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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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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.

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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.
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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...
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