Quantifying the single scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index
Jiyunting Sun, J. Pepijn Veefkind, P. van Velthoven, P. F. Levelt
- Year
- 2018
- Citations
- 3
- Access
- Open access
Abstract
Abstract. The absorbing aerosol index (AAI) based on the near Ultra-Violet (near-UV) remote sensing techniques is a qualitative parameter that allows to retrieve aerosol optical properties with confidence. In the first part of this study, a series of AAI sensitivity analysis is presented exclusively on biomass burning aerosols. Later on, this study applies a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) and to derive the aerosol single scattering albedo (ω0). The inputs for the radiative transfer calculations are satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the MODerate-resolution Imaging Spectroradiometer (MODIS), and aerosol micro-physical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI observed AAI of this event reached its peak. The Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) failed to capture the evolution of the smoke plume, therefore the aerosol profile is parameterized. The simulated plume ascends to an altitude of 4.5–4.9 km, which is in good agreement with measurements. Due to the relatively small data size of this case, an outlier detection criterion has to be applied. The results show that the AAI simulated by DISAMAR is consistent with observations. The correlation coefficients are over 0.85. The retrieved mean ω0 at 550 nm is approximately 0.84, slightly smaller than the value of 0.90 measured independently by the AERONET instrument. The relative distance between the AERONET site and the plume, the assumption of homogeneous and static plume properties, the lack of the aerosol profile information, and the uncertainties in observations are primarily responsible for this discrepancy. Except for the observational errors, the impact of remaining error sources on ω0 retrieval is difficult to quantify.
Keywords
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