Date of Award:

12-2024

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Physics

Committee Chair(s)

Titus Yuan

Committee

Titus Yuan

Committee

Pierre-Dominique Pautet

Committee

JR Dennison

Abstract

Carbon dioxide is one of the main greenhouse gasses that contributes to maintaining Earth’s warm global temperature in its atmosphere. Recent studies have indicated that this cooling trend has been determined to vary between less than 1 K/decade or 2 K/decade. However, these results have been determined using data that have been obtained using nighttime lidar observations and potentially do not give the complete picture on how the cooling trend is behaving. The main goal of this research is to understand the behavior of the linear temperature trend between the daytime and nighttime profiles using a 16-year dataset (2002-2017), when the sodium (Na) doppler lidar at Colorado State University, later located to Utah State University (2010), was conducting routine full diurnal cycle observations. A customized multi-linear regression (MLR) algorithm can be utilized to determine the linear temperature trends for either nighttime or daytime data sets. One main concern when utilizing the MLR for both types of lidar temperature profiles is the uncertainties in daytime profiles are shown to be higher compared to nighttime profiles. To counter this, two mathematical averaging methods were applied to the daytime and nighttime profiles to achieve a daily mean with its uncertainty. The first method uses an equal weighting average, and the second method is an inverse variance weighting (IVW) method. Applying both methods to calculate the daily mean temperatures, which are adopted into the MLR model fit, provided results for the optimized parameters and their fitting uncertainties.

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