Articles | Volume 8, issue 4
Geosci. Model Dev., 8, 1259–1273, 2015
Geosci. Model Dev., 8, 1259–1273, 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.

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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,, 2012.
Babacan, S. D., Molina, R., and Katsaggelos, A. K.: Bayesian compressive sensing using Laplace priors, IEEE T. Signal Proces., 19, 55–63,, 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.
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.