Validation of a Tropospheric Voxel Tomography Model in a Regional GPS Network
Bibliographic record
Abstract
Global Positioning System (GPS) signals experience ranging errors due to propagation through the neutral atmosphere. These range delays consist of a hydrostatic component, dependent on air pressure and temperature, and a wet delay dependent on water vapor pressure and temperature. Range delays arising from the hydrostatic component can be computed with accuracies of a few millimeters using existing models, provided that surface barometric or meteorological data are available. By using a regional network of GPS reference stations, it is possible to recover estimates of the slant wet delay (SWD) to all satellites in view. SWD observations can then be used to model the vertical and horizontal structure of water vapor over a local area, using a tomographic approach. The University of Calgary has deployed a regional GPS network in Southern Alberta with station spacing in the range of 30-100 km. Continuous network observations are currently logged at each site and are streamed to a central processing facility at the University of Calgary in real-time. This network is used primarily for research related to real-time precise positioning applications. One element of this work is improved troposphere modeling within the network, and secondary applications focus on meteorological processes and weather predictions. For these purposes, precise meteorological instruments are co- located with a number of stations within the network. Variable weather conditions occur in the foothills of the Rockies near Calgary, and the Southern Alberta network allows great opportunities to assess detection and modeling of severe weather events using GPS. Severe prairie thunderstorms are a multi-million dollar problem in Southern Alberta, and the physical processes associated with precipitation patterns are not well understood. These events form predominantly over the foothills near Calgary and may be identified in observations of 4-D water vapor distributions. In this paper, a newly developed 4-D wet refractivity model is implemented and tested using the Southern Alberta regional network. A field campaign was conducted, in collaboration with the Meteorological Service of Canada, to derive an extensive set of truth data from radiosonde soundings. The truth data was derived during summer months in which severe weather events are observed. This paper presents the validation of a voxel tomography approach implemented for the Southern Alberta Network using radiosonde-derived truth wet-refractivity values.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".