@misc{9246827,
  abstract     = {{Accurate estimations of regional CO2 fluxes are essential for understanding the global carbon cycle and further mitigating climate change. Satellite-based retrievals of column-averaged carbon dioxide (CO2) from the Orbiting Carbon Observatory 2 (OCO-2) offer a promising avenue for estimating carbon fluxes with atmospheric inversion systems, as they provide higher spatial resolution and observational density than traditional in situ networks. However, the large volume of satellite observations presents the challenge of how the data can be processed in order to make simulations computationally feasible.
In this study, total-column CO2 retrievals from OCO-2 were used to simulate total-column footprints with the Lagrangian FLEXible Particle Dispersion Model (FLEXPART) v.11. To reduce computational costs, an adaptive grid cell averaging scheme was applied to the satellite observations prior to the footprint calculations. The precomputed footprints were then used to assimilate OCO-2 observations for January and August 2022 with the Lund University Modular Inversion Algorithm (LUMIA). The resulting posterior fluxes were validated against ground-based observations from the Integrated Carbon Observation System (ICOS), non-ICOS sites contained in the Observation Package (ObsPack) dataset and the Total Column Carbon Observatory Network (TCCON).

The results show that the OCO-2-derived posterior fluxes reduced both mean biases and root mean square error (RMSE) in simulated CO2 concentrations at many validation sites across Europe. The inversion performance was notably better in August than in January, reflecting the stronger observational constraint associated with the higher density and spatial coverage of OCO-2 observations during summer. Sensitivity experiments investigating the temporal and spatial prior error covariance parameters indicated that a temporal correlation length of 30 days and a spatial correlation length of 100 km yielded the most effective configuration for OCO-2 observations, in terms of reducing posterior mean biases at the validation sites.}},
  author       = {{Rissbacher, Johanna}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Simulation of Total-Column Footprints with FLEXPART 11 for a Regional Inverse Application in the Lund University Modular Inversion Algorithm (LUMIA)}},
  year         = {{2026}},
}