Characterization of Wildfire Smoke over Complex Terrain Using Satellite Observations, Ground-Based Observations, and Meteorological Models

DOI Web Site 研究データあり 被引用文献3件 参考文献42件 オープンアクセス
  • Makiko Nakata
    Faculty of Applied Sociology, Kindai University, Higashiosaka 577-8502, Japan
  • Itaru Sano
    Faculty of Informatics, Kindai University, Higashiosaka 577-8502, Japan
  • Sonoyo Mukai
    School of Applied Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto 606-8225, Japan
  • Alexander Kokhanovsky
    Brockmann Consult GmbH, 21029 Hamburg, Germany

書誌事項

公開日
2022-05-12
資源種別
journal article
権利情報
  • https://creativecommons.org/licenses/by/4.0/
DOI
  • 10.3390/rs14102344
公開者
MDPI AG

説明

<jats:p>The severity of wildfires is increasing globally. In this study, we used data from the Global Change Observation Mission-Climate/Second-generation Global Imager (GCOM-C/SGLI) to characterize the biomass burning aerosols that are generated by large-scale wildfires. We used data from the September 2020 wildfires in western North America. The target area had a complex topography, comprising a basin among high mountains along a coastal region. The SGLI was essential for dealing with the complex topographical changes in terrain that we encountered, as it contains 19 polarization channels ranging from near ultraviolet (380 nm and 412 nm) to thermal infrared (red at 674 nm and near-infrared at 869 nm) and has a fine spatial resolution (1 km). The SGLI also proved to be efficient in the radiative transfer simulations of severe wildfires through the mutual use of polarization and radiance. We used a regional numerical model SCALE (Scalable Computing for Advanced Library and Environment) to account for variations in meteorological conditions and/or topography. Ground-based aerosol measurements in the target area were sourced from the National Aeronautics and Space Administration-Aerosol Robotic Network; currently, official satellite products typically do not provide the aerosol properties for very optically thick cases of wildfires. This paper used satellite observations, ground-based observations, and a meteorological model to define an algorithm for retrieving the aerosol properties caused by severe wildfire events.</jats:p>

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