An update to the expression of atmospheric refractivity for GNSS signals
Bibliographic record
Abstract
Abstract. This study revisits previous formulations of atmospheric refractivity at L-band frequencies, focusing on signals from Global Navigation Satellite Systems (GNSS). A refined model expression is proposed as a function of air density, temperature, and composition, evaluated using a comprehensive set of existing laboratory and atmospheric measurements. The key measurements that most affect the final accuracy are identified, establishing traceable error bounds and indicating where further experimental work could confirm or improve the model. Recent studies on the use of large volumes of GNSS radio occultation (GNSSRO) observations in Numerical Weather Prediction (NWP) show that the precise formulation of refractivity becomes increasingly critical as data volumes grow. Although the revision is modest, its impact lies within the range where NWP sensitivity becomes non-negligible. Compared to earlier work, this study (1) incorporates updated fundamental measurements, (2) accounts for the small but measurable variability in atmospheric composition, mainly increasing CO2 and decreasing O2, emphasizing that refractivity traceability is composition-dependent, and (3) extends the model to include hydrometeors. A simplified formulation based on hydrometeor oblateness is proposed, suitable for NWP applications where only limited hydrometeor information is available. Nonspherical hydrometeors tend to align during fall, introducing weak birefringence that can be detected during GNSS occultations with dual-polarization receivers. The resulting refractivity expression is presented as a function of air density, temperature, moisture, and composition, and (using a simplified model of atmospheric evolution) also as a function of density, temperature, moisture, and time.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".