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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STATE-RESOLVED QUENCHING DYNAMICS IN COLLISIONS OF VIBRATIONALLY EXCITED MOLECULES
(2010) Du, Juan; MULLIN, AMY S; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The collisional relaxation of highly excited molecules plays a very important role in many chemistry processes. The work presented in this thesis studies the collisional quenching dynamics of highly vibrationally excited molecules using high–resolution transient IR absorption spectroscopy. This work investigates “weak” (small energy transfer) and “strong” (large energy transfer) collisions between donor and bath molecules. The experimental results illustrate how the properties of donor molecules influence the collisional energy transfer. These properties include the molecular structure, internal energy, state density. In several weak collision studies, this thesis studies the vibration–rotation/translation pathway for pyrazine/DCl, pyrazine/CO₂ with different internal energies and for three excited alkylated pyridine molecules/CO₂ systems. A single–exponential rotational distribution and J–dependent translational energy distributions of scattered DCl molecules are observed. For CO₂ collisions, the scattered CO₂ has a biexponential rotational distribution and J–dependent translational energy distributions for all collision pairs. Recoil velocities scale with product angular momenta. The observed collision rates for these collision pairs match Lennard–Jones rates. The full energy transfer distribution for these pairs is determined by combining data for weak and strong collisions. Lowering the internal energy of donor molecules reduces the amount of rotational and translational energy transfer to CO₂. Reducing the internal energy of pyrazine decreases the probabilities of strong collision and increases the probabilities of weak collision. The average energy transfer reduces by ∼ 50% when the internal energy is decreased by only 15%. The collision rates are independent on the internal energy for these systems. Methylation of donor molecules decreases the magnitude of V—RT energy transfer. The collision results are affected by the number of methyl–groups, and not by the position of the group. Increasing the number of methyl groups increases the ratio of the measured collision rate to the Lennard–Jones collision rate. In the strong collision studies, the effects of alkylation and internal energy are studied. In collisions with alkylated pyridine donors with E ∼ 39000 cm−1, CO₂ molecules gain less energy from alkylpyridine than from pyridine. The alkylated donors undergo strong collisions with CO₂ via a less repulsive part of the intermolecular potential compared to pyridine. For azulene/CO₂ collisions with two different internal energies, scattered CO₂ molecules gain double the amount of rotational and translational energy when the azulene energy is doubled. The rate of strong collisions increases four times when the internal energy is doubled.
Synchronization in chaotic systems: Coupling of chaotic maps, data assimilation and weather forecasting
(2007-11-27) Baek, Seung-Jong; Ott, Edward; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The theme of this thesis is the synchronization of coupled chaotic systems. Background and introductory material are presented in Chapter 1. In Chapter 2, we study the transition to coherence of ensembles of globally coupled chaotic maps allowing for ensembles of non-identical maps and for noise. The transition coupling strength is determined from a transfer function of the perturbation evolution. Analytical results are presented and tested using numerical experiments. One of our examples suggests that the validity of the perturbation theory approach can be problematic for an ensemble of noiseless identical `nonhyperbolic' maps, but can be restored by noise and/or parameter spread. The problem of estimating the state of a large evolving spatiotemporally chaotic system from noisy observations and a model of the system dynamics is studied in Chapters 3 - 5. This problem, refered to as `data assimilation', can be thought of as a synchorization problem where one attempts to synchronize the model state to the system state by using incoming data to correct synchronization error. In Chapter 3, using a simple data assimilation technique, we show the possible occurrence of temporally and spatially localized bursts in the estimation error. We discuss the similarity of these bursts to those occurring at the `bubbling transition' in the synchronization of low dimensional chaotic systems. In general, the model used for state estimation is imperfect and does not exactly represent the system dynamics. In Chapter 4 we modify an ensemble Kalman filter scheme to incorporate the effect of model bias for large chaotic systems based on augmentation of the system state by the bias estimates, and we consider different ways of parameterizing the model bias. The experimental results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the model. In Chapter 5 we further test the method developed in Chapter 4 via numerical experiments employing previously developed codes for global weather forecasting. The results suggest that our method can be effective for obtaining improved forecasting results when using an ensemble Kalman filter scheme.
