Ground-GNSS ZTD trends for climate models
Bibliographic record
Abstract
GNSS Zenith Total Delay (ZTD) estimates are quantities of great interest by climate modellers since atmospheric water vapour is the major greenhouse gas. Therefore, the importance of its accurate, long-term monitoring and evaluation of trends and variability, potentially serving as independent benchmarks to climatological models, both on longer trends derived from GNSS, but also shorter trends, which could be used for assimilation and validation of climate models. ZTD estimates are determined on a regular basis by several processing centers as well as by demand. It has also been demonstrated that series of ZTD estimates can be used for quality control purposes. At the same time, GNSS reached the “maturity age” of 30 years when climate normals of ZTD and gradients can be derived. But what would be the best ZTD series to serve the climate community? What series would offer the most realistic trends? This poster discusses an on-going investigation under the auspices of the International Association of Geodesy, through a joint working group nested within the Inter-Commission Committee on Geodesy for Climate Research. In a previous study, we made use of the ZTD series derived by the third reprocessing campaign (REPRO3), based on a variety of processing modes and models. But this study was partial as the ZTD times series estimated by the Analysis Centers were not covering the same periods. This time, ZTD time series are generated using dedicated PPP scientific software suites. The generated trends are to be compared and analysed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.009 | 0.005 |
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".