Taylor Mchone

Date of Award

August 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Jared Marquis

Abstract

An unprecedented number of wildfires occurred across the Canadian mainland during May and June 2023 due to unusually hot and dry atmospheric conditions. As a result of these wildfires, smoke was transported over large areas of North America, posing significant threats to visibility, air quality, human health, and the economy. The global Navy Aerosol Analysis and Prediction System (NAAPS) model was the first model of its kind to predict global aerosol transport, including, among others, wildfire smoke. Operationally, NAAPS produces predictions of the horizontal and vertical distribution of smoke and how it diffuses through space and time. This study evaluates the performance of NAAPS by comparing vertically integrated aerosol optical depth (AOD) and vertical profiles of aerosol extinction with ground-based remote sensing observations from the Aerosol Robotic Network (AERONET) and Micro-Pulse Lidar Network (MPLNET) at London, Ontario, NASA Goddard Space Flight Center (GSFC), and Appalachian State. The operational (OPS) and research (near-real-time; NRT) versions of NAAPS are analyzed at the analysis time for five separate events affecting the three locations.

NAAPS captures the overall timing of aerosol loading at both sites, as indicated by peaks in AOD. There are instances where the timing of NAAPS differs from observations, however these instances are inconsistent and do not indicate that the model has a time bias. For all five events, average AOD measured by AERONET are 0.62, 0.72, and 0.42 at London-CDN, GSFC, and Appalachian\_State, respectively. The average AODs from NRT (OPS) NAAPS are 0.51, 0.54, and 0.42 (0.69, 0.74, and 0.49), respectively. When considering the event composite average for all three sites, NAAPS is able to reproduce the observed AOD to within ~20\%. However, for individual events, both versions of NAAPS can severely underestimate AOD during times of heavy aerosol loading by as much as ~80\%. On average, NAAPS fails to predict layers of smoke aloft and often overestimates aerosol extinction values near the surface.

Given the inherent complexity of models like NAAPS, initial and boundary conditions are critical for yielding skillful predictions of aerosol transport; for example, accurate depictions of aerosol source regions and, in particular for smoke, injection height. The results of this study provide key contextual answers about how aerosol transport models can be improved.

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