Signature of the atmospheric compressibility factor in COSMIC, CHAMP, and GRACE radio occultation data
Bibliographic record
Abstract
It is shown that the deviation of air from an ideal gas has nonnegligible effects when assimilating GPS radio occultation (GPSRO) data in a Numerical Weather Prediction (NWP) system. Therefore an assimilation system that aims to be unbiased to within the threshold of detection should account for this effect. GPSRO data are vertically referenced in terms of mean sea level altitude. Many other data types are vertically referenced in pressure units. The assimilation system may use yet another vertical coordinate. The required transformations between vertical coordinate systems should not induce significant biases. In the context of NWP the threshold of detection for a systematic height bias is on the order of 1–2 m. This study demonstrates that this level of accuracy cannot be obtained unless the deviation of air from an ideal gas, known as compressibility factor, is properly taken into account. With the current volume of GPSRO data an inconsistency between pressure and altitude scales larger than the mentioned threshold can lead to the development of nonnegligible biases in NWP assimilation cycles. Consideration of the compressibility factor realigns the altitude‐ and pressure‐based scales to better than 1 m in the entire troposphere. Impacts are appreciated not only from global averages but from zonal averages as well.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".