Variability of the Great Plains Low-Level Jet: Large Scale Circulation Context and Hydroclimate Impacts
(2007-04-26) Weaver, Scott Jamie; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Variability of the Great Plains Low-Level Jet (GPLLJ) is analyzed from the perspective of larger-scale, lower-frequency influences and regional hydroclimate impacts; as opposed to the usual analysis of its frequency, diurnal variability and mesoscale structure. The circulation-centric core analysis is conducted with monthly and pentad data from the high spatio-temporal resolution, precipitation-assimilating North American Regional Reanalysis, and ERA-40 global reanalysis (as necessary) to identify the recurrent patterns of GPLLJ variability and their large-scale circulation and regional hydroclimate links. The analysis reveals that GPLLJ variability is, indeed, linked to coherent, large-scale, upper-level height patterns over the Pacific, and NAO variability in the Atlantic. A Rossby Wave Source analysis shows the Pacific height pattern to be potentially linked to tropical diabatic heating anomalies in the west-central basin and in the eastern Pacific sector. EOF analysis of GPLLJ variability shows it to be comprised of three modes that exert profound influence on Great Plains precipitation variability, and together, account for ~75% of the variance. Ocean basin centered EOF analysis on summertime SLP anomalies shows similar GPLLJ and precipitation impacts as those found in the Great Plains centric perspective, supporting the claim for remotely generated influences on Great Plains low-level jet and hydroclimate variability. Pentad analysis of the atmospheric and terrestrial water balances during the 1988 drought and 1993 flood show that, jet variability, while influential over many of the subseasonal anomalous precipitation episodes was not a necessary condition for precipitation anomalies. Great Plains evaporation exhibited a 2-week delay with respect to precipitation suggesting a minor role for precipitation recycling during these events. ENSO and NAO variability are shown to contribute significantly to the large midsummer positive precipitation anomalies during 1993. EEOF analysis of pentad 900 hPa meridional winds during MJJ show three temporally stable modes of variability, each exhibiting similar spatial characteristics to the monthly EOF spatial patterns. Lead/lag regressions show a one pentad delay in moisture flux convergence generated precipitation anomalies, perhaps, suggesting the importance of moisture transports in generating Great Plains precipitation anomalies. Climate models are shown to be challenged in depicting the jet and precipitation variability over the Great Plains.
RETRIEVAL OF TROPOSPHERIC AEROSOL PROPERTIES OVER LAND FROM INVERSION OF VISIBLE AND NEAR-INFRARED SPECTRAL REFLECTANCE: APPLICATION OVER MARYLAND
(2007-04-26) Levy, Robert; Dickerson, Russell R.; Remer, Lorraine A; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Aerosols are major components of the Earth's global climate system, affecting the radiation budget and cloud processes of the atmosphere. When located near the surface, high concentrations lead to lowered visibility, increased health problems and generally reduced quality of life for the human population. Over the United States mid-Atlantic region, aerosol pollution is a problem mainly during the summer. Satellites, such as the MODerate Imaging Spectrometer (MODIS), from their vantage point above the atmosphere, provide unprecedented coverage of global and regional aerosols over land. During MODIS' eight-year operation, exhaustive data validation and analyses have shown how the algorithm should be improved. This dissertation describes the development of the 'second-generation' operational algorithm for retrieval of global tropospheric aerosol properties over dark land surfaces, from MODIS -observed spectral reflectance. New understanding about global aerosol properties, land surface reflectance characteristics, and radiative transfer properties were learned in the process. This new operational algorithm performs a simultaneous inversion of reflectance in two visible channels (0.47 and 0.66 μm) and one shortwave infrared channel (2.12 μm), thereby having increased sensitivity to coarse aerosol. Inversion of the three channels retrieves the aerosol optical depth (τ) at 0.55 μm, the percentage of non-dust (fine model) aerosol (η) and the surface reflectance. This algorithm is applied globally, and retrieves τ that is highly correlated (y = 0.02 + 1.0x, R=0.9) with ground-based sunphotometer measurements. The new algorithm estimates the global, over-land, long-term averaged τ ~ 0.21, a 25% reduction from previous MODIS estimates. This leads to reducing estimates of global, non-desert, over-land aerosol direct radiative effect (all aerosols) by 1.7 W·m-2 (0.5 W·m-2 over the entire globe), which significantly impacts assessment of aerosol direct radiative forcing (contribution from anthropogenic aerosols only). Over the U.S. mid-Atlantic region, validated retrievals of τ (an integrated column property) can help to estimate surface PM2.5 concentration, a monitored criteria air quality property. The 3-dimensional aerosol loading in the region is characterized using aircraft measurements and the Community Multi-scale Air Quality Model (CMAQ) model, leading to some convergence of observed quantities and modeled processes.
