Articles | Volume 15, issue 1
Geosci. Model Dev., 15, 45–73, 2022
Geosci. Model Dev., 15, 45–73, 2022
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 et al.

Data sets

Code for reproducing results and figures in manuscript Michael Bertolacci, Andrew Zammit-Mangion, Yi Cao, Jenny Fisher, and Ann Stavert

Model code and software

Intermediate files for reproducing results in manuscript Michael Bertolacci, Jenny Fisher, Yi Cao, Ann Stavert, and Matthew Rigby


The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.