Generation of Regional Ionospheric TEC Maps With EIA Nowcasting/Forecasting Capability During Geomagnetic Storm Conditions
Bibliographic record
Abstract
In this paper, ground based Global Positioning System (GPS) Total Electron Content (TEC) observations collected from the GPS Aided GEO Augmented Navigation (GAGAN) network over low latitude Indian subcontinent are assimilated into the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Gauss-Markov Kalman filter methodology is utilized and the background model error covariance (BMEC) matrix is framed using solar, geomagnetic, cross tail potential and hemispheric power indices using multivariate principal component analysis (MPCA) method. The model variations through different high latitude electric field models (Weimer and Heelis) are utilized in the data assimilation analysis. The proper attempt is modeled for providing TEC forecasts during geomagnetic storm conditions from 15thto 20thMarch 2015 days over Indian region. The more concentration is given in understanding the equatorial ionization anomaly (EIA) characteristics during the initial, main and recovery phase of St. Patrick's Day March 2015 geomagnetic storm. The proposed model forecast outperforms the persistence forecast with maximum forecast score (1.6) and forecast skill (2.2) during recovery phase of storm. TEC forecasts are validated with current time step GPS observations and 93 % correlation is observed on quiet day (10thMarch 2015) and 82 % correlation is noticed on main phase of storm (17thMarch 2015). Also proposed TEC model analysis and forecast results are compared with International Global Navigation Satellite Service (IGS) TEC stations datasets. A clear negative storm effect (EIA inhibition) has been forecasted well with the proposed Kalman filter and MPCA methodology aspects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".