This study proposed an aerosol characterization process using satellites for severe biomass burning events. In general, these severely hazy cases are labeled as “undecided” or “hazy.” Because atmospheric aerosols are significantly affected by factors such as air quality, global climate change, local environmental risk, and human and biological health, efficient and accurate algorithms for aerosol retrieval are required for global satellite data processing. Our previous classification of aerosol types was based primarily on near-ultraviolet (UV) data, which facilitated subsequent aerosol retrieval. In this study, algorithms for aerosol classification were expanded to events with serious biomass burning aerosols (SBBAs). Once a biomass burning event is identified, the appropriate radiation simulation method can be applied to characterize the SBBAs. The second-generation global imager (SGLI) on board the Japanese mission JAXA/Global Change Observation Mission-Climate contains 19 channels, including red (674 nm) and near-infrared (869 nm) polarization channels with a high resolution of 1 km. Using the large-scale wildfires in Kalimantan, Indonesia in 2019 as an example, the complementarity between the polarization information and the nonpolarized radiance measurements from the SGLI was demonstrated to be effective in radiation simulations for biomass burning aerosol retrieval. The retrieved results were verified using NASA/AERONET ground-based measurements, and then compared against JAXA/SGLI/L2-version-1 products, and JMA/Himawari-8/AHI observations.

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.

Global Change Observation Mission-Climate (GCOM-C) satellite was successfully launched on December 23, 2017, equipped with the multispectral Second-Generation Global Imager (SGLI). The SGLI has 19 channels that encompass the near-UV ($0.380\mu \mathrm{m}$) and violet ($0.412\mu \mathrm{m}$) wavelengths, and two polarization channels in the red ($0.674\mu \mathrm{m}$) and near-IR ($0.869\mu \mathrm{m}$) wavelengths. The motivation for this study was to develop effective aerosol retrieval algorithms by utilizing the features of the GCOM-C/SGLI. The goal was not only to develop these tools for the analysis of the SGLI data but also for future missions.

We investigate heavy haze episodes (with dense concentrations of atmospheric aerosols) occurring around Beijing in June, when serious air pollution was detected by both satellite and ground measurements. Aerosol retrieval is achieved by radiative transfer simulation in an Earth atmosphere model. We solve the radiative transfer problem in the case of haze episodes by successive order of scattering. We conclude that air pollution around Beijing in June is mainly due to increased emissions of anthropogenic aerosols and that carbonaceous aerosols from agriculture biomass burning in Southeast Asia also contribute to pollution.

The atmospheric aerosol distribution in Asia is complex because it is influenced by both natural phenomena and human activities. In this article, multifaceted approach using satellite observations and numerical model simulation shows seasonal and regional variations of the aerosol distribution over Asia. Asia covers a large area; however, this article is limited to East and Southeast Asia. The aerosol optical thickness (AOT) in the inland desert area is normally high in spring and dusty aerosols are widely spread with the wind. The AOT values are high in summer over East Asia. A high AOT caused by biomass burning plumes is observed in autumn over Southeast Asia and in spring over Siberia and heavy air pollution occurs in Beijing in winter, a very well-known phenomenon. These phenomena caused by heavy aerosol concentration were determined based on Aqua/MODIS data and aerosol characteristics retrievals and estimated the aerosol characteristics. Furthermore, the impact of aerosols on the climate assessed by numerical model simulations suggest that aerosols reduce solar radiation and therefore the Earth's surface temperature. In addition to these influences on the radiation budget, aerosols have a significant potential to change the circulation fields.

The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data.