FLEXINVERT: an atmospheric Bayesian inversion framework for determining surface fluxes of trace species using an optimized grid

Abstract

Abstract. We present a new modular Bayesian inversion framework, called FLEXINVERT, for estimating the surface fluxes of atmospheric trace species. FLEXINVERT can be applied to determine the spatio-temporal flux distribution of any species for which the atmospheric loss (if any) can be described as a linear process and can be used on continental to regional and even local scales with little or no modification. The relationship between changes in atmospheric mixing ratios and fluxes (the so-called source-receptor relationship) is described by a Lagrangian Particle Dispersion Model (LPDM) run in a backwards-in-time mode. In this study, we use FLEXPART but any LPDM could be used. The framework determines the fluxes on a nested grid of variable resolution, which is optimized based on the source-receptor relationships for the given observation network. Background mixing ratios are determined by coupling FLEXPART to the output of a global Eulerian model (or alternatively, from the observations themselves) and are also optionally optimized in the inversion. Spatial and temporal error correlations in the fluxes are taken into account using a simple model of exponential decay with space and time and, additionally, the aggregation error from the variable grid is accounted for. To demonstrate the use of FLEXINVERT, we present one case study in which methane fluxes are estimated in Europe in 2011 and compare the results to those of an independent inversion ensemble.

Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

“…The second advantage can make inverse modelling based on LPDM output even more attractive (see e.g. Stohl et al, 2009;Thompson and Stohl, 2014), as the Lagrangian models can accurately and efficiently be run backward from point measurements.…”

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

“…Not surprisingly, LS models are popular tools for such studies (e.g. Gerbig et al, 2003;Thompson and Stohl, 2014;Henne et al, 2016).…”

Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation

“…This approach utilizes the accurate transport of the LPDM to calculate the signal near to the receptors, and efficient calculation of background responses using the adjoint of the Eulerian global transport model. In contrast to previous works (Rödenbeck et al, 2009;Rigby et al, 2011;Thompson and Stohl, 2014), in which the regional models were coupled at the spatial boundary of the domain, we implemented a coupling at a time boundary in the global model domain (as described in Sect. 2.1).…”

“…Coupling can be performed in several ways; e.g., a regional-scale LPDM can be coupled to a global Eulerian model at a regional domain boundary (Rödenbeck et al, 2009;Rigby et al, 2011), or a global-scale LPDM can be coupled to an Eulerian model at the time boundary (Koyama et al, 2011;Thompson and Stohl, 2014).…”