Impact of different NWM-derived mapping functions on VLBI and GPS analysis
Bibliographic record
Abstract
In recent years, numerical weather models have shown the potential to provide a good representation of the electrically neutral atmosphere. This fact has been exploited for the modeling of space geodetic observations. The Vienna Mapping Functions 1 (VMF1) are the NWM-based model recommended by the latest IERS Conventions. The VMF1 are being produced 6 hourly based on the European Centre for Medium-Range Weather Forecasts operational model. UNB-VMF1 provide meteorological parameters aiding neutral atmosphere modeling for VLBI and GNSS, based on the same concept but utilizing the Canadian Meteorological Centre model. This study presents comparisons between the VMF1 and the UNB-VMF1 in both delay and position domains, using global networks of VLBI and GPS stations. It is shown that the zenith delays agree better than 3.5 mm (hydrostatic) and 20 mm (wet) which implies an equivalent predicted height error of less than 2 mm. In the position domain and VLBI analysis, comparison of the weighted root-mean-square error (wrms) of the height component showed a maximum difference of 1.7 mm. For 48% of the stations, the use of VMF1 reduced the height wrms of the stations by 2.6% on average compared to a respective reduction of 1.7% for 41% of the stations employing the UNB-VMF1. For the subset of VLBI stations participating in a large number of sessions, neither mapping function outranked the other. GPS analysis using Precise Point Positioning had a sub-mm respective difference, while the wrms of the individual solutions had a maximum value of 12 mm for the 1-year-long analysis. A clear advantage of one NWM over the other was not shown, and the statistics proved that the two mapping functions yield equal results in geodetic analysis.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".