Publication details
Event: EGU General Assembly (Vienna & online)
Date: May 2nd 2026 – May 7th 2026
Doi: doi.org/10.5194/egusphere-egu26-11347
Arkiv: hdl.handle.net/11250/5529724
Archive: nva.sikt.no/registration/019ed9eda046-5649d14f-3c20-471a-b3e7-f877dc46f62b
Summary:
Accurate quantification of atmospheric pollutant emissions is essential for evaluating the consequences of environmental incidents. Inverse modelling of such releases commonly employs a linear framework based on a source–receptor sensitivity (SRS) matrix; however, this matrix can be substantially biased or may even fail to represent the true scale of the release. We introduce a method in which the SRS matrix is corrected jointly with the inversion, resulting in a nonlinear inverse problem. The SRS discrepancies are interpreted as small shifts of observation points, leading to a deformation of the sensitivity field. The shifts are regularized through a Gaussian process prior, which imposes smoothness and sparsity while allowing inference at unobserved locations. The resulting posterior predictions of the shift field offer a practical tool for hyperparameter selection: the inferred shifts can be visualized geographically and evaluated by domain experts. This leads to a Bayesian framework that integrates inversion, SRS correction, and a tuning strategy based on L-curve-type diagnostics combined with maps of the predicted shifts. It will be demonstrated on a selected real continental-scale scenario of an atmospheric release. This research has been supported by the Czech Science Foundation (grant no. GA24-10400S). FLEXPART model simulations are cross-atmospheric research infrastructure services provided by ATMO-ACCESS (EU grant agreement No 101008004). Nikolaos Evangeliou was funded by the same EU grant. The computations were performed on resources provided by Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.