Efficient use of a Lagrangian Particle Dispersion Model for atmospheric inversions using satellite observations of column mixing ratios

Abstract. Satellite instruments for measuring atmospheric column mixing ratios have improved significantly over the past couple of decades with increases in pixel resolution and accuracy. As a result, satellite observations are being increasingly used in atmospheric inversions to improve estimates of emissions of greenhouse gases (GHGs), particularly CO₂ and CH₄, and to constrain regional and national emission budgets. However, in order to make use of the increasing resolution in inversions, the atmospheric transport models used need to be able to represent the observations at these finer resolutions. Here, we present a new and computationally efficient methodology to model satellite column average mixing ratios with a Lagrangian Particle Dispersion Model (LPDM) and calculate the Jacobian matrices describing the relationship between surface fluxes of GHGs and atmospheric column average mixing ratios, as needed in inversions. We present a case study using this methodology in the LMPD, FLEXPART, and the inversion framework, FLEXINVERT, to estimate CH₄ fluxes over Siberia using column average mixing ratios of CH₄ (XCH₄) from the TROPOMI instrument onboard the Sentinel-5P satellite. The results of the inversion using TROPOMI XCH₄ are evaluated against results using ground-based observations.