Static wind imaging Michelson interferometer for the measurement of stratospheric wind fields
Bibliographic record
Abstract
The stratospheric wind field provides significant information on the dynamics, constituent, and energy transport in the Earth's atmosphere. The measurement of the atmospheric wind field on a global basis at these heights is still lacking because few wind imaging interferometers have been developed that can measure wind in this region. In this paper, we describe an advanced compact static wind imaging Michelson interferometer (SWIMI) developed to measure the stratospheric wind field using near-infrared airglow emissions. The instrument contains a field widened and thermal compensated interferometer with a segmented reflective mirror in one arm, which replace the moving mirror in a conventional Michelson interferometer, to provide interference phase steps. The field widened, achromatic, temperature compensated scheme has been designed and manufactured. The characterization, calibration, inversion software, and test of the instrument have been completed. The capacity of two-dimensional wind, temperature, and ozone measurement of the instrument has been verified in the lab experiment and model simulation. What we believe to be the novel principle, modeling, design, and experiment demonstrated in this paper will offer a significant reference to the static, simultaneous and real-time detection and inversion of the global wind field, temperature, and ozone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".