Author

Aanan Schlief

Date of Award

May 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Jianglong Zhang

Abstract

Atmospheric aerosols play an important part in maintaining and altering Earth’s radiation balance and often pose a threat to human health. For this reason, atmospheric aerosols have been studied, typically, during daytime hours, through the use of space-borne instruments and ground-based observations. Currently, however, there are no operational nighttime aerosol retrieval methods available from passive-based satellite observations. The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, which is aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite, includes a Day/Night Band (DNB) that is sensitive to faint light signals at nighttime opening the door for nighttime aerosol studies. Using observations from the VIIRS DNB for 2018, two different nighttime aerosol retrieval methods were evaluated and inter-compared in this study. The first approach is a lunar reflectance-based retrieval method, which retrieves aerosol optical depth values using moonlight. The second approach used in this study is a point-based retrieval method utilizing changes in light patterns over regions with artificial light sources. Dakar, Senegal was selected for the target artificial light source location, as Dakar is frequently polluted by dust aerosols throughout the year. Retrievals from both approaches were evaluated against the ground-based Level 1.5 Lunar Provisional AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) data from Dakar, Senegal. Daytime AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) were also used for verification. The study suggests that while both retrieval methods show skills in retrieving nighttime AOD, cloud contamination and variations in lunar properties are factors that need to be carefully quantified in future studies for accurate nighttime aerosol retrievals using VIIRS DNB data.

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