Tropospheric and stratospheric wildfire smoke profiling with lidar:Mass, surface area, CCN and INP retrieval
Bibliographic record
Abstract
Abstract. We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, and surface area concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nucleus (CCN) and ice-nucleating particle (INP) concentrations from single-wavelength backscatter lidar measurements at ground and in space. A central role in the data analysis play conversion factors to convert the measured optical into microphysical properties. The set of needed conversion parameters for wildfire smoke are derived from AERONET observations of major smoke events caused by record-breaking wildfires in western Canada in August 2017 and southeastern Australia in January–February 2020. The new smoke analysis scheme is applied to stratospheric CALIPSO observations of fresh smoke plumes over northern Canada in 2017 and New Zealand in January 2020 and to ground-based lidar observation in southern Chile in aged Australian smoke layers in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in large detail and thus to provide valuable information for climate-, cloud-, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system.
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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 | 0.000 | 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".