An operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC

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dc.contributor.authorDaniel Okoh
dc.contributor.authorJohn Bosco Habarulema
dc.contributor.authorB. Nava
dc.contributor.authorClaudio Cesaroni
dc.contributor.authorPaul Baki
dc.contributor.authorYenca Migoya-Orué
dc.contributor.authorBabatunde Rabiu
dc.contributor.authorGeorge Ochieng
dc.contributor.authorAdero Ochieng Awuor
dc.contributor.authorPunyawi Jamjareegulgarn
dc.contributor.authorAdel Fathy
dc.contributor.authorPatrick Mungufeni
dc.contributor.authorClement Onime
dc.contributor.authorAderonke Akerele
dc.date.accessioned2026-05-08T19:17:15Z
dc.date.issued2026-1-21
dc.identifier.doi10.1016/j.asr.2026.01.042
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15914
dc.publisherAdvances in Space Research
dc.subjectIonosphere and magnetosphere dynamics
dc.subjectEarthquake Detection and Analysis
dc.subjectGNSS positioning and interference
dc.titleAn operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC
dc.typeArticle

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