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 10, issue 10
Geosci. Model Dev., 10, 3695-3713, 2017
https://doi.org/10.5194/gmd-10-3695-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 3695-3713, 2017
https://doi.org/10.5194/gmd-10-3695-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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.
Related authors  
A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor
Oliver Schneising, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, John P. Burrows, Tobias Borsdorff, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Christian Hermans, Laura T. Iraci, Rigel Kivi, Jochen Landgraf, Isamu Morino, Justus Notholt, Christof Petri, David F. Pollard, Sébastien Roche, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Voltaire A. Velazco, Thorsten Warneke, and Debra Wunch
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-243,https://doi.org/10.5194/amt-2019-243, 2019
Manuscript under review for AMT
Short summary
Evaluation of MOPITT version 7 joint TIR-NIR XCO retrievals with TCCON
Jacob K. Hedelius, Tai-Long He, Dylan B. A. Jones, Rebecca R. Buchholz, Martine De Mazière, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Laura T. Iraci, Pascal Jeseck, Matthäus Kiel, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Sébastien Roche, Coleen M. Roehl, Matthias Schneider, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Colm Sweeney, Yao Té, Osamu Uchino, Voltaire A. Velazco, Wei Wang, Thorsten Warneke, Paul O. Wennberg, Helen M. Worden, and Debra Wunch
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-201,https://doi.org/10.5194/amt-2019-201, 2019
Manuscript under review for AMT
Short summary
Impacts of H2O variability on accuracy of CH4 observations from MIPAS satellite over tropics
Temesgen Yirdaw Berhe, Gizaw Mengistu Tsidu, Thomas Blumenstock, Frank Hase, Thomas von Clarmann, Justus Notholt, and Emmanuel Mahieu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-209,https://doi.org/10.5194/amt-2019-209, 2019
Manuscript under review for AMT
Short summary
Modelling CO2 weather – why horizontal resolution matters
Anna Agustí-Panareda, Michail Diamantakis, Sébastien Massart, Frédéric Chevallier, Joaquín Muñoz-Sabater, Jérôme Barré, Roger Curcoll, Richard Engelen, Bavo Langerock, Rachel M. Law, Zoë Loh, Josep Anton Morguí, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Coleen Roehl, Alex T. Vermeulen, Thorsten Warneke, and Debra Wunch
Atmos. Chem. Phys., 19, 7347-7376, https://doi.org/10.5194/acp-19-7347-2019,https://doi.org/10.5194/acp-19-7347-2019, 2019
Short summary
Retrieval of atmospheric CH4 vertical information from TCCON FTIR spectra
Minqiang Zhou, Bavo Langerock, Mahesh Kumar Sha, Nicolas Kumps, Christian Hermans, Christof Petri, Thorsten Warneke, Huilin Chen, Jean-Marc Metzger, Rigel Kivi, Pauli Heikkinen, Michel Ramonet, and Martine De Mazière
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-94,https://doi.org/10.5194/amt-2019-94, 2019
Manuscript under review for AMT
Short summary
Related subject area  
Atmospheric Sciences
Tropospheric mixing and parametrization of unresolved convective updrafts as implemented in the Chemical Lagrangian Model of the Stratosphere (CLaMS v2.0)
Paul Konopka, Mengchu Tao, Felix Ploeger, Mohamadou Diallo, and Martin Riese
Geosci. Model Dev., 12, 2441-2462, https://doi.org/10.5194/gmd-12-2441-2019,https://doi.org/10.5194/gmd-12-2441-2019, 2019
Short summary
Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.1) – Part 1: Chemical mechanism
Jean-François Müller, Trissevgeni Stavrakou, and Jozef Peeters
Geosci. Model Dev., 12, 2307-2356, https://doi.org/10.5194/gmd-12-2307-2019,https://doi.org/10.5194/gmd-12-2307-2019, 2019
Short summary
The Matsuno baroclinic wave test case
Ofer Shamir, Itamar Yacoby, Shlomi Ziskin Ziv, and Nathan Paldor
Geosci. Model Dev., 12, 2181-2193, https://doi.org/10.5194/gmd-12-2181-2019,https://doi.org/10.5194/gmd-12-2181-2019, 2019
Short summary
Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0
Kathryn M. Emmerson, Jeremy D. Silver, Edward Newbigin, Edwin R. Lampugnani, Cenk Suphioglu, Alan Wain, and Elizabeth Ebert
Geosci. Model Dev., 12, 2195-2214, https://doi.org/10.5194/gmd-12-2195-2019,https://doi.org/10.5194/gmd-12-2195-2019, 2019
Short summary
Convective response to large-scale forcing in the tropical western Pacific simulated by spCAM5 and CanAM4.3
Toni Mitovski, Jason N. S. Cole, Norman A. McFarlane, Knut von Salzen, and Guang J. Zhang
Geosci. Model Dev., 12, 2107-2117, https://doi.org/10.5194/gmd-12-2107-2019,https://doi.org/10.5194/gmd-12-2107-2019, 2019
Short summary
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.
Publications Copernicus
Download
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.
Inverse modeling uses atmospheric measurements to estimate emissions of greenhouse gases, which...
Citation