Remote Sensing of Atmospheric Aerosols with the Aerosol Limb Imager
Bibliographic record
Abstract
Stratospheric aerosol has a large impact on the atmosphere of the Earth. In particular, it cools the climate via scattering sunlight into space. Although many processes ensure both a natural and anthropogenic background presence of these aerosols, significant acute changes can occur with major events like volcanic eruptions. Their role in the climate of the Earth, as well as their variability makes continuous observation of stratospheric aerosol a significant scientific priority. The Aerosol Limb Imager (ALI) is an instrument concept developed by the University of Saskatchewan to contribute to this observation. It is a multi-spectral polarized imager and is designed to take images only of the Earth's illuminated atmosphere as a measure of the scatting sunlight. These measurements are then used to infer stratospheric aerosol. The novel concept of ALI is the polarimetric ability. No other existing scientific imager which makes this type of measurement has had the ability to measure polarization. Discussed within this work are efforts to advance the ALI scientific and engineering readiness to stratospheric aerosol observation. This not only involved constructing and demonstrating a new optical iteration of the ALI instrument concept, but also advancing the scientific analysis techniques which make use of the polarized information ALI produces. In pursuit of this, a new calibration technique was developed and published by this work concerning the polarimetric calibration of optical instrumentation. Advantages of this new technique include characterizing the full sixteen element Mueller matrix where a typical method may not, quantifies meaningful uncertainty, and gives indication to performance and alignment of specific optical components. This calibration technique facilitated the scientific analysis of ALI observations to quantify stratospheric aerosol. In particular the polarized information is used to robustly identify clouds which may otherwise be mistaken for aerosol by an analysis of this nature. In addition, the algorithm developed by this work also yields aerosol size information on top of the typical metrics that most other comparable instrumentation can report. These capabilities are demonstrated in practice with the analysis of ALI data taken during a high-altitude balloon flight in 2022, where agreement with three other space base instruments is established.
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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 | 0.000 |
| 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 | 0.001 |
| 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".