Articles | Volume 10, issue 10
Geosci. Model Dev., 10, 3695–3713, 2017
https://doi.org/10.5194/gmd-10-3695-2017
Geosci. Model Dev., 10, 3695–3713, 2017
https://doi.org/10.5194/gmd-10-3695-2017
Methods for assessment of models
10 Oct 2017
Methods for assessment of models | 10 Oct 2017

Atmospheric inverse modeling via sparse reconstruction

Nils Hase et al.

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Cited articles

Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014.
Andrieu, C., de Freitas, N., Doucet, A., and Jordan, M. I.: An Introduction to MCMC for Machine Learning, Mach. Learn., 50, 5–43, https://doi.org/10.1023/A:1020281327116, 2003.
Banks, H., Holm, K., and Robbins, D.: Standard error computations for uncertainty quantification in inverse problems: Asymptotic theory vs. bootstrapping, Math. Comput. Model., 52, 1610–1625, https://doi.org/10.1016/j.mcm.2010.06.026, 2010.
Beck, A. and Teboulle, M.: A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM J. Img. Sci., 2, 183–202, https://doi.org/10.1137/080716542, 2009.
Biraud, S. C., Torn, M. S., Smith, J. R., Sweeney, C., Riley, W. J., and Tans, P. P.: A multi-year record of airborne CO2 observations in the US Southern Great Plains, Atmos. Meas. Tech., 6, 751–763, https://doi.org/10.5194/amt-6-751-2013, 2013.
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Short summary
Inverse modeling uses atmospheric measurements to estimate emissions of greenhouse gases, which are key to understand the climate system. However, the measurement information alone is typically insufficient to provide reasonable emission estimates. Additional information is required. This article applies modern mathematical inversion techniques to formulate such additional knowledge. It is a prime example of how such tools can improve the quality of estimates compared to commonly used methods.