Are fire probabilistic products an effective early warning tool in the management of prevention fire activities? – the case of Monchique 2018 wildfire.
Bibliographic record
Abstract
At the beginning of August 2018 Portugal experienced extreme fire prone meteorological conditions with very hot and dry air, driven by the occurrence of a severe fire event in Southern Portugal, noun as Monchique wildfire. The severe wildfire probability occurrence was re-enhanced by the substantial fuel amount accumulated since the last extreme wildfire occurred over this region in August 2003. On the 2nd August 2018, extreme fire danger conditions were predicted for Monchique region and the fire started on the 3rd and lasting till the 10th of August, with the evacuation of people from several villages and the associated burnt area of 27000 ha (ICNF, https://www.icnf.pt/). This event posed hard challenges on suppression activities due to its exceptional severity, related to high values of fire radiative energy released. This work aims to study the driving factors of Monchique wildfire in 2018 and assessing the usefulness of fire probabilistic products disseminated up to 72 hours in advance, as an early warning tool in fire prevention and suppression activities. The assessment of fire danger conditions was done based on ensemble forecasts fire products of the Ensemble Prediction System (EPS), provided by Copernicus Atmosphere Monitoring Service (CAMS); and based on fire danger metrics produced by Copernicus Emergency Management Service (CEMS) for the European Forest Fire Information System (EFFIS). Fire Weather Index (FWI) and Fine Fuel Moisture Code (FFMC) were selected from the Canadian Forest Fire Weather Indices System (CFFWIS) to describe the meteorological fire danger of Monchique event. The assessment of fire severity was based on the Fire Radiative Energy (FRE) released by the fire, computed from the Fire Radiative Power (FRP) product delivered in near real-time by EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) (https://landsaf.ipma.pt/en). FWI and FFMC ensemble results based on CAMS dataset, 24 hours before the ignition, showed Monchique region above the 95th percentile of the ensemble, with ensemble maximum values, for both indices, being achieved on the period 6th-9th August 2018. FWI and FFMC, obtained from ERA5 data, registered the highest daily anomalies on the 3rd August 2018, recording values that are classified from very high to the extreme over Monchique region. The fire severity/intensity assessment based on the FRE product showed very high amounts of energy released during this fire event, daily maximum amounts of 10000 MW during 5th -8th August. Total FRP (MW) and FRE (GJ) values accumulated per pixel over the duration of the event achieved maximum values of 7x104 and 6x104, respectively, in certain pixels, illustrating the severity of this event and the hard challenge that was developed on suppression activities by Portuguese authorities. Therefore, obtained results show that selected products were able to properly assess fire danger and fire severity for Monchique region over those days. Acknowledgments: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT and was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".