Climate trends derived from long-term ground-based GNSS-derived Zenith Total Delay (ZTD)
Bibliographic record
Abstract
We present findings from an ongoing investigation into the evaluation of long-term trends in ground-based GNSS-derived Zenith Total Delay (ZTD) for potential integration into climate models, either for assimilation or validation purposes. Our analysis focuses on ZTD time series obtained from six REPRO3 IGS Analysis Centers (ACs) – COD, ESA, GFZ, GRG, JPL, and TUG – spanning 20 years or more. Thirty stations from the IGS global network were selected for this study. The ZTD time series underwent a homogenization process, utilizing ERA-5 derived ZTDs as a reference, followed by daily value averaging to minimize potential discrepancies arising from diverse estimation strategies employed by individual ACs. Similar averaging procedures were applied to ERA-5 ZTDs and the IGS tropo-product if already reprocessed in REPRO3. Two combinations, employing weighted mean and a robust least median of squares, were generated from the six homogenized ACs, serving as quality control measures for each AC. Analysis of trends in each of the nine ZTD time series was conducted in both time and frequency domains, revealing geographical variations in results. For instance, at station ALBH in Canada, the inter-AC scatter was 0.47 mm/decade for trends, 0.11 mm for annual amplitudes, and 0.29 degrees for annual phases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".