271
- Statistical Modeling and Uncertainty Quantification for Atmospheric Remote Sensing Retrievals
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #312463
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Title:
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Functional ANOVA for Carbon Flux Estimates from Remote Sensing Data
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Author(s):
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Jonathan Hobbs* and Matthias Katzfuss and Hai Nguyen and Vineet Yadav
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Companies:
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Jet Propulsion Laboratory and Texas A&M University and Jet Propulsion Laboratory and Jet Propulsion Laboratory
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Keywords:
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Bayesian inference;
spatial statistics;
functional data analysis;
remote sensing
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Abstract:
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Scientific data products from multiple Earth-orbiting satellites are providing estimates of atmospheric composition with unprecedented spatial and temporal coverage. A growing constellation of satellites now provide a multi-year record of atmospheric carbon dioxide concentration. These products are used in combination with transport models to estimate the magnitude of carbon sources and sinks around the globe. In this flux inversion methodology, several choices, including the transport model and the strategy for combining multiple satellite products, impact the resulting estimates. We illustrate a functional analysis of variance (ANOVA) approach to estimate the common flux signals across implementation choices while documenting the effects of the individual instances.
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Authors who are presenting talks have a * after their name.
