PhD Proposal: Roshan Mishra
Location
Physics : 401
Date & Time
August 11, 2023, 12:00 pm – 2:00 pm
Description
ADVISOR: Dr. Zhibo Zhang
TITLE: Retrieving Spectral Aerosol Absorption of Rapidly Evolving Smoke Using Critical Reflectance Method with Geostationary Observations
ABSTRACT: North America has seen a concerning rise in the frequency, size, and duration of wildfires, leading to significant impacts on the society and environment partly due to the emissions of smoke and associated issues at local to global scales. The climate and environmental impacts of smoke depends on various smoke properties influenced by factors like fuel source, atmospheric conditions, and chemical changes, while smoke aerosols rapidly change in particle properties as they age through processes such as coagulation, condensation, and gas-to-particle conversion, occurring from minutes to hours. Accurate measurement of smoke absorption properties is crucial for accurately quantifying aerosol impacts on Earth’s radiation budget and faithful air quality forecasting. Geostationary satellites like the GOES-R series with Advanced Baseline Imager (ABI) onboard have better temporal, spatial, and spectral capabilities to retrieve fast-changing smoke absorption properties. Using the powerful remote sensing technique, critical reflectance (CR) on the GOES ABI dataset, we now can retrieve wavelength-dependent absorption every 5 minutes over the Continental United States (CONUS) region. CR refers to the unchanging top of the atmosphere (TOA) reflectance over a surface, regardless of the addition or removal of aerosol loading. CR can be applied to high variable surfaces and is spectrally independent, providing a direct correspondence with aerosol Single Scattering Albedo (SSA). Using AOD-dependent aged smoke models, we have employed the CR method to retrieve smoke SSA for 8 hours of daylight at three handpicked AERONET sites, where the differences between CR-retrieved SSA and AERONET-retrieved SSA mostly within 0.01. A sensitivity study is conducted to analyze the uncertainties involved in CR calculation and SSA retrieval.
TITLE: Retrieving Spectral Aerosol Absorption of Rapidly Evolving Smoke Using Critical Reflectance Method with Geostationary Observations
ABSTRACT: North America has seen a concerning rise in the frequency, size, and duration of wildfires, leading to significant impacts on the society and environment partly due to the emissions of smoke and associated issues at local to global scales. The climate and environmental impacts of smoke depends on various smoke properties influenced by factors like fuel source, atmospheric conditions, and chemical changes, while smoke aerosols rapidly change in particle properties as they age through processes such as coagulation, condensation, and gas-to-particle conversion, occurring from minutes to hours. Accurate measurement of smoke absorption properties is crucial for accurately quantifying aerosol impacts on Earth’s radiation budget and faithful air quality forecasting. Geostationary satellites like the GOES-R series with Advanced Baseline Imager (ABI) onboard have better temporal, spatial, and spectral capabilities to retrieve fast-changing smoke absorption properties. Using the powerful remote sensing technique, critical reflectance (CR) on the GOES ABI dataset, we now can retrieve wavelength-dependent absorption every 5 minutes over the Continental United States (CONUS) region. CR refers to the unchanging top of the atmosphere (TOA) reflectance over a surface, regardless of the addition or removal of aerosol loading. CR can be applied to high variable surfaces and is spectrally independent, providing a direct correspondence with aerosol Single Scattering Albedo (SSA). Using AOD-dependent aged smoke models, we have employed the CR method to retrieve smoke SSA for 8 hours of daylight at three handpicked AERONET sites, where the differences between CR-retrieved SSA and AERONET-retrieved SSA mostly within 0.01. A sensitivity study is conducted to analyze the uncertainties involved in CR calculation and SSA retrieval.
