Date of Award

May 2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Atmospheric Sciences

First Advisor

Jianglong Zhang

Abstract

Smoke aerosols arise from a variety of regional sources and fuel types dependent on the properties of the fire, leading to spatial variability in smoke composition and optical properties. After emission, these aerosols age and mix in the atmosphere with other aerosol species, such as sulfates, altering the optical and microphysical properties of the smoke aerosols over time. Further, smoke particles originating from biomass burning events are typically assumed to be spherical, yet non-spherical smoke particles are also reported from in situ observations. Thus, lidar ratio (extinction to backscatter ratio) and depolarization ratio exhibit spatiotemporal variability for smoke. In this study, a signal loss method for simultaneous retrievals of both layer-averaged lidar ratio and particulate extinction is applied on a dataset of smoke plumes sampled by NASA’s Cloud Physics Lidar (CPL) during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. The findings suggest lidar ratio is likely to be regionally specific and evolve with plume transport. Thus, innovative methods for simultaneous retrieval of lidar ratio and aerosol extinction, such as the signal loss method highlighted in this study, are needed for accurate aerosol retrievals from standard backscatter lidars in the future. Then, using NASA’s Cloud Aerosol Transport System (CATS) lidar data during the biomass burning season over Africa and South America from 2015-2017, the frequency and distribution of non-spherical smoke particles are analyzed. A newly developed supplemental smoke aerosol typing algorithm was used to classify smoke aerosol layers which could otherwise be misclassified as dust or dust mixture using the CATS standard aerosol typing algorithm. Approximately 30% of smoke layers over Africa and South America are non-spherical (depolarization ratio >0.1) and align with dry biomes of low soil moisture values. Conversely, spherical smoke layers (depolarization ratio <0.1) are in moist regions. In the final phase of this analysis, the smoke aerosol lidar ratio and aerosol optical depth (AOD) are examined for regions of spherical and non-spherical smoke to understand the impacts of smoke sphericity on active and passive sensor retrievals. CATS 1064 nm lidar ratio are investigated as a function of optical and meteorological parameters and a dependance of spherical smoke lidar ratio on relative humidity is presented. Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target and Aerosol Robotic Network (AERONET) AOD retrievals are compared in of spherical and non-spherical smoke , where overall MODIS retrievals are found to be low biased likely due to ill-assumed single scatter albedo values.

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