Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 14
Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5547-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5547-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 14
Development and technical paper
 | 
20 Jul 2022
Development and technical paper |  | 20 Jul 2022

Computationally efficient methods for large-scale atmospheric inverse modeling

Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba

Data sets

Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2 (1.0) Scot M. Miller, Arvind K. Saibaba, Michael E. Trudeau, Arlyn E. Andrews, Thomas Nehrkorn, and Marikate E. Mountain https://doi.org/10.5281/zenodo.3241467

Model code and software

Efficient methods for large-scale atmospheric inverse modeling Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba https://doi.org/10.5281/zenodo.5772660

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Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
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