Atmospheric Moisture Estimation Using GPS on a Moving Platform
Bibliographic record
Abstract
GPS signals experience range delays as they propagate through the neutral atmosphere. These range errors are dependent on pressure, temperature and humidity along the signal path. By removing all other sources of ranging error, the effects due to atmospheric moisture may be isolated and estimates of zenith wet delay (ZWD) derived from GPS observations. Typical methods for ZWD estimation are based on postmission software operating in near real-time (e.g. Bernese, GAMIT). The real-time availability of precise GPS satellite orbit and clock products has enabled the development of a novel positioning methodology, however, known as precise point positioning (PPP). Based on the processing of undifferenced pseudorange and carrier phase observations from a single GPS receiver, PPP provides a new way to perform real-time ZWD estimation. A real-time PPP software product, P3®, has been developed by researchers at the University of Calgary. Recent advances in this PPP approach allow simultaneous estimation of position and ZWD in kinematic mode. In this paper, several tests are conducted to evaluate the feasibility of employing real-time PPP techniques for ZWD estimation in kinematic mode. Initial experiments are conducted using a static receiver, with known coordinates, to evaluate the limitations in kinematic processing. Further tests are conducted for ZWD estimation using a vehicle. Results for both the static and vehicle tests are compared against local truth data obtained from a water vapor radiometer. ZWD accuracies are at the 1-2 cm level. Data from an airborne platform is also processed for ZWD estimation using PPP under high-dynamic conditions. The feasibility of vertical moisture profiling is evaluated, where successive estimates of ZWD are differenced during the ascent phase of flight such that the amount of atmospheric moisture in discrete vertical layers is computed directly. Truth data are derived from highresolution regional numerical weather predictions provided by Environment Canada. ZWD accuracies are at the cm level for the airborne tests, and relative vertical profile accuracies are at the 20-30% level for the lower altitudes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".