Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 7
Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4683-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4683-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 7
Development and technical paper
 | 
29 Jul 2021
Development and technical paper |  | 29 Jul 2021

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

Supplement

https://doi.org/10.5194/gmd-14-4683-2021-supplement

Data sets

Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2 Scot M. Miller, Arvind K. Saibaba, Michael E. Trudeau, Arlyn E. Andrews, Thomas Nehrkorn, and Marikate E. Mountain https://doi.org/10.5281/zenodo.3241466

Model code and software

Data reduction for large atmospheric satellite datasets Xiaoling Liu, Scot M. Miller, and August Weinbren https://doi.org/10.5281/zenodo.3899307

Geostatistical inverse modeling with large atmospheric datasets Scot Miller and Arvind K. Saibaba https://doi.org/10.5281/zenodo.3241524

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Short summary
Observations of greenhouse gases have become far more numerous in recent years due to new satellite observations. The sheer size of these datasets makes it challenging to incorporate these data into statistical models and use these data to estimate greenhouse gas sources and sinks. In this paper, we develop an approach to reduce the size of these datasets while preserving the most information possible. We subsequently test this approach using satellite observations of carbon dioxide.
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