Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1771-2020
https://doi.org/10.5194/gmd-13-1771-2020
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

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Cited articles

Ambikasaran, S., Li, J. Y., Kitanidis, P. K., and Darve, E.: Large-scale stochastic linear inversion using hierarchical matrices, Computat. Geosci., 17, 913–927, https://doi.org/10.1007/s10596-013-9364-0, 2013a. a, b
Ambikasaran, S., Saibaba, A. K., Darve, E. F., and Kitanidis, P. K.: Fast Algorithms for Bayesian Inversion, in: Computational Challenges in the Geosciences, edited by: Dawson, C. and Gerritsen, M., 101–142, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4614-7434-0_5, 2013b. a, b
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus B, 58, 359–365, https://doi.org/10.1111/j.1600-0889.2006.00218.x, 2006. a, b, c, d
Barrett, R., Berry, M., Chan, T., Demmel, J., Donato, J., Dongarra, J., Eijkhout, V., Pozo, R., Romine, C., and van der Vorst, H.: Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, Other Titles in Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1994. a
Bousserez, N., Henze, D. K., Perkins, A., Bowman, K. W., Lee, M., Liu, J., Deng, F., and Jones, D. B. A.: Improved analysis-error covariance matrix for high-dimensional variational inversions: application to source estimation using a 3D atmospheric transport model, Q. J. Roy. Meteorol. Soc., 141, 1906–1921, https://doi.org/10.1002/qj.2495, 2015. a
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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.