Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation

Stanley, Michael; Kuusela, Mikael; Byrne, Brendan; Liu, Junjie

doi:https://doi.org/10.5194/acp-24-9419-2024

Articles | Volume 24, issue 16

https://doi.org/10.5194/acp-24-9419-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-24-9419-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 16

Technical note

|

28 Aug 2024

Technical note |

| 28 Aug 2024

Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation

Michael Stanley, Mikael Kuusela, Brendan Byrne, and Junjie Liu

Data sets

CarbonTracker CT2016 Peters et al. https://gml.noaa.gov/ccgg/carbontracker/

CO2 virtual science data environment Jet Propulsion Laboratory https://co2.jpl.nasa.gov/

GOSAT Data Archive Service National Institute for Environmental Studies https://data2.gosat.nies.go.jp

Model code and software

GEOS-Chem Adjoint D. Henze et al. http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_Adjoint

Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation I. Bey et al. http://wiki.seas.harvard.edu/geos-chem

Short summary

To serve the uncertainty quantification (UQ) needs of 4D-Var data assimilation (DA) practitioners, we describe and justify a UQ algorithm from carbon flux inversion and incorporate its sampling uncertainty into the final reported UQ. The algorithm is mathematically proved, and its performance is shown for a carbon flux observing system simulation experiment. These results legitimize and generalize this algorithm's current use and make available this effective algorithm to new DA domains.