Articles | Volume 8, issue 4
Geosci. Model Dev., 8, 1259–1273, 2015
https://doi.org/10.5194/gmd-8-1259-2015
Geosci. Model Dev., 8, 1259–1273, 2015
https://doi.org/10.5194/gmd-8-1259-2015

Development and technical paper 29 Apr 2015

Development and technical paper | 29 Apr 2015

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

J. Ray et al.

Related authors

A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions
J. Ray, V. Yadav, A. M. Michalak, B. van Bloemen Waanders, and S. A. McKenna
Geosci. Model Dev., 7, 1901–1918, https://doi.org/10.5194/gmd-7-1901-2014,https://doi.org/10.5194/gmd-7-1901-2014, 2014

Related subject area

Numerical Methods
A note on precision-preserving compression of scientific data
Rostislav Kouznetsov
Geosci. Model Dev., 14, 377–389, https://doi.org/10.5194/gmd-14-377-2021,https://doi.org/10.5194/gmd-14-377-2021, 2021
Short summary
An N-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences – application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs
Bertrand Bessagnet, Laurent Menut, and Maxime Beauchamp
Geosci. Model Dev., 14, 91–106, https://doi.org/10.5194/gmd-14-91-2021,https://doi.org/10.5194/gmd-14-91-2021, 2021
Short summary
A framework to evaluate IMEX schemes for atmospheric models
Oksana Guba, Mark A. Taylor, Andrew M. Bradley, Peter A. Bosler, and Andrew Steyer
Geosci. Model Dev., 13, 6467–6480, https://doi.org/10.5194/gmd-13-6467-2020,https://doi.org/10.5194/gmd-13-6467-2020, 2020
Inequality-constrained free-surface evolution in a full Stokes ice flow model (evolve_glacier v1.1)
Anna Wirbel and Alexander Helmut Jarosch
Geosci. Model Dev., 13, 6425–6445, https://doi.org/10.5194/gmd-13-6425-2020,https://doi.org/10.5194/gmd-13-6425-2020, 2020
Short summary
A fast and efficient MATLAB-based MPM solver: fMPMM-solver v1.1
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 13, 6265–6284, https://doi.org/10.5194/gmd-13-6265-2020,https://doi.org/10.5194/gmd-13-6265-2020, 2020
Short summary

Cited articles

Andres, R. J., Boden, T. A., Bréon, F.-M., Ciais, P., Davis, S., Erickson, D., Gregg, J. S., Jacobson, A., Marland, G., Miller, J., Oda, T., Olivier, J. G. J., Raupach, M. R., Rayner, P., and Treanton, K.: A synthesis of carbon dioxide emissions from fossil-fuel combustion, Biogeosciences, 9, 1845–1871, https://doi.org/10.5194/bg-9-1845-2012, 2012.
Babacan, S. D., Molina, R., and Katsaggelos, A. K.: Bayesian compressive sensing using Laplace priors, IEEE T. Signal Proces., 19, 55–63, https://doi.org/10.1109/TIP.2009.2032894, 2010.
Baraniuk, R., Davenport, M., DeVore, R., and Wakin, M.: A simple proof of the restricted isometry property for random matrices, Constr. Approx., 28, 253–263, 2008.
Baraniuk, R., Cevher, V., Duarte, M., and Hegde, C.: Model-based compressive sensing, IEEE T. Inform. Theory, 56, 1982–2001, 2010.
Candes, E. and Tao, T.: Near optimal signal recovery from random projections: universal encoding strategies?, IEEE T. Inform. Theory, 52, 5406–5425, 2006.
Download
Short summary
The paper presents a statistical method (shrinkage) that can be used to estimate rough emission fields, e.g., fossil fuel CO2 emissions, from measurements of concentrations. This method is demonstrated in a test case where the emissions are modeled using wavelets. We find that the method can eliminate unnecessary complexity from the wavelet model, ensures non-negativity of the emissions, is computationally efficient and is, by construction, insensitive to prior guesses of the total emission.