{"id":114901,"date":"2026-09-11T15:09:09","date_gmt":"2026-09-11T13:09:09","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/nonlinear-atmospheric-inversion-with-interpretable-bias-correction-via-gaussian-process-prior\/"},"modified":"2026-09-11T15:09:09","modified_gmt":"2026-09-11T13:09:09","slug":"nonlinear-atmospheric-inversion-with-interpretable-bias-correction-via-gaussian-process-prior","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/nonlinear-atmospheric-inversion-with-interpretable-bias-correction-via-gaussian-process-prior\/","title":{"rendered":"Nonlinear Atmospheric Inversion with Interpretable Bias Correction via Gaussian Process Prior"},"content":{"rendered":"<p class=\"wp-block-paragraph\">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\u2013receptor 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.\u00a0This 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 &#8211; the National Infrastructure for High Performance Computing and Data Storage in Norway.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 13:56:36","footnotes":""},"nva_tax_category":[1093],"class_list":["post-114901","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-poster"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/114901","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication"}],"about":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/types\/nva_publication"}],"version-history":[{"count":0,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/114901\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=114901"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=114901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}