{"id":114800,"date":"2026-09-11T15:09:02","date_gmt":"2026-09-11T13:09:02","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/intuitively-tuned-elastic-bias-correction-of-atmospheric-inversion-using-gaussian-process-prior-application-to-accidental-radioactive-emissions\/"},"modified":"2026-09-11T15:09:02","modified_gmt":"2026-09-11T13:09:02","slug":"intuitively-tuned-elastic-bias-correction-of-atmospheric-inversion-using-gaussian-process-prior-application-to-accidental-radioactive-emissions","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/intuitively-tuned-elastic-bias-correction-of-atmospheric-inversion-using-gaussian-process-prior-application-to-accidental-radioactive-emissions\/","title":{"rendered":"Intuitively tuned elastic bias correction of atmospheric inversion using Gaussian process prior: Application to accidental radioactive emissions"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Precise estimation of atmospheric pollutant releases is crucial for assessing the impact of environmental accidents. Atmospheric inversion typically relies on a linear model with a source\u2013receptor sensitivity (SRS) matrix, which may contain significant errors or even completely fail to capture the real magnitude of the event. We propose a correction of the SRS matrix formulated as slight shifts in the observation locations, effectively warping the sensitivity field. To constrain these shifts and ensure data-driven corrections, we model them using a Gaussian process prior. This prior not only enforces smoothness and sparsity, but also enables posterior prediction of shifts at previously unseen locations. This key feature provides a mechanism for hyper-parameter tuning: the predicted shift field can be visualized on a map and assessed by an expert. We present a user-friendly framework that combines a Bayesian inversion model with correction and a tuning algorithm based on L-curve-like plots and the maps of predicted shifts. The proposed method is demonstrated on three case studies: the ETEX-I experiment, the 137Cs emissions during the 2020 Chernobyl wildfires, and the 106Ru release in 2017.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 13:55:59","footnotes":""},"nva_tax_category":[1099],"class_list":["post-114800","nva_publication","type-nva_publication","status-publish","hentry","nva_tax_category-scientific-journal-publication"],"acf":[],"_links":{"self":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_publication\/114800","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\/114800\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=114800"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=114800"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}