Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-45-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-45-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Development and technical paper
 | 
06 Jan 2022
Development and technical paper |  | 06 Jan 2022

WOMBAT v1.0: a fully Bayesian global flux-inversion framework

Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, and Noel Cressie

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Short summary
We present a framework for estimating the sources and sinks (flux) of carbon dioxide from satellite data. The framework is statistical and yields measures of uncertainty alongside all estimates of flux and other parameters in the underlying model. It also allows us to generate other insights, such as the size of errors and biases in the data. The primary aim of this research was to develop a fully statistical flux inversion framework for use by atmospheric scientists.
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