Articles | Volume 24, issue 21
https://doi.org/10.5194/acp-24-12447-2024
https://doi.org/10.5194/acp-24-12447-2024
Research article
 | 
11 Nov 2024
Research article |  | 11 Nov 2024

Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother

Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner

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Cited articles

Berchet, A., Pison, I., Chevallier, F., Bousquet, P., Bonne, J.-L., and Paris, J.-D.: Objectified quantification of uncertainties in Bayesian atmospheric inversions, Geosci. Model Dev., 8, 1525–1546, https://doi.org/10.5194/gmd-8-1525-2015, 2015. a
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Short summary
Atmospheric greenhouse gas inversions have great potential to independently check reported bottom-up emissions; however they are subject to large uncertainties. It is paramount to address and reduce the largest source of uncertainty, which stems from the representation of atmospheric transport in the models. In this study, we show that the use of a temporally varying flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation in an idealized experiment.
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