Assessing meteorological fire danger over Europe based on a statistical model of satellite-derived fire radiative power
Bibliographic record
Abstract
The Satellite Application Facility for Land Surface Analysis (LSA SAF), that is part of EUMETSAT’s ground segment, operationally disseminates daily forecasts of meteorological fire danger over Mediterranean Europe. The so-called Fire Risk Map (FRM) product relies on estimates of the probability of exceedance of predefined thresholds of daily released energy by active fires as derived from a Generalized Pareto model that uses FWI as covariate for the scale parameter; FWI, the Fire Weather Index (FWI), is part of the Canadian Fire Weather Index System and has proven to be very suitable to rate fire danger over Europe.The aim of this study is to extend the procedure to Northern and Central Europe making use of a statistical model of Fire Radiative Power (FRP) as derived from MODIS observations over Europe covering the period 2000-2022. Following the approach developed by DaCamara et al. (2023), the statistical model consists of an 8 parameter, doubly truncated lognormal body distribution with generalized Pareto tails, using FWI as a covariate of its parameters.First, Europe is divided into eight regions according to the recorded number of hotspots and to the averaged FRP over the study period. Each one of those regions is then stratified into three subregions according to the dominant land cover type, i.e., forest, shrub, or agriculture. For each of the subregions, a statistical model is fitted to the sample of historical records of FRP together with the associated sample of FWI values obtained from the Copernicus Emergency Management Service.The fitted models are then applied to Europe to generate monthly climatological values of probability of exceedance of prescribed thresholds of FRP. This information is used to define appropriate limits for classes of fire danger (i.e. low, moderate, high, very high and extreme) for each subregion of Europe. Finally, these classes are validated by analyzing the distribution of recorded FRP among the classes and by examining maps for extreme fire events. This work was supported by EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) and by Instituto Dom Luiz (IDL), a research unit financed with national funds (PIDDAC) by FCT (UIDB/50019/2020). References:DaCamara, C. C., Libonati, R., Nunes, S. A., de Zea Bermudez, P., & Pereira, J. M. C. (2023). Global-scale statistical modelling of the radiative power released by vegetation fires using a doubly truncated lognormal body distribution with generalized Pareto tails. Physica A: Statistical Mechanics and Its Applications, 625. https://doi.org/10.1016/j.physa.2023.129049
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".