Scientific Assessment of the SWIFT Instrument Design
Bibliographic record
Abstract
Abstract The Stratospheric Wind Interferometer for Transport Studies (SWIFT) is a proposed satellite instrument. SWIFT is an imaging field-widened Doppler Michelson interferometer. It observes a thermal IR atmospheric emission line in a limb-viewing geometry in order to measure stratospheric winds and stratospheric ozone concentration profiles with global coverage during both day and night. SWIFT has the capability of improving the knowledge of the dynamics of the stratosphere and global distribution of and global transport of ozone. The target wind and ozone accuracies are 3 m s−1 and 5%–10%, respectively. The instrument is a follow up to the highly successful Canada–France Wind-Imaging Interferometer (WINDII) instrument on NASA's Upper Atmosphere Research Satellite (UARS). To assess the suitability of the method of Doppler imaging Michelson interferometry for the measurement of stratospheric wind and ozone using the SWIFT instrument, a scientific assessment of the instrument performance was undertaken using forward and inverse modeling and error analyses. This paper is aimed at determining the technical and scientific feasibility of the SWIFT instrument and its ability to meet the science requirements. This paper also briefly describes the SWIFT experiment, the data retrieval algorithms, and technical challenges in stratospheric wind measurements. Meeting the wind accuracy requirement imposes tight requirements on instrument thermal stability, filter monitoring, and determination of reference phase calibration. The SWIFT instrument design shows a strong level of dependence on the knowledge of atmospheric N2O concentration. The presence of N2O as an interfering species degrades the SWIFT performance at all altitudes with the largest impact especially for altitudes below 30 km.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".