Articles | Volume 9, issue 9
Geosci. Model Dev., 9, 3213–3229, 2016
Geosci. Model Dev., 9, 3213–3229, 2016

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

Mark F. Lunt et al.

Related authors

Evaluation of Wetland CH4 in the JULES Land Surface Model Using Satellite Observations
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences Discuss.,,, 2022
Preprint under review for BG
Short summary
Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020)
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276,,, 2021
Short summary
Understanding the influence of combustion on atmospheric CO2 over Europe by using satellite observations of CO2 and reactive trace gases
Mehliyar Sadiq, Paul I. Palmer, Mark F. Lunt, Liang Feng, Ingrid Super, Stijn N. C. Dellaert, and Hugo A. C. Denier van der Gon
Atmos. Chem. Phys. Discuss.,,, 2021
Preprint under review for ACP
Short summary
An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data
Mark F. Lunt, Paul I. Palmer, Liang Feng, Christopher M. Taylor, Hartmut Boesch, and Robert J. Parker
Atmos. Chem. Phys., 19, 14721–14740,,, 2019
Short summary
Atmospheric radiocarbon measurements to quantify CO2 emissions in the UK from 2014 to 2015
Angelina Wenger, Katherine Pugsley, Simon O'Doherty, Matt Rigby, Alistair J. Manning, Mark F. Lunt, and Emily D. White
Atmos. Chem. Phys., 19, 14057–14070,,, 2019
Short summary

Related subject area

Atmospheric sciences
Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators
Prabhakar Shrestha, Jana Mendrok, Velibor Pejcic, Silke Trömel, Ulrich Blahak, and Jacob T. Carlin
Geosci. Model Dev., 15, 291–313,,, 2022
Short summary
Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume
Mahtab Majdzadeh, Craig A. Stroud, Christopher Sioris, Paul A. Makar, Ayodeji Akingunola, Chris McLinden, Xiaoyi Zhao, Michael D. Moran, Ihab Abboud, and Jack Chen
Geosci. Model Dev., 15, 219–249,,, 2022
Short summary
The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)
Xiaodong Wang, Chun Zhao, Mingyue Xu, Qiuyan Du, Jianqiu Zheng, Yun Bi, Shengfu Lin, and Yali Luo
Geosci. Model Dev., 15, 199–218,,, 2022
Short summary
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
Geosci. Model Dev., 15, 45–73,,, 2022
Short summary
Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS
Galina Wind, Arlindo M. da Silva, Kerry G. Meyer, Steven Platnick, and Peter M. Norris
Geosci. Model Dev., 15, 1–14,,, 2022
Short summary

Cited articles

Berchet, A., Pison, I., Chevallier, F., Bousquet, P., Conil, S., Geever, M., Laurila, T., Lavric, J., Lopez, M., Moncrieff, J., Necki, J., Ramonet, M., Schmidt, M., Steinbacher, M., and Tarniewicz, J.: Towards better error statistics for atmospheric inversions of methane surface fluxes, Atmos. Chem. Phys., 13, 7115–7132,, 2013.
Berchet, A., Pison, I., Chevallier, F., Bousquet, P., Bonne, J.-L., and Paris, J.-D.: Objectified quantification of uncertainties in Bayesian atmospheric inversions, Geosci. Model Dev., 8, 1525–1546,, 2015.
Bocquet, M.: Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation, Mon. Weather Rev., 137, 2331–2348,, 2009.
Bocquet, M., Wu, L., and Chevallier, F.: Bayesian design of control space for optimal assimilation of observations. Part I: Consistent multiscale formalism, Q. J. Roy. Meteor. Soc., 137, 1340–1356,, 2011.
Bodin, T. and Sambridge, M.: Seismic tomography with the reversible jump algorithm, Geophys. J. Int., 178, 1411–1436,, 2009.
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.