Use of fire danger seasonal forecasts to support fire prevention management in Attica Greece
Bibliographic record
Abstract
Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean Europe. Weather and climate are among the main factors influencing wildfire potential. As fire danger is expected to increase under changing climatic conditions, seasonal forecasting of weather conditions conducive to fires is of paramount importance for implementing effective fire prevention policies. The aim of the current study is to provide high resolution (~9km) probabilistic seasonal fire danger forecasts, utilizing the Canadian Fire Weather Index (FWI) for Attica region, one of the most fire prone regions in Greece. Furthermore, the study aims to assess the ability of probabilistic FWI seasonal forecasts to provide robust information and support management decisions by comparing hindcast years of above normal fire danger conditions with historical fire occurrence data. Towards this aim, the fifth generation of the ECMWF seasonal forecasting system (SEAS5) (Johnson et al. 2019) hindcasts for the period 1993 to 2016 available in C3S Climate Data Store are utilized. The variables to calculate daily FWI values include instantaneous outputs at 12 UTC for 2-meter temperature, northward and eastward near-surface wind components, 2-m dewpoint temperature as well as daily accumulated precipitation. In order to statistically downscale and verify FWI seasonal forecasts, the state-of-the-art global reanalysis dataset ERA5-Land (Muñoz-Sabater 2019) of Copernicus CDS is used. The verification of the FWI (including its sub-components) re-forecasts was performed using adequate probabilistic verification measures of skill and reliability. Preliminary results indicate that FWI as well as its Initial Spread Index (ISI) sub-component, present statistically significant (95% confidence interval) high skill scores for Attica and are proven respectively, “marginally useful” and “perfectly reliable” in predicting above normal fire danger conditions. When comparing year-by-year the SEAS5 FWI predictions with the historical fire occurrence as obtained by the Hellenic Fire Service database, both FWI and ISI forecasts indicate a skill in identifying years with high fire occurrences. Overall, fire danger and its subcomponents can potentially be exploited by regional authorities in fire prevention management regarding preparedness and resources allocation in the Attica Region.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".