Optimal Estimation Method Retrievals of Stratospheric OzoneProfiles from a DIAL Lidar
Bibliographic record
Abstract
Abstract. This paper provides a detailed description of the first principle Optimal Estimation Method (OEM) which is applied to ozone retrieval analysis using Differential Absorption Lidar (DIAL) measurements. The air density, detector dead times, background coefficients, and lidar constants are simultaneously retrieved along with ozone density profiles. Using an averaging kernel, the OEM provides the vertical resolution of the retrieval as a function of altitude. A maximum acceptable height at which the a priori has a small contribution to the retrieval is calculated for each profile as well. Moreover, a complete uncertainty budget including both systematic and statistical uncertainties is given for each individual retrieved profile. Long term stratospheric DIAL ozone measurements have been carried out at the Observatoire de Haute-Provence (OHP) since 1985. The OEM is applied to 3 nights of measurements at OHP during an intensive ozone campaign in July 2017 where coincident lidar-ozonesonde measurements are available. The retrieved ozone density profiles are in good agreement with both traditional analysis and the ozonesonde measurements. For the three nights of measurements, below 15 km the difference between the OEM and the sonde profiles is less than 25 %, at altitudes between 15 km to 25 km the difference is less than 10 %, and the OEM can successfully catch many variations of ozone which are detected in the sonde profiles due to its ability to adjust its vertical resolution as the signal varies. Above 25 km the difference between the OEM and the sonde profiles does not exceed 20 %.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".