Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy)
Bibliographic record
Abstract
In the summer of 2017, huge wildfires in the British Columbia region (Canada) led to the injection of a remarkably high concentration of biomass burning aerosol in the atmosphere. These aerosol masses reached the city of Naples, Italy, at the end of August 2017, where they were characterized by means of a multiwavelength lidar and a sun–sky–lunar photometer. Here we report on the optical and microphysical properties of this aerosol in an intriguing condition, occurring on 4 September 2017, which is characterized by an interesting multi-layered vertical distribution of the aerosol. The Lidar profiles highlighted the presence of four aerosol layers, with two located in the lower troposphere and the other two at stratospheric altitudes. A rather thorough characterization of the biomass burning aerosol was carried out. The aerosol depolarization ratio showed an increasing dependence on the altitude with averaged values of 2–4% for the tropospheric layers, which are indicative of almost spherical smoke particles, and larger values in the stratospheric layers, suggestive of aspheric particles. Lidar-derived size distributions were retrieved for the first three aerosol layers, highlighting a higher particle concentration in the fine-mode fraction for the layers observed at higher altitudes. A dominance of fine particles in the atmosphere (fine-mode fraction > 0.8) with low absorption properties (absorption AOD < 0.0025 and SSA > 0.97) was also observed over the whole atmospheric column by sun photometer data. The space-resolved results provided by the lidar data are consistent with the columnar features retrieved by the AERONET sun photometer, thus evidencing the reliability and capability of lidar characterization of atmospheric aerosol in a very interesting condition of multiple aerosol layers originating from Canadian fires overpassing the observation station.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".