Accuracy Assessment Study of UNB3m Neutral Atmosphere Model for Global Tropospheric Delay Mitigation
Bibliographic record
Abstract
Abstract Tropospheric delay is the second major source of error after the ionospheric delay for satellite navigation systems. The transmitted signal could face a delay caused by the troposphere of over 2m at zenith and 20m at lower satellite elevation angles of 10 degrees and below. Positioning errors of 10m or greater can result from the inaccurate mitigation of the tropospheric delay. Many techniques are available for tropospheric delay mitigation consisting of surface meteorological models and global empirical models. Surface meteorological models need surface meteorological data to give high accuracy mitigation while the global empirical models need not. Several hybrid neutral atmosphere delay models have been developed by (University of New Brunswick, Canada) UNB researchers over the past decade or so. The most widely applicable current version is UNB3m, which uses the Saastamoinen zenith delays, Niell mapping functions, and a look-up table with annual mean and amplitude for temperature, pressure, and water vapour pressure varying with respect to latitude and height. This paper presents an assessment study of the behaviour of the UNB3m model compared with highly accurate IGS-tropospheric estimation for three different (latitude/height) IGS stations. The study was performed over four nonconsecutive weeks on different seasons over one year (October 2014 to July 2015). It can be concluded that using UNB3m model gives tropospheric delay correction accuracy of 0.050m in average for low latitude regions in all seasons. The model's accuracy is about 0.075m for medium latitude regions, while its highest accuracy is about 0.014m for high latitude regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".