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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-185
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-185
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 20 Sep 2019

Submitted as: development and technical paper | 20 Sep 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Geostatistical inverse modeling with very large datasets: an example from the OCO-2 satellite

Scot M. Miller1, Arvind K. Saibaba2, Michael E. Trudeau3, Marikate E. Mountain4, and Arlyn E. Andrews3 Scot M. Miller et al.
  • 1Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA
  • 2Department of Mathematics, North Carolina State University, Raleigh, NC, USA
  • 3Global Monitoring Division, National Oceanic and Atmospheric Administration, Boulder, CO, USA
  • 4Atmospheric and Environmental Research, Inc., Lexington, MA, USA

Abstract. Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly-used approaches because they do not require a single emissions inventory or a bottom-up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4 × 106 unknown fluxes using 9.9 × 104 observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximation are typically an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Scot M. Miller et al.
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Data sets

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

Model code and software

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

Scot M. Miller et al.
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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.
New observations of greenhouse gases from satellites and aircraft provide an unprecedented...
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