A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions

Vojta, Martin; Plach, Andreas; Thompson, Rona L.; Stohl, Andreas

doi:https://doi.org/10.5194/gmd-15-8295-2022

Articles | Volume 15, issue 22

https://doi.org/10.5194/gmd-15-8295-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

The Lagrangian particle dispersion model FLEXPART

https://doi.org/10.5194/gmd-15-8295-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 22

Model evaluation paper

|

18 Nov 2022

Model evaluation paper |

| 18 Nov 2022

A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions

Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl

Supplement

https://doi.org/10.5194/gmd-15-8295-2022-supplement

Data sets

Multi-laboratory compilation of atmospheric sulfure hexafluoride data for the period 1983-2017; obspack_sf6_1_v2.1.1_2018-08-17 NOAA Carbon Cycle Group ObsPack Team https://doi.org/10.25925/20180817

Model code and software

Supplementary for Vojta et al., 2022, GMD Martin Vojta https://doi.org/10.25365/phaidra.339

FLEXPART 8-CTM-1.1: Atmospheric Lagrangian Particle Dispersion Model for global tracer transport (8-CTM-1.1) Stephan Henne, Dominik Brunner, Christine Groot Zwaaftink, and Andreas Stohl https://doi.org/10.5281/zenodo.1249190

Short summary

In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.