The SPARC water vapor assessment II: Assessment of satellite measurements of upper tropospheric water vapor
Bibliographic record
Abstract
Abstract. Nineteen limb viewing (occultation and passive thermal) and two nadir humidity data sets are intercompared and also compared to frostpoint hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. Water vapor in this region is a challenging measurement because concentrations vary between 2–1000 parts per million volume with sharp changes in vertical gradients near the tropopause. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite measured humidity is based on coincident comparisons with frostpoint hygrometer sondes, multi month mapped comparisons, zonal mean time series comparisons and coincident satellite to satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively, they can differ by a factor of two in concentration, with strong dependencies on the amount of H2O. Additionally, time-lag response corrected Vaisala-RS92 radiosondes are compared to satellites and the frostpoint hygrometer measurements. In summary, most satellite data sets reviewed here show on average ~30 % agreement amongst themselves and frostpoint data but with an additional ~30 % variability about the mean. The Vaisala-RS92 sonde even with a time-lag correction shows poor behavior for pressure less than 200 hPa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".