Understanding the critical elements of the pyrocumulonimbus storm sparked by high-intensity wildland fire
Bibliographic record
Abstract
Abstract High-intensity wildland fires can produce extreme flaming and smoke emissions that develop into a fire-cloud chimney, reaching into the upper troposphere or lower stratosphere. Termed pyrocumulonimbus, these storms are both conventional and counterintuitive. They have been observed to produce lightning, hail, downdraft wind hazards, and tornadoes as expected with severe convective storms, but counterintuitively, they are not associated with significant precipitation. Pyrocumulonimbus storms have been noticed outside wildfire expert circles following Australia’s Black Summer in 2019/20, and have since repeatedly made headlines in the United States. However, much is unknown about their behavior, energetics, history, and impact on the Earth/atmosphere system. We address several questions and science challenges related to these unknowns. Our worldwide record of pyrocumulonimbus events from 2013 to 2021 shows that the phenomenon is neither new nor rare. Despite high occurrences in 2019 and 2021, these data do not support identification of a trend. Future studies require an expansive record of pyrocumulonimbus occurrence globally and regionally, both historically and continuously forward in time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".