Precipitable water characterization using global navigation satellite system data: A case study in Nghia Do area, Vietnam
Bibliographic record
Abstract
This paper evaluates the estimated total precipitable water (TPW) and studies its characteristics in the Nghia Do area (Hanoi) using Global Positioning System (GPS) data and the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) processing technique and calculation tool. The TPW was estimated from GPS data from September 22, 2022 to March 31, 2023 using CSRS-PPP provided by Natural Resources Canada. The calculated TPW was validated with TPW products from Aeronet data and radiosonde data. Taking advantage of its high time resolution, the TPW estimated from GPS data was used to analyze the temporal variation of TPW during cold surges affecting the Nghia Do area. The results indicate a strong agreement between the estimated TPW from GPS data and TPW products derived from Aeronet and radiosonde data. The mean error (ME), root mean square error (RMSE), and correlation coefficient between the estimated TPW from GPS data and the Aeronet-derived product are 0.68 mm, 2.05 mm, and 0.988, respectively. The corresponding values between the estimated TPW and the radiosonde-derived product are -3.01 mm, 3.24 mm, and 0.996, respectively. The study of TPW variation over the Nghia Do area during the research period revealed that, generally, TPW values in December and January were lower than those in the other months. Before the arrival of a specific cold surge at a station, TPW tends to increase by about 6 mm within 12 hours as convection activities ahead of the cold front intensify. After the cold air passes over the station, the TPW value decreases by about 8 mm within 12 hours due to the influx of cold and dry winter air brought by the prevailing winds. This distinct TPW variation pattern suggests that GPS data can be effectively employed to define the arrival of cold surges in the station area.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".