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Record W1991916635 · doi:10.1007/bf02899828

Strategies for 4-D regional modeling of water vapour using GPS

2003· article· en· W1991916635 on OpenAlexaff
S. Skone, Shirshak Shrestha

Bibliographic record

VenueWuhan University Journal of Natural Sciences · 2003
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGlobal Positioning SystemWater vaporHydrostatic equilibriumEnvironmental scienceRemote sensingRangingData assimilationMeteorologyAtmospheric modelNumerical weather predictionAtmosphere (unit)Range (aeronautics)GeodesyComputer scienceGeologyGeographyMaterials sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

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 vapour 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 to all satellites in view. Observations of the Slant Wet Delay (SWD) can be used to model the vertical and horizontal structure of water vapour over a local area. These techniques are based on a tomographic approach using the SWD as input observables, where 4-D models of the wet refractivity may be derived. This method allows improved resolution of water vapour estimates for precise positioning applications and assimilation into Numerical Weather Predictions (NWP). In this paper we present strategies for real-time modeling of wet refractivity, with simulations and preliminary results of data processing for a regional GPS network in Southern California.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.235
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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