Investigating Over Critical Thresholds of Forest Megafires Danger Conditions in Europe Utilising the ECMWF ERA-Interim Reanalysis
Bibliographic record
Abstract
The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC)\nand the Directorate General for Environment (DG ENV) of the European Commission (EC) to support the services\nin charge of the protection of forests against fires in the EU and neighbour countries, and also to provide the EC\nservices and the European Parliament with information on forest fires in Europe. Within its applications, EFFIS\nprovides current and forecast meteorological fire danger maps up to 6 days. Weather plays a key role in affecting\nwildfire occurrence and behaviour. Meteorological parameters can be used to derive meteorological fire weather\nindices that provide estimations of fire danger level at a given time over a specified area of interest. In this work,\nwe investigate the suitability of critical thresholds of fire danger to provide an early warning for megafires (fires >\n500 ha) over Europe.\nPast trends of fire danger are analysed computing daily fire danger from weather data taken from re-analysis\nfields for a period of 31 years (1980 to 2010). Re-analysis global data sets coming from the construction of\nhigh-quality climate records, which combine past observations collected from many different observing and\nmeasuring platforms, are capable of describing how Fire Danger Indices have evolved over time at a global\nscale. The latest and most updated ERA-Interim dataset of the European Centre for Medium-Range Weather\nForecast (ECMWF) was used to extract meteorological variables needed to compute daily values of the Canadian\nFire Weather Index (CFWI) over Europe, with a horizontal resolution of about 75x75 km. Daily time series of\nCFWI were constructed and analysed over a total of 1,071 European NUTS3 centroids, resulting in a set of\npercentiles and critical thresholds. Such percentiles could be used as thresholds to help fire services establish a\nmeasure of the significance of CFWI outputs as they relate to levels of fire potential, fuel conditions and fire danger.\nMedian percentile values of fire days accumulated over the 31-year period were compared to median val-\nues of all days from that period. As expected, the CWFI time series exhibit different values on fire days than on\nall days. In addition, a percentile analysis was performed in order to determine the behaviour of index values\ncorresponding to fire events falling into the megafire category. This analysis resulted in a set of critical thresholds\nbased on percentiles. By utilising such thresholds, an initial framework of an early warning system has being\nestablished. By lowering the value of any of these thresholds, the number of hits could be increased until all\nextremes were captured (resulting in zero misses). However, in doing so, the number of false alarms tends to\nincrease significantly. Consequently, an optimal trade-off between hits and false alarms has to be established when\nsetting different (critical) CFWI thresholds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".