A new probabilistic method to identify fire-igniting lightning events
Notice bibliographique
Résumé
Lightning-induced ignitions play a major role shaping the frequency, patterns and characteristics of wildfires in several regions across the globe, including extreme wildfire events (e.g., Góis wildfire in 2017 in Portugal) and fire seasons, such as 2019-20 in Australia, 2020 in California, and 2023 in Canada. The attention to lightning-ignited wildfires has been growing in recent years. Studies on LIWs frequently associate lightning and wildfire data to discern or approximate the place and moment of fire ignition. This typically requires to select the lightning strike responsible for the ignition.Currently, several methods are applied to select the most likely lightning strike causing the ignition. However, this selection is complicated by, at least, two aspects. First, the spatial uncertainty of fire and lightning data (e.g., the location errors of detected lightning events). Second, the holdover phenomenon. Holdover time, commonly defined as the time between lightning-induced fire ignition and fire detection, can range from a few minutes to several days, and more rarely to some weeks or even months. Long holdover times are associated to the presence of a smoldering phase that hinders the detection of these lightning fires.Here, we present a novel method that uses location accuracy information from lightning location networks, as well as expected distributions of holdover time, to assess the probabilities of lightning igniting wildfires. Our method computes a probability metric, which is the product of two independent probabilities: a spatial and a temporal probability. The spatial component assesses the probability of a cloud-to-ground lightning event striking within a given area surrounding the fire discovery point, while the temporal component evaluates the probability of a lightning-ignited wildfire undergoing a certain holdover time. The lightning event with the maximum probability metric value is then selected as the most likely ignition source. We applied this method in three study areas: Switzerland, Catalonia (Spain), and California and Nevada (USA). The results were compared with lightning selections identified by the index of proximity, one of the currently most common methods to select the most likely ignition source of lightning-induced wildfires.The initial results indicate that the probability metric yields a different selection of lightning events, in comparison with the index of proximity, for a great proportion of wildfires, with considerable differences across the study areas. We suggest that the probability metric provides a solid alternative to current methods. The probability metric offers some advantages: (1) it simplifies some methodological decisions despite the need for additional computations; (2) it is flexible and can be adapted to different types of lightning and fire data (e.g., fire perimeters); (3) it has a more robust theoretical basis than current methods; and (4) the lightning selection can be enhanced over time due to continuous improvements in lightning and fire databases.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».