Identifying Coherent Motions in Near-Surface Turbulence: Microfronts with Subsequent Quiescent Periods in Temperature Time Series

Open Access
- Author:
- Anderson, Alex
- Graduate Program:
- Meteorology and Atmospheric Science
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- May 31, 2023
- Committee Members:
- David Stensrud, Program Head/Chair
Ying Pan, Thesis Advisor/Co-Advisor
Kenneth James Davis, Committee Member
Scott James Richardson, Committee Member - Keywords:
- microfront
near-surface turbulence
coherent motions
coherent structures
quiescent periods
temperature burst event - Abstract:
- Near-surface turbulence is responsible for around 70% of the fluxes of heat, momentum, and mass between Earth’s surface and its atmosphere and provides important boundary conditions for atmospheric dynamics. To improve the modeling of this turbulence and the fluxes it carries, it is important to understand the microscale coherent structures and associated microfronts of the turbulent motions. In the presence of mean shear, the turbulent mixing of air with different momentum causes horizontal convergence and combines with existing temperature stratification to create temperature microfronts in time series. In this work, temperature microfronts are identified using sonic anemometer data ranging from 10 to 29 meters above the surface during turbulent periods from the Canopy Horizontal Array Turbulence Study (CHATS) from March to June 2007. Each temperature microfront is characterized by a sharp temperature drop following a gradual temperature increase. Temperature and time scales derived from the temperature standard deviation and integral time scale are used to develop new identification criteria of temperature microfronts that are more data dependent and objective. The temperature time series is combined with an analysis of quiescent periods and the velocity components to identify the passage of individual temperature burst events. Using these criteria, 44 initial events were identified and categorized further into three categories. The new criteria are successful in refining microfront characteristics and automating the identification process to objectively extract individual burst events.