Document Type

Thesis

Date of Degree Completion

Spring 2024

Degree Name

Master of Science (MS)

Department

Geological Sciences

Committee Chair

Walter Szeliga

Second Committee Member

Timothy Melbourne

Third Committee Member

Darci Snowden

Abstract

Atmospheric lee waves, also known as mountain waves, are a type of gravity wave that form as air that is forced over a mountain creates turbulence downstream. Trapped, or stationary, lee waves located directly over a Global Navigation Satellite System (GNSS) receiver on Earth’s surface appear to lead to anomalies in the receiver’s position estimate, usually skewed toward the neighboring mountain range. The exact mechanism by which trapped lee waves might cause these anomalies is not known, and so my research aims to understand this. GNSS station P612 located in the lee of the San Bernardino Mountains in southern California records positively-skewed north position time series data anomalies. Using 300 hPa upper-level air maps, I found that anomalous days tend to share an atmospheric configuration related to the jet stream forming a positively tilted trough which likely interacts with the topography of the San Bernardino Mountains to create trapped lee waves.

I use the Advanced Weather Research & Forecasting (WRF-ARW) Model V4.4 with one nested domain and initial and hourly boundary conditions from the NOAA HRRR 3 km model to simulate the atmospheres on anomalous days during boreal winter and spring of 2018-2019, where I model anomalous days which exhibit trapped lee wave events and the day prior for comparison to a ‘normal’ atmosphere. I primarily use the Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) scheme and explore four other PBL schemes or damping parameters on two days with large anomalies, 22 January and 23 April 2019. I compared three diagnostics from the model output to three local weather stations to validate the model, and found the output acceptable. From vertical cross-section animations of the wave events, I found a weakly (0 < R2 < 0.3) positive correlation to the duration and approximate downstream length of the trapped lee waves. I also ray traced through the output using the KARAT program, and compared the refractivity and delay output between days and parameters to understand the structure of the lee waves. I found the refractivity of a given trace can detect the presence of lee waves while the delay cannot.

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