Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5219-2023
https://doi.org/10.5194/gmd-16-5219-2023
Methods for assessment of models
 | 
08 Sep 2023
Methods for assessment of models |  | 08 Sep 2023

Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion

Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller

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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024,https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Quantification of fossil fuel CO2 from combined CO, δ13CO2 and Δ14CO2 observations
Jinsol Kim, John B. Miller, Charles E. Miller, Scott J. Lehman, Sylvia E. Michel, Vineet Yadav, Nick E. Rollins, and William M. Berelson
Atmos. Chem. Phys., 23, 14425–14436, https://doi.org/10.5194/acp-23-14425-2023,https://doi.org/10.5194/acp-23-14425-2023, 2023
Short summary
A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)
Dien Wu, John C. Lin, Henrique F. Duarte, Vineet Yadav, Nicholas C. Parazoo, Tomohiro Oda, and Eric A. Kort
Geosci. Model Dev., 14, 3633–3661, https://doi.org/10.5194/gmd-14-3633-2021,https://doi.org/10.5194/gmd-14-3633-2021, 2021
Short summary
Plant responses to volcanically elevated CO2 in two Costa Rican forests
Robert R. Bogue, Florian M. Schwandner, Joshua B. Fisher, Ryan Pavlick, Troy S. Magney, Caroline A. Famiglietti, Kerry Cawse-Nicholson, Vineet Yadav, Justin P. Linick, Gretchen B. North, and Eliecer Duarte
Biogeosciences, 16, 1343–1360, https://doi.org/10.5194/bg-16-1343-2019,https://doi.org/10.5194/bg-16-1343-2019, 2019
Short summary

Related subject area

Atmospheric sciences
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025,https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary

Cited articles

Berk, R., Brown, L., Buja, A., Zhang, K., and Zhao, L.: Valid post-selection inference, Ann. Stat., 41, 802–837, 2013. a
Bouchard, M., Jousselme, A.-L., and Doré, P.-E.: A proof for the positive definiteness of the Jaccard index matrix, Int. J. Approx. Reason., 54, 615–626, 2013. a
Brasseur, G. P. and Jacob, D. J.: Modeling of atmospheric chemistry, Cambridge University Press, https://doi.org/10.1017/9781316544754, 2017. a, b, c
Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions, City, 1, p. 1, 2007. a
Conley, S., Franco, G., Faloona, I., Blake, D. R., Peischl, J., and Ryerson, T.: Methane emissions from the 2015 Aliso Canyon blowout in Los Angeles, CA, Science, 351, 1317–1320, 2016. a
Download
Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Share