{"id":118540,"date":"2026-09-11T21:19:13","date_gmt":"2026-09-11T19:19:13","guid":{"rendered":"https:\/\/nilu.gnist.dev\/publikasjoner\/machine-learning-based-retrieval-of-total-ozone-column-amount-and-cloud-optical-depth-from-irradiance-measurements\/"},"modified":"2026-09-11T21:19:13","modified_gmt":"2026-09-11T19:19:13","slug":"machine-learning-based-retrieval-of-total-ozone-column-amount-and-cloud-optical-depth-from-irradiance-measurements","status":"publish","type":"nva_publication","link":"https:\/\/nilu.gnist.dev\/en\/publications\/machine-learning-based-retrieval-of-total-ozone-column-amount-and-cloud-optical-depth-from-irradiance-measurements\/","title":{"rendered":"Machine Learning-Based Retrieval of Total Ozone Column Amount and Cloud Optical Depth from Irradiance Measurements"},"content":{"rendered":"<p class=\"wp-block-paragraph\">A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74\u00b0 N, \u221274.03\u00b0 E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014\u20132023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA\u2019s AURA satellite. COD results are also provided.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_searchwp_excluded":"","_id":"","_status":"PUBLISHED","_sync_date":"2026-09-11 21:16:59","footnotes":""},"nva_tax_category":[1099],"class_list":["post-118540","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\/118540","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\/118540\/revisions"}],"wp:attachment":[{"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/media?parent=118540"}],"wp:term":[{"taxonomy":"nva_tax_category","embeddable":true,"href":"https:\/\/nilu.gnist.dev\/en\/wp-json\/wp\/v2\/nva_tax_category?post=118540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}