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

Methane fluxes from arctic & boreal North America: Comparisons between process-based estimates and atmospheric observations
Hanyu Liu, Felix R. Vogel, Misa Ishizawa, Zhen Zhang, Benjamin Poulter, Doug E. J. Worthy, Leyang Feng, Anna L. Gagné-Landmann, Ao Chen, Ziting Huang, Dylan C. Gaeta, Joe R. Melton, Douglas Chan, Vineet Yadav, Deborah Huntzinger, and Scot M. Miller
EGUsphere, https://doi.org/10.5194/egusphere-2025-2150,https://doi.org/10.5194/egusphere-2025-2150, 2025
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
Data reduction for inverse modeling: an adaptive approach v1.0
Xiaoling Liu, August L. Weinbren, He Chang, Jovan M. Tadić, Marikate E. Mountain, Michael E. Trudeau, Arlyn E. Andrews, Zichong Chen, and Scot M. Miller
Geosci. Model Dev., 14, 4683–4696, https://doi.org/10.5194/gmd-14-4683-2021,https://doi.org/10.5194/gmd-14-4683-2021, 2021
Short summary
Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021,https://doi.org/10.5194/acp-21-6663-2021, 2021
Short summary

Related subject area

Numerical methods
Optimized step size control within the Rosenbrock solvers for stiff chemical ordinary differential equation systems in KPP version 2.2.3_rs4
Raphael Dreger, Timo Kirfel, Andrea Pozzer, Simon Rosanka, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev., 18, 4273–4291, https://doi.org/10.5194/gmd-18-4273-2025,https://doi.org/10.5194/gmd-18-4273-2025, 2025
Short summary
Potential-based thermodynamics with consistent conservative cascade transport for implicit large eddy simulation: PTerodaC3TILES version 1.0
John Thuburn
Geosci. Model Dev., 18, 3331–3357, https://doi.org/10.5194/gmd-18-3331-2025,https://doi.org/10.5194/gmd-18-3331-2025, 2025
Short summary
Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025,https://doi.org/10.5194/gmd-18-2891-2025, 2025
Short summary
Numerical simulations of ocean surface waves along the Australian coast with a focus on the Great Barrier Reef
Xianghui Dong, Qingxiang Liu, Stefan Zieger, Alberto Alberello, Ali Abdolali, Jian Sun, Kejian Wu, and Alexander V. Babanin
EGUsphere, https://doi.org/10.5194/egusphere-2025-698,https://doi.org/10.5194/egusphere-2025-698, 2025
Short summary
CLAQC v1.0 – Country Level Air Quality Calculator: an empirical modeling approach
Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Schulz-Antipa
Geosci. Model Dev., 18, 2373–2408, https://doi.org/10.5194/gmd-18-2373-2025,https://doi.org/10.5194/gmd-18-2373-2025, 2025
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