Doppler temperatures from O(<sup>1</sup>D) airglow in the daytime thermosphere as observed by the Wind Imaging Interferometer (WINDII) on the UARS satellite
Bibliographic record
Abstract
Abstract. From 1992 to 1997, the WINDII interferometer on board the UARS satellite acquired a large set of thermospheric data from the O(1D) and O(1S) airglows. We report here for the first time on daytime O(1D) Doppler temperatures obtained with version 5.11 of the WINDII data processing software. Using a statistical analysis of the temperatures independently measured by the two WINDII fields of view, we estimate that the temperature variations larger than 40 K can be considered as geophysical. Comparisons of WINDII temperatures measured during magnetically quiet days with temperatures obtained by the MSIS-90 and DTM-94 thermospheric models show a 100 K bias. We demonstrate, however, that the modeled temperature variations represent very well the mean temperature variation observed by WINDII over 4 years. We also show that the observed latitudinal/local time variation is in very good agreement with the two empirical models. Finally, the temperature variations during a magnetically disturbed day are found to be qualitatively well represented in form by the models, but largely underestimated. The presence of non-thermal atoms and instrument related issues are discussed as possible explanations for the 100 K bias between the WINDII Doppler temperatures and the empirical models.Key words. Atmospheric composition and structure (air-glow and aurora; pressure, density and temperature; instruments and techniques)
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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 | 0.000 |
| 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) | 0.001 | 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".