SCIAMACHY lunar occultation water vapor measurements: retrieval and validation results
Bibliographic record
Abstract
Abstract. SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) lunar occultation measurements have been used to derive vertical profiles of stratospheric water vapor for the Southern Hemisphere in the near infrared (NIR) spectral range of 1350–1420 nm. The focus of this study is to present the retrieval methodology including the sensitivity studies and optimizations for the implementation of the radiative transfer model on SCIAMACHY lunar occultation measurements. The study also includes the validation of the data product with the collocated measurements from two satellite occultation instruments and two instruments measuring in limb geometry. The SCIAMACHY lunar occultation water vapor measurement comparisons with the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) instrument have shown an agreement of 5% on the average that is well within the reported biases of ACE in the stratosphere. The comparisons with HALOE (Halogen Occultation Experiment) have also shown good results where the agreement between the instruments is within 5%. The validations of the lunar occultation water vapor measurements with MLS (Microwave Limb Sounder) instrument are exceptionally good, varying between 1.5 to around 4%. The validations with MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) are in the range of 10%. A validated dataset of water vapor vertical distributions from SCIAMACHY lunar occultation measurements is expected to facilitate the understanding of physical and chemical processes in the southern mid-latitudes and the dynamical processes related to the polar vortex.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".