Long-term ZTD and ZWD series and climate normals using NCEP1
Bibliographic record
Abstract
The World Meteorological Organization (WMO) recommends the use of climate normals for dealing with the analysis of variations and trends of the meteorological parameters or be used as input to predictive climate models. The suggested period is 30 years, but shorter periods can also be employed. We computed zenith total delay (ZTD) and zenith wet delay (ZWD) series for each node of NCEP1 numerical weather model, starting in 1948. We computed climate normals of those two parameters using periods of 1, 5, 10, 15, 20 and 30 years, with and without the annual signature. To assess window size impact, we looked at variations and correlation of trends derived from the various solutions. Results shows the obvious better smoothing using larger windows and the decrease of the impact of annual signature. Regions with positive trends appear to be concentrated in continental masses and the equator line, and the most significant negative trends are in the oceans. ZTD increase is caused primarily by an increase in ZWD and is an indication of variations in ZWD variables. In the case of water vapor, such an increase in ZWD shows us a probable increase in the amount of water vapor in the atmosphere. Comparisons with trends computed from GNSS-derived ZTD and ZWD series are included with the caveat that time period for such comparisons must be shorter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".