Document


Title

Characterization of wildfire smoke over complex terrain using satellite observations, ground-based observations, and meteorological models
Document Type: Journal Article
Author(s): Makiko Nakata; Itaru Sano; Sonoyo Mukai; Alexander Kokhanovsky
Publication Year: 2022

Cataloging Information

Keyword(s):
  • AERONET - Aerosol Robotic Network
  • GCOM - Global Change Observation Mission-Climate
  • polarizations
  • radiative transfer models
  • SCALE - Scalable Computing for Advanced Library and Environment
  • SGLI - Second-generation Global Imager
Record Maintained By:
Record Last Modified: May 15, 2022
FRAMES Record Number: 65873

Description

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.

Online Link(s):
Citation:
Nakata, Makiko; Sano, Itaru; Mukai, Sonoyo; Kokhanovsky, Alexander. 2022. Characterization of wildfire smoke over complex terrain using satellite observations, ground-based observations, and meteorological models. Remote Sensing 14(10):2344.