Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 9, issue 9
Geosci. Model Dev., 9, 3213–3229, 2016
https://doi.org/10.5194/gmd-9-3213-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 3213–3229, 2016
https://doi.org/10.5194/gmd-9-3213-2016
© Author(s) 2016. This work is distributed under
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

Mark F. Lunt et al.
Related authors  
UK greenhouse gas measurements at two new tall towers for aiding emissions verification
Ann R. Stavert, Simon O'Doherty, Kieran Stanley, Dickon Young, Alistair J. Manning, Mark F. Lunt, Christopher Rennick, and Tim Arnold
Atmos. Meas. Tech., 12, 4495–4518, https://doi.org/10.5194/amt-12-4495-2019,https://doi.org/10.5194/amt-12-4495-2019, 2019
Short summary
Emissions of halocarbons from India inferred through atmospheric measurements
Daniel Say, Anita L. Ganesan, Mark F. Lunt, Matthew Rigby, Simon O'Doherty, Christina Harth, Alistair J. Manning, Paul B. Krummel, and Stephane Bauguitte
Atmos. Chem. Phys., 19, 9865–9885, https://doi.org/10.5194/acp-19-9865-2019,https://doi.org/10.5194/acp-19-9865-2019, 2019
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. Discuss., https://doi.org/10.5194/acp-2019-477,https://doi.org/10.5194/acp-2019-477, 2019
Revised manuscript accepted for ACP
Short summary
Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network
Emily D. White, Matthew Rigby, Mark F. Lunt, T. Luke Smallman, Edward Comyn-Platt, Alistair J. Manning, Anita L. Ganesan, Simon O'Doherty, Ann R. Stavert, Kieran Stanley, Mathew Williams, Peter Levy, Michel Ramonet, Grant L. Forster, Andrew C. Manning, and Paul I. Palmer
Atmos. Chem. Phys., 19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019,https://doi.org/10.5194/acp-19-4345-2019, 2019
Short summary
Atmospheric observations and emission estimates of ozone-depleting chlorocarbons from India
Daniel Say, Anita L. Ganesan, Mark F. Lunt, Matthew Rigby, Simon O'Doherty, Chris Harth, Alistair J. Manning, Paul B. Krummel, and Stephane Bauguitte
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1287,https://doi.org/10.5194/acp-2018-1287, 2019
Short summary
Related subject area  
Atmospheric Sciences
Simulating lightning NO production in CMAQv5.2: performance evaluations
Daiwen Kang, Kristen M. Foley, Rohit Mathur, Shawn J. Roselle, Kenneth E. Pickering, and Dale J. Allen
Geosci. Model Dev., 12, 4409–4424, https://doi.org/10.5194/gmd-12-4409-2019,https://doi.org/10.5194/gmd-12-4409-2019, 2019
Short summary
A Lagrangian convective transport scheme including a simulation of the time air parcels spend in updrafts (LaConTra v1.0)
Ingo Wohltmann, Ralph Lehmann, Georg A. Gottwald, Karsten Peters, Alain Protat, Valentin Louf, Christopher Williams, Wuhu Feng, and Markus Rex
Geosci. Model Dev., 12, 4387–4407, https://doi.org/10.5194/gmd-12-4387-2019,https://doi.org/10.5194/gmd-12-4387-2019, 2019
Short summary
Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation
Takuro Michibata, Kentaroh Suzuki, Tomoo Ogura, and Xianwen Jing
Geosci. Model Dev., 12, 4297–4307, https://doi.org/10.5194/gmd-12-4297-2019,https://doi.org/10.5194/gmd-12-4297-2019, 2019
Short summary
Development of turbulent scheme in the FLEXPART-AROME v1.2.1 Lagrangian particle dispersion model
Bert Verreyken, Jérome Brioude, and Stéphanie Evan
Geosci. Model Dev., 12, 4245–4259, https://doi.org/10.5194/gmd-12-4245-2019,https://doi.org/10.5194/gmd-12-4245-2019, 2019
Short summary
Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)
Seppo Pulkkinen, Daniele Nerini, Andrés A. Pérez Hortal, Carlos Velasco-Forero, Alan Seed, Urs Germann, and Loris Foresti
Geosci. Model Dev., 12, 4185–4219, https://doi.org/10.5194/gmd-12-4185-2019,https://doi.org/10.5194/gmd-12-4185-2019, 2019
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, https://doi.org/10.5194/acp-13-7115-2013, 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, https://doi.org/10.5194/gmd-8-1525-2015, 2015.
Bocquet, M.: Toward Optimal Choices of Control Space Representation for Geophysical Data Assimilation, Mon. Weather Rev., 137, 2331–2348, https://doi.org/10.1175/2009MWR2789.1, 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, https://doi.org/10.1002/qj.837, 2011.
Bodin, T. and Sambridge, M.: Seismic tomography with the reversible jump algorithm, Geophys. J. Int., 178, 1411–1436, https://doi.org/10.1111/j.1365-246X.2009.04226.x, 2009.
Publications Copernicus
Download
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...
Citation