Measuring Stratospheric H<sub>2</sub>O With an Airborne Spectrometer: Simulation With Realistic Detector Characteristics
Bibliographic record
Abstract
This study examines the ability of a realistic spectral sensor flying at the tropopause level for retrieving stratospheric H2O and temperature. This paper is an extension of an earlier study; the assumptions to best fit the characteristics of the operational sensors have been updated with the noise characteristics of real sensors. Several tests are conducted to examine the effects of changing spectral coverage and noise level on the quality of the retrieval. The results show that the potential advantage of including far infrared (IR) in the sensor's spectral coverage is hindered by the realistic noise level of the sensors under consideration. Under the current technology, enabling the far IR at the cost of mid-IR accuracy does not help improve H2O retrieval. Nevertheless, it is possible to achieve the retrieval accuracy of 0.5 ppmv for H2O and 1 K for temperature up to 50 hPa using a realistic sensor. The high sensitivity retrieval is advantageous for detecting the small temporal/spatial scale lower stratospheric moistening episodes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".