An evaluation of the expression of the atmospheric refractivity for GPS signals
Bibliographic record
Abstract
[1] An expression is derived from first principles for the refractivity of air at L band frequencies, which includes GPS, as well as other GNSS satellite radionavigation signals. Under conditions of pressure, temperature, and moisture content found in the Earth's atmosphere, the expression has an average relative error of approximately 0.01%. This level of accuracy is required to guarantee that the expression does not introduce bias, when it is used within the context of Numerical Weather Prediction (NWP) applications. The thermodynamic dependences of the air's refractivity N are revisited, and the possible sources of uncertainty are analyzed. A first principles microphysical model is constructed, which relates the refractivity at L band frequencies with several measurable properties of matter. The experimental values that are critical for this purpose are already available in the literature and are of high accuracy. Based on this model, a simple expression suitable for atmospheric and weather applications is proposed: N ≡ (n − 1) · 106 = N0 · (1 + N0) where N0 = (222.682 + 0.069 · τ) · ρd + (6701.605 + 6385.886 · τ) · ρw with ρd and ρw as the densities of dry air and water vapor in the air (kg/m3), τ = 273.15/T − 1, and T as the absolute temperature in K. The dependence of the coefficients in the expression with respect to the input physical parameters is analyzed. Given the error of the experimental parameters, it is concluded that the proposed expression improves the accuracy to meet the needs of NWP applications.
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 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".