Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the UK
Bibliographic record
Abstract
Abstract. Wildfires in the United Kingdom (UK) can pose a threat to people, infrastructure and the natural environment (e.g. to the carbon in peat soils), and their simultaneous occurrence within and across UK regions can periodically place considerable stress upon the resources of Fire and Rescue Services. "Fire danger" rating systems (FDRS) attempt to anticipate periods of heightened fire risk, primarily for early-warning purposes. The UK FDRS, termed the Met Office Fire Severity Index (MOFSI) is based on the Fire Weather Index (FWI) component of the Canadian Forest FWI System. MOFSI currently provides operational mapping of landscape fire danger across England and Wales using a simple thresholding of the final FWI component of the Canadian System. Here we explore a climatology of the full set of FWI System components across the entire UK (i.e. extending to Scotland and Northern Ireland), calculated from daily 2 km gridded numerical weather prediction data, supplemented by meteorological station observations. We used this to develop a percentile-based calibration of the FWI System optimised for UK conditions. We find the calibration to be well justified, since for example the values of the "raw" uncalibrated FWI components corresponding to a very "extreme" (99th percentile) fire danger situation can vary by up to an order of magnitude across UK regions. Therefore, simple thresholding of the uncalibrated component values (as is currently applied) may be prone to large errors of omission and commission with respect to identifying periods of significantly elevated fire danger compared to "routine" variability. We evaluate our calibrated approach to UK fire danger rating against records of wildfire occurrence, and find that the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and final FWI component of the FWI system generally have the greatest predictive skill for landscape fires in Great Britain, with performance varying seasonally and by land cover type. At the height of the most recent severe wildfire period in the UK (2 May 2011), 50 % of all wildfires occurred in areas where the FWI component exceeded the 99th percentile, and for each of the ten most serious wildfire events that occurred in the 2010–2012 period, at least one FWI component per event was found to surpass the 95th percentile. Overall, we demonstrate the significant advantages of using a calibrated, percentile-based approach for classifying UK fire danger, and believe our findings provide useful insights for any future redevelopment of the current operational UK FDRS.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".