Satellite Measurement of Stratospheric Winds and Ozone Using Doppler Michelson Interferometry. Part II: Retrieval Method and Expected Performance
Bibliographic record
Abstract
Abstract This paper is about the retrieval of horizontal wind and ozone number density from measurement simulations for the Stratospheric Wind Interferometer for Transport Studies (SWIFT). This instrument relies on the concept of imaging Doppler Michelson interferometry applied to thermal infrared emission originating from the stratosphere. The instrument and measurement simulations are described in detail in the first of this series of two papers. In this second paper, a summary of the measurement simulations and a data retrieval method suited to these measurements are first presented. The inversion method consists of the maximum a posteriori solution approach with added differential regularization and, when required, iterations performed with the Gauss–Newton method. Inversion characterization and an error analysis have been performed. Retrieval noise estimates have been obtained both from derived covariance matrices and sample inversions. Retrieval noise levels for wind and ozone number density of ∼1–3 m s−1 and <1% have been obtained over the altitude range of 20–45 km with Backus–Gilbert resolving lengths of ∼1.5 km. Retrieval noise levels over the extended altitude range of 15–55 km are less than 10 m s−1 and 2%. The sensitivity to other error sources has been examined through a few sample realizations. The contributions from these other errors can be as important as or more so than retrieval noise. An error budget identifying contributing wind and ozone error levels to total errors of 5 m s−1 and 5% for altitudes of 20–45 km has been prepared relying on the retrieval errors and knowledge of the instrument design.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".