Solar occultation satellite data and derived meteorological products: Sampling issues and comparisons with Aura Microwave Limb Sounder
Bibliographic record
Abstract
Derived Meteorological Products (DMPs, including potential temperature, potential vorticity (PV), equivalent latitude (EqL), horizontal winds and tropopause locations) from several meteorological analyses have been produced for the locations and times of measurements taken by several solar occultation instruments and the Aura Microwave Limb Sounder (MLS). MLS and solar occultation data are analyzed using DMPs to illustrate sampling issues that may affect interpretation and comparison of data sets with diverse sampling patterns and to provide guidance regarding the kinds of studies that benefit most from analyzing satellite data in relation to meteorological conditions using the DMPs. Using EqL or PV as a vortex‐centered coordinate does not alleviate all sampling problems, including those in studies using “vortex averages” of solar occultation data and in analyses of localized features (such as polar stratospheric clouds) and other fields that do not correlate well with PV. Using DMPs to view measurements with respect to their air mass characteristics is particularly valuable in studies of transport of long‐lived trace gases, polar processing in the winter lower stratosphere, and distributions and transport of O3 and other trace gases from the upper troposphere through the lower stratosphere. The comparisons shown here demonstrate good agreement between MLS and solar occultation data for O3, N2O, H2O, HNO3, and HCl; small biases are attributable to sampling effects or are consistent with detailed validation results presented elsewhere in this special section. The DMPs are valuable for many scientific studies and to facilitate validation of noncoincident measurements.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".