Evaluation of GPS Precipitable Water over Canada and the IGS Network
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Bibliographic record
Abstract
Abstract Precipitable water (PW) derived from the GPS zenith tropospheric delay (ZTD) is evaluated (as a first step toward variational data assimilation) through comparison with that of collocated radiosondes (RS_PW), operational analyses, and 6-h forecasts (from the Canadian Global Environmental Multiscale model) of the Canadian Meteorological Centre. Two sources of ZTD data are considered: 1) final ZTD (over Canada), computed by the Geodetic Survey Division (GSD) of Natural Resources Canada, and 2) final ZTD (distributed globally), obtained from the International GPS Service (IGS). The mean GSD GPS–derived PW (GPS_PW) is 14.9 mm (reflecting the relatively cold Canadian climate), whereas that of the IGS dataset is 20.8 mm. Intercomparison statistics [correlation, standard deviation (SD), and bias] between GPS_PW and RS_PW are, respectively, 0.97, 2.04 mm, and 1.35 mm for the GSD data and 0.98, 2.6 mm, and 0.67 mm for the IGS data. Comparisons of GPS_PW with 6-h forecast PW (TRIAL_PW) show slightly lower correlations and a higher SD. The increase in SD is greater for the IGS data, which is not surprising, because in regions such as the Tropics and subtropics, moisture forecasts are of a lower quality and the RS observation network is sparse. From a three-way intercomparison (IGS GPS_PW, RS_PW, and TRIAL_PW) of the SD statistics, it is found that GPS_PW has the lowest estimated PW error (≈1 mm) for PW in the 5–30-mm range. For PW greater than 30 mm, the RS_PW estimated error is ≈2 mm, and that of GPS_PW is ≈2.5 mm. The TRIAL_PW estimated error increases with PW, reaching 5.5 mm in the 40–55-mm PW range. These intercomparison results indicate that GPS_PW should be a useful source of humidity information for NWP applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it