Assimilating Satellite Observations with a Local Ensemble Kalman Filter
(2007-04-25) Fertig, Elana Judith; Hunt, Brian R; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Numerical weather prediction relies on data assimilation to estimate the current state of the atmosphere. Generally speaking, data assimilation methods combine information from observations and from a prior forecast state, taking into account their respective uncertainties. Ensemble-based data assimilation schemes estimate the forecast uncertainty with the sample covariance from an ensemble of forecasts. While these schemes have been shown to successfully assimilate conventional observations of model state variables, they have only recently begun to assimilate satellite observations. This dissertation explores some of the complications that arise when ensemble-based schemes assimilate satellite observations. Although ensemble data assimilation schemes often assume that observations are taken at the time of assimilation, satellite observations are available almost continuously between consecutive assimilation times. In Chapter 2, we formulate a ``four-dimensional'' extension to ensemble-based schemes that is analogous to the operationally used scheme 4D-VAR. Using perfect model experiments with the Lorenz-96 model, we find that the four-dimensional ensemble scheme can perform comparably to 4D-VAR. Many ensemble data assimilation schemes utilize spatial localization so that a small ensemble can capture the unstable degrees of freedom in the model state. These local ensemble-based schemes typically allow the analysis at a given location to depend only on observations near that location. Meanwhile, the location of satellite observations cannot be pinpointed in the same manner as conventional observations. In Chapter 3, we propose a technique to update the state at a given location by assimilating satellite radiance observations that are strongly correlated to the model state there. For satellite retrievals, we propose incorporating the observation error covariance matrix and selecting the retrievals that have errors correlated to observations near the location to be updated. Our selection techniques improve the analysis obtained when assimilating simulated satellite observations with a seven-layer primitive equation model, the SPEEDY model. Finally, satellite radiance observations are subject to state-dependent, systematic errors due to errors in the radiative transfer model used as the observation operator. In Chapter 4 we propose applying state-space augmentation to ensemble based assimilation schemes to estimate satellite radiance biases during the data assimilation procedure. Our approach successfully corrects such systematic errors in simulated biased satellite observations with the SPEEDY model.
Classification of Northern Hemisphere Stratospheric Ozone and Water Vapor Profiles by Meteorological Regime: Validation, Climatology, and Trends
(2007-01-19) Follette, Melanie Beth; Hudson, Robert D.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The presence of stratospheric ozone is essential for the survival of life on the Earth's surface. The decrease in the column content of ozone over mid-latitudes from 1979-1991 has previously been attributed to destruction by anthropogenic halogens, and changes in the general circulation. The research presented here shows that a poleward movement of the subtropical and polar upper troposphere fronts is responsible for 35% of this observed decrease. In Hudson et al. (2003) we showed that the Northern Hemisphere total ozone field could be separated into meteorological regimes, bounded by the subtropical and polar upper troposphere fronts. These regimes were characterized by relatively constant total ozone, tropopause height, and ozonepause height. Negative trends in total ozone within each regime were found for the time period January 1979-May 1991. These trends corresponded to a statistically significant increase in the relative area of the tropical regime, and decrease in the relative area of the polar regime, indicating a net poleward movement of the subtropical and polar fronts over this time period. This poleward frontal movement was responsible for ~35% of the negative zonal trend in total ozone over this time period and latitude range, the remaining 65% being the result of total ozone changes within the meteorological regimes. Ozone and water vapor profiles from 1997-2004, from the HALOE and SAGE II satellite-based instruments, were classified by regime. Each regime was characterized by a distinct ozonepause and hygropause height, and profile shape below ~25km, over a wide latitude range (25°-60°N). Therefore, previously reported zonal trends in the lower stratosphere and upper troposphere are a combination of both tropospheric and stratospheric air. Trends within each regime were calculated for both ozone and water vapor from 1997-2004 and from October 1984-May 1991. The relationship between the observed zonal vertical trends and the trends within each regime were consistent with the idea of meteorological regimes and reinforce the major conclusion of this work. A true understanding of zonal trends in either the column or in the lower stratosphere involves understanding both changes within each regime and changes in the relative weighting of each regime over time.
