| dc.description.abstract | The physical processes governing stable atmospheric boundary layer (SBL) dynamics have significant societal impacts ranging from pollution dispersion and wind energy production to polar sea ice loss. For decades, SBL turbulence has proven challenging to measure, parameterize, simulate, and interpret for a variety of reasons. For example, turbulence intensity in the SBL is often orders of magnitude smaller than in the convective boundary layer as thermal stratification suppresses vertical motions. As atmospheric stability increases, turbulence can also become intermittent in space and time, resulting in poor convergence of temporally-averaged turbulence statistics. Characteristic turbulent motions within the SBL can also be considerably smaller than the grid spacings employed by operational numerical weather prediction (NWP) models. These NWP models therefore need to parameterize turbulent energy exchange within the SBL, which can result in significant errors in near-surface temperature and wind speed forecasts due to the imperfect nature of parameterization schemes. It has been shown that improvements in SBL forecasting skill have been hindered by a relative lack in knowledge of fundamental SBL processes, which in turn is partially due to a dearth in routine and spatially dense thermodynamic and kinematic observations within the SBL. To address this so-called data gap, uncrewed aircraft systems (UAS) are proving the ability to reliably sample the atmospheric boundary layer (ABL), offering a new perspective for understanding the SBL. Moreover, continual computational advances have enabled the use of large-eddy simulations (LES) to simulate the atmosphere at ever-smaller scales. This dissertation therefore seeks to synergize UAS observations and large-eddy simulations to explore the underlying processes governing SBL dynamics.
In the first component of this dissertation, we explore the potential of a new method for the estimation of profiles of turbulence statistics in the SBL. By applying gradient-based scaling to multicopter UAS profiles of temperature and wind, sampled over sea ice during the 2018 Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR18) field campaign, turbulence profiles can be derived. We first validate this method by scaling turbulence observations from three levels on a 10-m mast with the corresponding scaling parameters, and comparing the resulting non-dimensional parameters to the semi-empirical similarity functions proposed for this scaling framework. The scaled data of turbulent fluxes and variances from the three levels collapse to their corresponding similarity functions.
After the successful validation, we estimate turbulence statistics from UAS profiles by computing profiles of the gradient Richardson number to which we then apply the similarity functions. These UAS profiles are processed from raw time series data by applying low-pass filters, time-response corrections, altitude corrections, and temporal averaging across successive flights. We present three case studies covering a broad range of SBL conditions to demonstrate the validity of this approach. Comparisons against turbulence statistics from the 10-m mast and a sodar indicate the broad agreement and physically meaningful results of this method. Successful implementation of this method thus offers a powerful diagnostic tool that requires only a multicopter UAS with a simple thermodynamic sensor payload. This ability to estimate vertical profiles of turbulent parameters that were otherwise unobtainable with traditional ground-based observations can be invaluable, e.g., for NWP verification studies within the SBL.
As UAS continue to be recognized as a robust observational platform, it is becoming increasingly important to establish a baseline framework towards understanding the extent to which vertical profiles from UAS can represent larger-scale SBL flows. This representativeness can be quantified by evaluating the magnitude of random errors for a given observation, which arise due to averaging a signal across an insufficient amount of independent samples for a statistical quantity to converge towards its true underlying ensemble value. Moreover, the LES technique can be a powerful tool for simulating SBL turbulence in space and time while varying thermal stratification to contextualize observations by UASs. The second component of this dissertation therefore seeks to quantify the representativeness of observations from UAS profiles and eddy-covariance observations within the SBL by performing a random error analysis using a suite of six large eddy simulations for a wide range of stabilities. For each experiment, we estimate relative random errors using the relaxed filtering method of Dias et al. (Boundary-Layer Meteorology, 2018, Vol. 168, 387--416) for first- and second-order moments as functions of height and averaging time. We show that the random errors can be of the same order of magnitude as other errors due to e.g. instrument bias and dynamic response, especially close to the surface. For these reasons, we recommend coupling UAS observations with other ground based instruments as well as dynamically adjusting the UAS vertical ascent rate to account for how errors change with height and stability.
In the first component of this dissertation, we consider only observations by UAS in the Arctic SBL, and in the second component we further explore the representativeness of UAS observations within idealized SBLs with LES. To conclude this dissertation, in the third component we employ only a series of eight large-eddy simulations to investigate fundamental processes within stably-stratified wall-bounded turbulent flows from the perspective of coherent structures. To date, a growing body of literature has documented the existence and impacts of so-called large- and very-large-scale motions within wall-bounded turbulent flows under neutral and convective thermal stratification. Large- and very-large-scale motions have been attributed to modulating turbulence intensity near the wall, and properly characterizing their contributions to ABL turbulence may lead to improvements in NWP forecast skill. In the context of the SBL, however, the examination of such coherent structures has garnered relatively little attention. Stable stratification limits vertical transport and turbulent mixing within flows, which makes it unclear whether previous findings on coherent structures under unstable and neutral stratification are applicable to the SBL. Moreover, mesoscale processes can obscure the underlying physics of stably-stratified flows when collecting observations in the SBL. In this third component, we investigate the existence and characteristics of coherent structures within the SBL with a wide range of statistical and spectral analyses. A quadrant analysis of turbulent transport efficiencies (the ratio of net fluxes to their respective downgradient components) demonstrates dependencies on both stability and height above ground, which may be related to morphological differences in the coherent structures under increasing stability. Physical mechanisms responsible for these differences are explored through analyses of spectrograms, linear coherence spectra, amplitude modulation coefficients, and conditional sampling for a variety of first- and second-order turbulent moments. Results indicate the presence of coherent structures at near-neutral stability that diminish with increasing stable stratification. Stable stratification was found to suppress large eddies, thereby limiting any inner-outer scale interactions. | en_US |
