Doctoral Dissertation Research: Spatial Structure of Turbulent Flows in the Atmospheric Boundary Layer

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This doctoral dissertation project will investigate optimal spatial sampling of important variables in the atmospheric boundary layer (ABL). This layer is of considerable importance for local weather development and turbulence prediction, yet it is one of the most difficult portions of the atmosphere to sample using conventional measurement technologies. The doctoral student will use small unmanned aircraft systems (sUAS), a promising technology that has emerged to fill sampling gaps in the ABL and improve the understanding of local weather dynamics. This research will advance knowledge by identifying appropriate spatial sampling scales for temperature and relative humidity variables in the ABL using the new sUAS technology. Working closely with the meteorologist-in-charge at the National Weather Service office in Tulsa, profiles of atmospheric variables captured with the sUAS will be shared in real-time with weather scientists for inclusion in immediate forecast models. Results will also be presented to national and international audiences through peer-reviewed publications and conference presentations. As a Doctoral Dissertation Research Improvement award, this project will provide support to enable a promising student to establish an independent research career.This research will contribute to a better understanding of small-scale turbulence in the ABL, which is of critical importance in meteorology, by analyzing the scales at which these variables are spatially similar (autocorrelated). Building on theories of small scale turbulence along with established spatial analytical methods from geography and the spatial sciences, this research will develop the spatial sampling strategies needed for efficient and effective sampling of scaler variables in the ABL and develop analytical methods for uncovering the processes impacting weather development. Specifically, this research will (1) determine the optimal sampling scales for thermodynamic variables captured with sUAS in the ABL using variogram analysis; (2) investigate the universality of parameters commonly used to identify the size and shape of turbulent structures across a variety of landscape types and atmospheric conditions; (3) share real time data and research findings with meteorologists at the NWS Tulsa office to aid in forecasting and numerical weather prediction. Sampling will be conducted in Oklahoma, a region of the country that frequently experiences severe local storms including tornadoes. The Oklahoma Mesonet is a network of 120 automated meteorological and weather stations that will be used for sensor calibration and validation purposes. The Mesonet sites are distributed across the state in a variety of climate zones and ecoregions. Data collection will be targeted during times of the year when storms are likely, such as spring, to ensure diverse meteorological conditions are captured.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.