Combining ALI Space-based and MPL Ground-based Measurements for Improved Aerosol Characterization
Bibliographic record
Abstract
Aerosols play an important role in Earth’s radiative balance, however, considerable uncertainty remains on their overall climate impact. The Aerosol Limb Imager (ALI) is a satellite instrument that will provide high-sensitivity measurements of aerosols in the upper troposphere and stratosphere. ALI will fly on the Canadian High-Altitude Aerosols, Water Vapour and Clouds (HAWC) satellite. We investigate the synergy between ALI and ground-based remote-sensing measurements from The Canadian Micro-Pulse Lidar Network (MPLCAN) to determine the possibility and advantages of a merger data product. The ALI retrieved quantities are not directly comparable to the MPL attenuated backscatter measurements, so assumptions are made about the constituents and optical properties of the atmosphere to compare them. We show that ALI's novel particle size retrieval provides a method for comparison with lidar that is applicable to any backscatter lidar measurements where the lidar constant is well known, including networks such as NASA's MPLNET and EUMETNET's E-PROFILE network. Simulated coincident measurements from ALI and MPLCAN are performed for two aerosol cases: background stratospheric aerosols and a wildfire smoke layer. It is determined that MPLCAN has the potential to validate ALI measurements from 5-10\,km and extend the vertical coverage of measurements to near the surface. ALI can currently retrieve extinction for specific cases of light wildfire smoke, therefore MPLCAN and ALI could track a wildfire event simultaneously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".