4-D Tropospheric Tomography using a Ground-Based Regional GPS Network and Vertical Profile Constraints
Bibliographic record
Abstract
The troposphere causes a delay in the GPS signal received at the Earth, generally classified as a hydrostatic component and a wet component. Once other sources of error are mitigated, the positioning problem at a given GPS station can be inverted such that the total neutral atmospheric delay overlying the site is solved for. By applying a hydrostatic model, the wet delay can be isolated and recovered, and this type of information can be used to describe water vapor distribution within a network of GPS receivers. It is possible to derive estimates of slant wet delay (SWD) along individual satellite-receiver lines-of-sight for a GPS reference station. By using data from a regional network of GPS stations, spatial models of the atmospheric moisture can be derived in a local area. Such models may employ a tomographic technique to estimate the 4-D wet refractivity. The resulting atmospheric moisture field generally has high horizontal resolution, but often poor vertical resolution due to network geometry. If vertical atmospheric profiles are available from other sources this information may be assimilated into the model to strengthen the tomography solution. The focus of this paper is improved 4-D modeling of tropospheric refractivity in a regional GPS network using ground-based observations and vertical profiles derived from alternative sources. In 2003 a network of 14 GPS stations were deployed by the University of Calgary’s Department of Geomatics Engineering in southern Alberta for precise positioning and atmospheric water vapor determination. Data was collected from this network (called the Southern Alberta Network - SAN) and radiosondes were released by the Meteorological Service of Canada (MSC) at regular intervals during a data collection campaign in the summer of 2003. Accuracy of tomographic water vapor retrieval techniques are evaluated in the SAN for the 2003 data collection campaign. Tomography methods are tested using 1) only ground-based GPS input, 2) a ground-based GPS solution augmented with vertical wet refractivity profiles derived from radiosondes released within the GPS network, and 3) a ground-based GPS solution augmented with climate information about the vertical wet refractivity distribution. Zenith wet delays (ZWD) are computed for the various cases, by integrating through the 4-D tomography predictions, and these values are compared with truth ZWD derived from radiosondes. Results indicate that ZWD may be modeled with accuracies at the sub-centimeter level using a ground-based GPS network augmented with radiosonde observations. This represents an improvement over the GPS-only approach.
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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 | 0.001 |
| 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".