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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Jan 2022
Development and technical paper |
| 06 Jan 2022
| 06 Jan 2022
WOMBAT v1.0: a fully Bayesian global flux-inversion framework
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
Michael Bertolacci
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
Jenny Fisher
School of Earth and Life Sciences, University of Wollongong, Wollongong, Australia
Ann Stavert
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
Matthew Rigby
School of Chemistry, University of Bristol, Bristol, UK
Yi Cao
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
Noel Cressie
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
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Total article views: 3,260 (including HTML, PDF, and XML)
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Total article views: 1,161 (including HTML, PDF, and XML)
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Cited
13 citations as recorded by crossref.
- Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion A. Stell et al. 10.5194/acp-22-12945-2022
- Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem M. Su et al. 10.1016/j.scitotenv.2023.162825
- Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux J. Wang et al. 10.1029/2023GL103379
- Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation M. Stanley et al. 10.5194/acp-24-9419-2024
- Earth’s CO2 battle: a view from space N. Cressie et al. 10.1093/jrssig/qmad003
- Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements D. Wesloh et al. 10.1029/2023JG007526
- National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework M. Bertolacci et al. 10.1214/23-AOAS1790
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. 10.5194/gmd-15-5547-2022
10 citations as recorded by crossref.
- Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion A. Stell et al. 10.5194/acp-22-12945-2022
- Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem M. Su et al. 10.1016/j.scitotenv.2023.162825
- Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Soil Dryness‐Shaped Spatial Pattern but Temperature‐dominated Total Flux J. Wang et al. 10.1029/2023GL103379
- Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation M. Stanley et al. 10.5194/acp-24-9419-2024
- Earth’s CO2 battle: a view from space N. Cressie et al. 10.1093/jrssig/qmad003
- Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements D. Wesloh et al. 10.1029/2023JG007526
- National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake B. Byrne et al. 10.5194/essd-15-963-2023
- A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
- Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals W. He et al. 10.1029/2022JD037773
- Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework M. Bertolacci et al. 10.1214/23-AOAS1790
3 citations as recorded by crossref.
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- WOMBAT v1.0: a fully Bayesian global flux-inversion framework A. Zammit-Mangion et al. 10.5194/gmd-15-45-2022
- Computationally efficient methods for large-scale atmospheric inverse modeling T. Cho et al. 10.5194/gmd-15-5547-2022
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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.
We present a framework for estimating the sources and sinks (flux) of carbon dioxide from...
