Articles | Volume 9, issue 9
https://doi.org/10.5194/gmd-9-3213-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-3213-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
19 Sep 2016
Development and technical paper |
| 19 Sep 2016
Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo
School of Chemistry, University of Bristol, Bristol, UK
Matt Rigby
School of Chemistry, University of Bristol, Bristol, UK
Anita L. Ganesan
School of Geographical Sciences, University of Bristol,
Bristol, UK
Alistair J. Manning
Hadley Centre, Met Office, Exeter, UK
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38 citations as recorded by crossref.
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38 citations as recorded by crossref.
- Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
- Marine Nitrous Oxide Emissions From Three Eastern Boundary Upwelling Systems Inferred From Atmospheric Observations A. Ganesan et al. 10.1029/2020GL087822
- Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area J. Sheng et al. 10.1088/1748-9326/ac24d1
- Perfluorocyclobutane (PFC-318, <i>c</i>-C<sub>4</sub>F<sub>8</sub>) in the global atmosphere J. Mühle et al. 10.5194/acp-19-10335-2019
- Quantifying sources of Brazil's CH<sub>4</sub> emissions between 2010 and 2018 from satellite data R. Tunnicliffe et al. 10.5194/acp-20-13041-2020
- Rapid increase in ozone-depleting chloroform emissions from China X. Fang et al. 10.1038/s41561-018-0278-2
- History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE) R. Prinn et al. 10.5194/essd-10-985-2018
- Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach P. Levy et al. 10.5194/bg-15-1497-2018
- 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
- Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields L. Western et al. 10.5194/gmd-13-2095-2020
- The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. 10.5194/gmd-14-5331-2021
- Technical Note: Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation P. Kountouris et al. 10.5194/acp-18-3027-2018
- Increase in CFC-11 emissions from eastern China based on atmospheric observations M. Rigby et al. 10.1038/s41586-019-1193-4
- An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data M. Lunt et al. 10.5194/acp-19-14721-2019
- Substantial increase in perfluorocarbons CF 4 (PFC-14) and C 2 F 6 (PFC-116) emissions in China M. An et al. 10.1073/pnas.2400168121
- Continued Emissions of the Ozone‐Depleting Substance Carbon Tetrachloride From Eastern Asia M. Lunt et al. 10.1029/2018GL079500
- Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition H. Nesser et al. 10.5194/amt-14-5521-2021
- Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020) M. Lunt et al. 10.5194/acp-21-16257-2021
- Anthropogenic Chloroform Emissions from China Drive Changes in Global Emissions M. An et al. 10.1021/acs.est.3c01898
- Mission CO2ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide N. Cressie 10.1080/01621459.2017.1419136
- Atmospheric observations show accurate reporting and little growth in India’s methane emissions A. Ganesan et al. 10.1038/s41467-017-00994-7
- The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015 G. Monteil et al. 10.5194/acp-20-12063-2020
- Atmospheric radiocarbon measurements to quantify CO<sub>2</sub> emissions in the UK from 2014 to 2015 A. Wenger et al. 10.5194/acp-19-14057-2019
- A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions M. Vojta et al. 10.5194/gmd-15-8295-2022
- Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network E. White et al. 10.5194/acp-19-4345-2019
- Quantifying fossil fuel methane emissions using observations of atmospheric ethane and an uncertain emission ratio A. Ramsden et al. 10.5194/acp-22-3911-2022
- A pragmatic protocol for characterising errors in atmospheric inversions of methane emissions over Europe B. Szénási et al. 10.1080/16000889.2021.1914989
- Rising Perfluorocyclobutane (PFC-318, c-C4F8) Emissions in China from 2011 to 2020 Inferred from Atmospheric Observations Y. Wang et al. 10.1021/acs.est.3c10325
- A decline in emissions of CFC-11 and related chemicals from eastern China S. Park et al. 10.1038/s41586-021-03277-w
- Emissions of halocarbons from India inferred through atmospheric measurements D. Say et al. 10.5194/acp-19-9865-2019
- Atmospheric HCFC-22, HFC-125, and HFC-152a at Cape Point, South Africa B. Kuyper et al. 10.1021/acs.est.9b01612
- Fundamentals of data assimilation applied to biogeochemistry P. Rayner et al. 10.5194/acp-19-13911-2019
- Rapid increase in dichloromethane emissions from China inferred through atmospheric observations M. An et al. 10.1038/s41467-021-27592-y
- Atmospheric oxygen as a tracer for fossil fuel carbon dioxide: a sensitivity study in the UK H. Chawner et al. 10.5194/acp-24-4231-2024
- Sustained growth of sulfur hexafluoride emissions in China inferred from atmospheric observations M. An et al. 10.1038/s41467-024-46084-3
- Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides Y. Liu et al. 10.1002/qj.3138
- A measurement-based verification framework for UK greenhouse gas emissions: an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project P. Palmer et al. 10.5194/acp-18-11753-2018
- Development of an urban greenhouse gas modelling system to support a London monitoring network T. Hoare et al. 10.1002/wea.3795
Saved (preprint)
Latest update: 20 Nov 2024
Short summary
Bayesian inversions can be used to estimate emissions of gases from atmospheric data. We present an inversion framework that objectively defines the basis functions, which describe regions of emissions. The framework allows for the uncertainty in the choice of basis functions to be propagated through to the posterior emissions distribution in a single-step process, and provides an alternative to using a single set of basis functions.
Bayesian inversions can be used to estimate emissions of gases from atmospheric data. We present...
