Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Improving our Understanding of Tropical Cyclone Unusual Motion and Rapid Intensification(2019) Miller, William James Schouler; Zhang, Da-Lin; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite steady improvement in their tropical cyclone (TC) track and intensity forecasts over recent decades, operational numerical weather prediction (NWP) models still struggle at times in predicting two TC phenomena: climatologically unusual motion and rapid intensification (RI). Atlantic TCs typically move clockwise along curved tracks skirting the southern, western, and northwestern periphery of the Western Atlantic Ridge. Hurricane Joaquin (2015) followed a particularly unusual hairpin loop-shaped track that was poorly predicted by most operational NWP models, including the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Over recent years, considerable interest has also developed in understanding the cause-and-effect relationship between RI, defined here as a maximum surface wind (VMAX) intensification rate exceeding 15 m s-1 (24 h-1), and outbreaks of inner core deep convection, known as convective bursts (CBs), that have been observed to precede or coincide with RI in some TCs. A deeper physical understanding of the atmospheric processes governing TC unusual motion and RI, together with retrospective case study analyses of model forecast errors, will help us to identify NWP model components – data assimilation and physical parameterizations, for example – that may need further improvement. This research project seeks to (i) identify the atmospheric features that steered Hurricane Joaquin (2015) along the southwestward leg of its looping track and (ii) investigate the thermodynamic and three-dimensional characteristics of CBs as a first step toward developing a more comprehensive understanding of how CBs may facilitate RI. To accomplish (i), we generate a high-resolution Weather Research and Forecasting (WRF) model Control (CTL) simulation of Hurricane Joaquin (2015) that reproduces its looping track and intensification trends. Comparing CTL forecast fields against sensitivity WRF simulations initialized from perturbed analyses and against two representative GFS forecasts, we find that a sufficiently strong mid-to-upper level ridge northwest of Joaquin and a vortex sufficiently deep to interact with northeasterly geostrophic flows surrounding the ridge are both necessary for steering Joaquin southwestward. These results suggest that more accurate track forecasts for TCs developing in vertically sheared environments may be at least partly contingent on improved vortex initialization; for these cases, assimilation of more inner-core observations such as cloudy radiances and airborne radar-derived winds could be particularly beneficial. We address (ii) by comparing parcel traces, thermodynamic variables, and vertical accelerations along trajectories run through CB updraft cores with trajectories representative of the background eyewall ascent in a Hurricane Wilma (2005) WRF simulation. We compute three-dimensional trajectories from WRF-output winds using a model developed for this study that implements an experimental advection correction algorithm designed to reduce time interpolation errors, with the latter confirmed by tests on analytical and numerically-simulated flows. Results show that Wilma’s CBs are characterized by significant thermal buoyancy, particularly in the upper troposphere; this is consistent with their lower environmental air entrainment rates and reduced midlevel hydrometeor loading relative to the background ascent, and with their updrafts being rooted in portions of the boundary layer where ocean surface heat and moisture fluxes are locally higher.Item Errors in the Initial Conditions for Numerical Weather Prediction: A Study of Error Growth Patterns and Error Reduction with Ensemble Filtering(2006-04-19) Harlim, John; Hunt, Brian R.; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we study the errors of a numerical weather prediction due to the errors in initial conditions and we present efficient nonlinear ensemble filters for reducing these errors. First, we investigate the error growth, that is, the growth in time of the distance <em>E</em> between two solutions of a global weather model with similar initial conditions. Typically <em>E</em> grows until it reaches a saturation value <em>E_s</em>. We find two distinct broad <em>log-linear regimes</em>, one for <em>E</em> below 2% of <em>E_s</em> and the other for <em>E</em> above. In each, <em>log(E/E_s)</em> grows as if satisfying a linear differential equation. When plotting <em>dlog(E)/dt</em> vs <em>log(E)</em>, the graph is convex. We argue this behavior is quite different from error growth in other simpler dynamical systems, which yield concave graphs. Secondly, we present an efficient variation of the Local Ensemble Kalman Filter (Ott et al. 2002, 2004) and the results of perfect model tests with the Lorenz-96 model. This scheme is locally similar to performing the Ensemble Transform Kalman Filter (Bishop et al. 2001). We also include a ``four-dimensional" extension of the scheme to allow for asynchronous observations. Finally, we present a modified ensemble Kalman filter that allows a non-Gaussian background error distribution. Using a distribution that decays more slowly than a Gaussian is an alternative to using a high amount of variance inflation. We demonstrate the effectiveness of this approach for the three-dimensional Lorenz-63 model and the 40-dimensional Lorenz-96 model in cases when the observations are infrequent, for which the non-Gaussian filter reduces the average analysis error by about 10% compared to the analogous Gaussian filter. The mathematical formulation of this non-Gaussian filter is designed to preserve the computational efficiency of the local filter described in the previous paragraph for high-dimensional systems.