Multi-GNSS global ionosphere modeling enhanced by virtual observation stations based on IRI-2016 model
Bibliographic record
Abstract
Abstract The inhomogeneous distribution of Global Navigation Satellite System (GNSS) stations results in inaccurate vertical total electron contents (VTECs) in global ionosphere maps (GIMs) over areas with large GNSS data gaps. Incorporating VTECs from the International Reference Ionosphere (IRI) model is usually adopted as one approach to mitigate the inaccurate VTECs. However, large and complicated spatiotemporal varying VTEC biases between GNSS and IRI suggest a robust strategy to optimally combine GNSS and IRI VTECs for operational high-precision modeling. Here, we thoroughly analyze the characteristics of VTEC biases between GNSS and IRI-2016 model in different latitudes from 2009 to 2019, and develop an improved functional and stochastic model. An automated assimilation strategy of GNSS and IRI-2016 VTECs is proposed for Shanghai Astronomical Observatory final GIM (SHAG) routine estimation, and the reliability of GIMs in areas with lack of stations is enhanced by attaching Virtual Observation Stations (VOSs) based on IRI-2016 model and VOS bias parameters. Experimental results show that the root-mean-square errors (RMSEs) of SHAG with respect to VTECs retrieved from four independent GNSS assessment stations are reduced by 21.65–53.06% in the large data gaps with the assistance of VOSs. Furthermore, we validated the long-term reliability of SHAG spanned one solar cycle (2009–2019) with International GNSS Service (IGS) final GIMs and satellite altimetry VTECs. Validation results suggest that SHAG is in good agreement with IGS final GIMs, and reliability of SHAG in large GNSS data gap areas is significantly improved by attaching VOSs and biases. This methodology also represents an efficient tool for automated global ionospheric modeling integrating multi-source data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".