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An operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC
(2026-03-15)
Okoh, Daniel
;
Habarulema, John Bosco
;
Nava, Bruno
;
Cesaroni, Claudio
;
Baki, Paul
The 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.
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Development of a Global Climate Model for Atmospheric Temperature Using Machine Learning
(2026-01-01)
Okoh, Daniel
;
Awuor, Adero
;
Ochieng, George
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Baki, Paul
;
Habarulema, John Bosco
This article presents a novel three-dimensional global model of atmospheric temperature developed using Artificial Neural Networks (ANNs) trained on radio occultation (RO) data from the COSMIC I and COSMIC II satellite missions. Over 14.7 million quality-controlled profiles were used, providing approximately 9.5 billion data points that capture temperature variability across latitude, longitude, altitude (0-60 km), and time (2006-2025). The global domain was divided into 1296 spatial grid cells (10° × 5°) to enable localized ANN training and ensure efficient handling of regional atmospheric dynamics. Model performance was evaluated through cross-validation and independent testing against radiosonde measurements from 684 stations worldwide. Results show mean absolute errors of 1.5 °C-4.5 °C and root-mean-square errors of 2.5 °C-6.5 °C, with best performance in the tropical troposphere and increasing errors toward high latitudes. The model successfully reproduces key climatological structures (including the tropospheric lapse rate, stratospheric inversion, and seasonal hemispheric asymmetries), and accurately captures diurnal and annual thermal cycles. Long-term simulations (2006-2025) reveal a distinct tropospheric warming trend (∼+0.07 °C per year at 11 km) and a corresponding stratospheric cooling (∼-0.03 °C per year near 32 km), consistent with established satellite and reanalysis records. These results demonstrate that ANN-based frameworks can effectively model global atmospheric thermal structure and evolution, providing a scalable approach for future climate monitoring and forecasting applications.
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A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers
(2025-06-01)
Okoh, Daniel
;
Cesaroni, Claudio
;
Rabiu, Babatunde
;
Shiokawa, Kazuo
;
Otsuka, Yuichi
Equatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.

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