Characterizing Wildfire Danger in Italy: The Added Value of High-Resolution Reanalyses
Notice bibliographique
Résumé
Wildfires are a critical threat to both people and infrastructures. Although most wildfires in Italy are human-caused, their ignition and propagation are strongly influenced by wildfire-prone meteorological conditions, such as droughts, heatwaves, and strong winds, which are projected to increase in both severity and frequency in the coming decades due to ongoing climate change.To effectively prevent wildfires and to forecast wildfire risk over a territory, it is essential to understand the meteorological situation in which they have ignited and developed in the past. In this work, we focus on calculating the meteorological wildfire danger through the Canadian Fire Weather Index (FWI) over two high resolution reanalyses for Italy, MERIDA HRES and MERIDA HRES OI.The FWI represents an estimate of the meteorological wildfire danger of an area, combining 2m temperature, 2m relative humidity, 10m wind speed, and total rainfall fields; therefore, the more accurate the meteorological inputs are, the more accurate the FWI becomes. Meteorological reanalyses represent the most reliable source for such inputs, as they integrate observational data with numerical weather prediction models. This approach enables the detailed reconstruction of past weather conditions over extensive territories, including areas lacking direct observational dataIn this context, we have investigated the added value of higher resolution reanalyses by comparing FWI computed over the coarser ERA5 reanalysis with the higher resolution MERIDA HRES and MERIDA HRES OI reanalyses. These two reanalyses, which use ERA5 as a meteorological driver, are downscaled through the WRF-ARW model with parametrizations specifically tailored to the complex geography of the Italian territory. MERIDA HRES covers the period from 1986 to 2021, while MERIDA HRES OI spans 2005 to 2021, integrating observational data for enhanced accuracy.The comparison has been carried out through the analysis of several case studies and through the analysis of the datasets’ performances over all the wildfires that happened over Italy in the past decade, as well as through considerations over FWI climatological trends. While ERA5 is a robust and extensively validated resource, its coarser resolution poses limitations in accurately capturing the complex topography and local climatic variations of the Italian landscape. The MERIDA HRES datasets, with their finer resolution, consistently outperformed ERA5 in these scenarios, highlighting their added value for applications requiring detailed, high-resolution meteorological data.In conclusion, MERIDA HRES and MERIDA HRES OI offer valuable tools for improving the characterization of wildfire danger across Italy, benefiting from their higher spatial resolution and parametrization specific for the Italian territory. These datasets contribute to a deeper understanding of the meteorological conditions associated with wildfire danger and provide robust resources for studying climatological trends. Additionally, they support a wide range of stakeholders by aiding in the development of more effective risk management and mitigation strategies in response to the growing threat of wildfires.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».