The Aerosol Limb Imager: Multi-spectral Polarimetric Observations of Stratospheric Aerosol
Bibliographic record
Abstract
Abstract. The Aerosol Limb Imager (ALI) is designed to measure stratospheric aerosol by imaging limb-scattered sunlight. Each image taken by ALI is spectrally filtered at a tunable wavelength, and refined to consist of either horizontally or vertically polarized light. Novel to limb imaging, these polarized observations of ALI provide a means to isolate tangent altitudes which have signal contaminated by clouds. This avoids the ambiguity caused by clouds to be interpreted as aerosol in a retrieval. We present a polarized aerosol retrieval methodology which retrieves vertically resolved aerosol number density, and median radius of a uni-modal log-normal distribution, in addition to a scalar width. We explore the cloud discrimination and aerosol retrieval of ALI in simulation as validation of the efficacy and the limits to the technique. We then apply the retrieval to three example sets of observations taken from the most recent high-altitude balloon flight of ALI. One set provides a nominal exemplar, while the other two represent more difficult retrieval conditions of an increasingly polarized atmosphere. We compare the aerosol extinction of ALI in all three exemplar cases to the best coincident extinctions of three space based instruments: the Stratospheric Aerosol and Gas Experiment (SAGE III), the Ozone Mapping and Profiler Suite (OMPS), and the Optical Spectrograph and InfraRed Imaging System (OSIRIS). We provide discussion to the agreement of all three cases against the comparison instruments with respected to the efficacy of our approach. However, we find the retrieved aerosol extinction of ALI in the nominal case is in good agreement to the extinction reported by SAGE III, OMPS, and OSIRIS while also yielding aerosol particle size information.
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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 | 0.001 |
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".