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<p>This study presents an innovative approach to forecasting seasonal anomalies in burned areas (BA) by integrating process-based seasonal prediction and a random forest climate-fire model. The Standardized Precipitation Index (SPI), derived from observed precipitation, allows us to predict burned area anomalies a month before the start of the target fire season in ~68% of the burnable area. When utilizing seasonal predictions, the system maintains skillful results in ~46% of the burnable area. Given the availability of observational and forecast data in near-real-time, a prototype operational forecast for burned areas could be provided to enhance climate services.</p>

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Enhancing seasonal fire predictions with hybrid dynamical and random forest models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月25日

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  • Miguel Ángel Torres-Vázquez,
  • Sixto Herrera,
  • Andrina Gincheva,
  • Amar Halifa-Marín,
  • Leone Cavicchia,
  • Francesca Di Giuseppe,
  • Juan Pedro Montávez,
  • Marco Turco

摘要

This study presents an innovative approach to forecasting seasonal anomalies in burned areas (BA) by integrating process-based seasonal prediction and a random forest climate-fire model. The Standardized Precipitation Index (SPI), derived from observed precipitation, allows us to predict burned area anomalies a month before the start of the target fire season in ~68% of the burnable area. When utilizing seasonal predictions, the system maintains skillful results in ~46% of the burnable area. Given the availability of observational and forecast data in near-real-time, a prototype operational forecast for burned areas could be provided to enhance climate services.