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

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dc.contributor.authorOkoh, Daniel
dc.contributor.authorHabarulema, John Bosco
dc.contributor.authorNava, Bruno
dc.contributor.authorCesaroni, Claudio
dc.contributor.authorBaki, Paul
dc.contributor.authorMigoya-Orué, Yenca
dc.contributor.authorRabiu, Babatunde
dc.contributor.authorOchieng, George
dc.contributor.authorAwuor, Adero
dc.contributor.authorJamjareegulgarn, Punyawi
dc.contributor.authorFathy, Adel
dc.contributor.authorMungufeni, Patrick
dc.contributor.authorOnime, Clement
dc.contributor.authorAkerele, Aderonke
dc.date.accessioned2026-08-06T10:55:00Z
dc.date.available2026-08-06T10:55:00Z
dc.date.issued2026-03-15
dc.description.abstractThe Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) provides global Radio Occultation (RO) measurements of ionospheric total electron content (TEC), but these values are systematically underestimated relative to ground-based Global Navigation Satellite System (GNSS)-derived TEC due to the exclusion of the plasmaspheric contribution. This study presents a machine learning calibration framework that transforms COSMIC TEC into GNSS-equivalent values. Using co-located COSMIC and GNSS observations from 2006 to 2025, we developed neural network models (ROTEC-A and ROTEC-B) trained on (19 and 22) input features respectively, including COSMIC profile parameters, spatiotemporal descriptors, and optionally, solar and geomagnetic activity indices. Results show that the calibration effectively mitigates systematic underestimation, reducing mean bias from 6.97 TECU (uncalibrated COSMIC) to near zero (0.02–0.03 TECU). The calibrated products also substantially reduce skewness in residuals, yielding nearly symmetric error distributions suitable for data assimilation. Across various latitudinal, local time, and seasonal sectors, mean absolute errors were reduced by 50–75%, with the best performance at mid-latitudes and slightly elevated errors in high-latitude and equatorial regions. Although, the inclusion of solar and geomagnetic indices yielded marginal improvements, statistical tests confirmed no significant advantage over the baseline model. The operationally oriented framework outputs calibrated GNSS-equivalent TEC in near real-time, providing enhanced ionospheric monitoring capability, especially over GNSS-sparse regions such as oceans and deserts. These results demonstrate the potential of COSMIC RO data, once calibrated, to serve as a reliable complement to GNSS observations for ionospheric research, space weather monitoring, and operational applications.
dc.identifier.citationAdvances in Space Research, 77(6), 7257-7273, 2026
dc.identifier.doi10.1016/j.asr.2026.01.042
dc.identifier.issn02731177
dc.identifier.other2-s2.0-105030270061
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17963
dc.sourceAdvances in Space Research
dc.subjectCOSMIC-TEC
dc.subjectGNSS-TEC
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectTEC Calibration
dc.titleAn operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC
dc.typeArticle

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