Extreme blocking ridges are associated with large wildfires in England
Bibliographic record
Abstract
Abstract. Persistent positive anomalies in 500 hPa geopotential heights (PPAs) are an event-based paradigm for tracking specific large scale atmospheric patterns that often correspond to blocking events. PPAs are associated with hot, dry surface weather conditions that promote fuel aridity and wildfire activity. We examine the importance of PPA events for surface fire weather across the UK and wildfires in England, a temperate, emerging fire prone region. Surface fire weather is more extreme under PPAs, characterised by reduced precipitation and anomalously high temperatures. Overall, 34 % of England’s burned area and 16 % of all wildfire events occur during or up to five days following the presence of a PPA event. PPAs are generally more strongly associated with wildfire burned area than ignition frequency. The percentage of PPAs associated with wildfire events increases with increasing fire size, with PPAs being associated with half of wildfire events > 500 ha. PPAs are most important for heathland/moorland (40 % burned area) followed by grassland (30 % burned area) wildfires and are more important during the summer wildfire season. Synoptic-scale indicators of wildfire activity like PPAs may improve longer-term fire weather forecasts beyond surface fire weather indices alone, aiding wildfire preparedness and management decision-making. This is particularly important in emerging fire prone regions where wildfire risk is increasing but established tools for assessing fire danger may not yet exist.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".