Geostatistical inverse modeling with very large datasets:  an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite

Miller, Scot M.; Saibaba, Arvind K.; Trudeau, Michael E.; Mountain, Marikate E.; Andrews, Arlyn E.

doi:https://doi.org/10.5194/gmd-13-1771-2020

Articles | Volume 13, issue 3

https://doi.org/10.5194/gmd-13-1771-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-1771-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 3

Development and technical paper

|

02 Apr 2020

Development and technical paper |

| 02 Apr 2020

Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite

Scot M. Miller, Arvind K. Saibaba, Michael E. Trudeau, Marikate E. Mountain, and Arlyn E. Andrews

Supplement

https://doi.org/10.5194/gmd-13-1771-2020-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, Marikate E. Mountain, and Arlyn E. Andrews https://doi.org/10.5281/zenodo.3241466

Model code and software

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

Short summary

New observations of greenhouse gases from satellites and aircraft provide an unprecedented window into global carbon sources and sinks. However, these new datasets also present enormous computational challenges due to the sheer number of observations. In this article, we discuss the challenges of estimating greenhouse gas source and sinks using very large atmospheric datasets and evaluate several strategies for overcoming these challenges.