Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8853-2024
https://doi.org/10.5194/gmd-17-8853-2024
Development and technical paper
 | 
12 Dec 2024
Development and technical paper |  | 12 Dec 2024

A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)

Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba

Related authors

GenGHG v1.0: A generative machine learning emulator of greenhouse-gas atmospheric transport around global emission hotspots
Zeyu Wang, Jieyi Wang, Hanyu Liu, Ziting Huang, Kewei Liang, Feng Zhang, and Scot M. Miller
EGUsphere, https://doi.org/10.5194/egusphere-2026-4304,https://doi.org/10.5194/egusphere-2026-4304, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Hanyu Liu, Misa Ishizawa, Felix R. Vogel, Zhen Zhang, Benjamin Poulter, Leyang Feng, Ao Chen, Anna L. Gagné-Landmann, Deborah N. Huntzinger, Joe R. Melton, Vineet Yadav, Dylan C. Gaeta, Ziting Huang, Douglas E. J. Worthy, Douglas Chan, and Scot M. Miller
Atmos. Chem. Phys., 26, 1229–1247, https://doi.org/10.5194/acp-26-1229-2026,https://doi.org/10.5194/acp-26-1229-2026, 2026
Short summary
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023,https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Computationally efficient methods for large-scale atmospheric inverse modeling
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022,https://doi.org/10.5194/gmd-15-5547-2022, 2022
Short summary

Cited articles

Bauer, F. and Lukas, M. A.: Comparing parameter choice methods for regularization of ill-posed problems, Math. Comput. Simulat., 81, 1795–1841, https://doi.org/10.1016/j.matcom.2011.01.016, 2011. a
Beck, A. and Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Imaging Sci., 2, 183–202, https://doi.org/10.1137/080716542, 2009. a
Björck, Å.: Numerical methods for least squares problems, SIAM, ISBN 978-0-89871-360-2, https://doi.org/10.1137/1.9781611971484, 1996. a
Bozdogan, H.: Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions, Psychometrika, 52, 345–370, 1987. a
Brasseur, G. P. and Jacob, D. J.: Inverse Modeling for Atmospheric Chemistry, 487–537, Cambridge University Press, https://doi.org/10.1017/9781316544754.012, 2017. a, b, c
Download
Short summary
Making an informed decision about what prior information to incorporate or discard in an inverse model is important yet very challenging, as it is often not straightforward to distinguish between informative and non-informative variables. In this study, we develop a new approach for incorporating prior information in an inverse model using predictor variables, while simultaneously selecting the relevant predictor variables for the estimation of the unknown quantity of interest. 
Share