Impact of Smoke Aerosol Loading on Lightning Characteristics of Pyrocumulonimbus Compared With Other High‐Based Thunderstorms
Bibliographic record
Abstract
Abstract Pyrocumulonimbus (pyroCb) is a form of deep convection that is generated by the heating from large wildfires and specific meteorology known for producing lightning. We study the lightning characteristics of five pyroCb events in British Columbia, Canada, from June 29 to July 1 of 2021, and compare them to other clean and smoke‐filled high‐based thunderstorms in the same region and season using ground‐based lightning detection data, satellite retrievals, meteorological and atmospheric composition reanalysis, and observed thermodynamic profiles. One large pyroCb event over the Sparks Lake fire that generated persistent overshooting tops had a remarkable amount of lightning activity, with 5,600 total lightning strikes, while the rest of the pyroCb events corresponded with lower injection altitudes and minimal to no observed lightning activity. The cloud‐to‐cloud (CC) to cloud‐to‐ground (CG) lightning ratio (CC:CG) in this Sparks Lake pyroCb was significantly higher than in other high‐based storms but displayed similar lightning density and slightly lower peak current distributions. All clean and smoke‐filled thunderstorms produced significant levels of lightning activity, regardless of their cloud‐top altitudes. However, ingestion of smoke significantly reduced the percentage of positive polarity CG strikes when compared to clean cases. These results set a reference for identifying the characteristics of pyrogenic lightning and improved predictions of lightning‐caused fire ignitions, which will aid in understanding pyroCb activity and related impacts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".