An analysis of convective transport, Lightning NO.sub.x production, and chemistry in midlatitude and subtropical thunderstorms
(2006-10-18) Ott, Lesley Elaine; Dickerson, Russell R.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The impact of lightning NO.sub.x production and convective transport on tropospheric chemistry was studied in four thunderstorms observed during field projects using a 3-dimensional (3-D) cloud-scale chemical transport model (CSCTM). The dynamical evolution of each storm was simulated using a cloud-resolving model, and the output used to drive the off-line CSCTM which includes a parameterized source of lightning NO.sub.x based on observed cloud-to-ground (CG) and intracloud (IC) flash rates. Simulated mixing ratios of tracer species were compared to anvil aircraft observations to evaluate convective transport in the model. The production of NO per CG flash (P.sub.CG) was estimated based on mean observed peak current, and production per IC flash (P.sub.IC) was scaled to P.sub.CG. Different values of P.sub.IC/P.sub.CG were assumed and the results compared with in-cloud aircraft measurements to estimate the ratio most appropriate for each storm. The impact of lightning NO.sub.x on ozone and other species was examined during the storm in the CSCTM and following each storm in the convective plume using a chemistry-only version of the model which includes diffusion but without advection, and assumes clear-sky photolysis rates. New lightning parameterizations were implemented in the CSCTM. One parameterization uses flash length data, rather than flash rates, as input, and production per meter of flash channel length is estimated. A second parameterization simulates indivdual lightning flashes rather than distributing lightning NO_x uniformly among a large number of gridcells to better reproduce the variability of observations. The results suggest that P_IC is likely on the order of P_CG and not significantly less as has been assumed in many global modeling studies. Mean values of P_CG=500 moles NO and P_IC=425 moles NO have been estimated from these simulations of midlatitude and subtropical continental thunderstorms. Based on the estimates of production per flash, and an assumed ratio of the number of IC to CG flashes and global flash rate, a global annual lightning NO source of 8.6 Tg N yr^-1 is estimated. Based on these simulations, vertical profiles of lightning NO_x mass for subtropical and midlatitude continental regimes have been computed for use in global and regional chemical transport models.
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 E between two solutions of a global weather model with similar initial conditions. Typically E grows until it reaches a saturation value E_s. We find two distinct broad log-linear regimes, one for E below 2% of E_s and the other for E above. In each, log(E/E_s) grows as if satisfying a linear differential equation. When plotting dlog(E)/dt vs log(E), 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.
Regional Aspects of the North American Land Surface-Atmosphere Interactions and Their Contributions to the Variability and Predictability of the Regional Hydrologic Cycle
(2006-04-17) Luo, Yan; Berbery, Ernesto Hugo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In this study, we investigate the pathways responsible for soil moisture-precipitation interactions and the mechanisms for soil moisture memory at regional scales through analysis of NCEP's North American Regional Reanalysis dataset, which is derived from a system using the mesoscale Eta model coupled with Noah land surface model. The consideration of the relative availability of water and energy leads to the relative strengths of land-atmosphere interaction and soil moisture memory, which are related to the predictability of the regional hydrologic cycle. The seasonal and geographical variations in estimated interaction and memory may establish the relative predictability among the North American basins. The potential for seasonal predictability of the regional hydrologic cycle is conditioned by the foreknowledge of the land surface soil state, which contributes significantly to summer precipitation: (i) The precipitation variability and predictability by strong land-atmosphere interactions are most important in the monsoon regions of Mexico; (ii) Although strong in interactions, the poor soil moisture memory in the Colorado basin and the western part of the Mississippi basin lowers the predictability; (iii) The Columbia basin and the eastern part of the Mississippi basin also stand out as low predictability basins, in that they have good soil moisture memory, but weak strength in interactions, limiting their predictabilities. Our analysis has revealed a highly physically and statistically consistent picture, providing solid support to studies of predictability based on model simulations.
Making Forecasts for Chaotic Processes in the Presence of Model Error
(2006-02-20) Danforth, Christopher M; Yorke, James A; Kalnay, Eugenia; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Numerical weather forecast errors are generated by model deficiencies and by errors in the initial conditions which interact and grow nonlinearly. With recent progress in data assimilation, the accuracy in the initial conditions has been substantially improved so that accounting for systematic errors associated with model deficiencies has become even more important to ensemble prediction and data assimilation applications. This dissertation describes two new methods for reducing the effect of model error in forecasts. The first method is inspired by Leith (1978) who proposed a statistical method to account for model bias and systematic errors linearly dependent on the flow anomalies. DelSole and Hou (1999) showed this method to be successful when applied to a very low order quasi-geostrophic model simulation with artificial "model errors." However, Leith's method is computationally prohibitive for high-resolution operational models. The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure that could be applied operationally, and to compare the impact of correcting the model integration with statistical corrections performed a posteriori. The second method is inspired by the dynamical systems theory of shadowing. Making a prediction for a chaotic physical process involves specifying the probability associated with each possible outcome. Ensembles of solutions are frequently used to estimate this probability distribution. However, for a typical chaotic physical system H and model L of that system, no solution of L remains close to H for all time. We propose an alternative and show how to "inflate" or systematically perturb the ensemble of solutions of L so that some ensemble member remains close to H for orders of magnitude longer than unperturbed solutions of L. This is true even when the perturbations are significantly smaller than the model error.

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