Decades of research have fostered a greater understanding of the environmental controls that drive tropical cyclone (TC) intensity change, yet the community has achieved only small improvements in intensity forecasting. Numerous environmental factors impact TC intensity, such as vertical wind shear and sea surface temperatures (SSTs), but little research has focused on establishing if SST change under the TC, or SST gradients, influence these intensity changes. This study investigated three methods to compute SST gradients. The first method calculated the SST change within fixed distances along the track. In the second and third methods, the SST was calculated over the distance traversed by the TC in two separate six-hour periods. By examining 455 24-hour weakening episodes in the eastern North Pacific, this study revealed that the first SST gradient method explained the highest 24-hour weakening variance for TCs located within SSTs at or lower than 26.5 degrees C.

African easterly waves (AEWs) are known precursors to tropical cyclone (TC) formation, although it is not always clear which AEWs will develop and which AEWs will not. To investigate AEW evolution, this study examines novel observations from the geostationary Advanced Baseline Imager (ABI) during July-September 2019. Case studies are conducted for two AEWs: one that became Hurricane Dorian, the strongest and most devastating hurricane of the 2019 Atlantic hurricane season, and a long-lived September AEW that did not become a TC. Lower-level moisture and flow, and the strength and spatial distribution of convective activity, differed between these two waves. By then exploring these characteristics for additional developing and non-developing AEWs, ABI observations show that developing AEWs are associated with low-level moist air ahead of the wave combined with enhanced convective activity, while non-developing AEWs tend to encounter drier air and exhibit a persistently broader structure with less-organized convection.

Tropical cyclone (TC) intensity and structure are affected by their environments, including sea surface temperature, vertical wind shear, and atmospheric moisture. Analyses of TC environments often rely on area-averaged quantities, yet the spatial variability of these fields can affect TC behavior, such as moisture distribution impacting where and how much rain falls. This study identifies spatial patterns of environmental moisture surrounding mature North Atlantic TCs during 2000-2021 in shear of less than 20 knots. Empirical orthogonal function analysis of total column water vapor reveals six dominant patterns. These patterns account for nearly 67% of the variance in the dataset and are affected by geographic location and large-scale atmospheric phenomena. Mid-level ventilation appears more likely in certain patterns. Future work will explore radar and passive microwave observations for cases in each pattern to quantify the physical impacts of these moisture patterns on mature TCs.

The QuikSCAT scatterometer provides global daily coverage of oceanic near-surface vector winds. Recently, algorithms have been developed to enhance the spatial resolution of QuikSCAT winds from 25~km to 2.5~km posting. These ultra-high resolution winds are used, in comparison with standard L2B data product winds, to observe and track tropical cyclones. Resolution enhanced winds are found to provide additional storm structure such as inner core size and structure and the presence of multiple eyewalls compared with standard resolution winds. The 2.5~km winds are also able to observe storms nearer to the shore than 25~km winds. An analysis of circulation center locatability with each resolution wind field is performed. Center fixes with enhanced resolution winds are nearer the National Hurricane Center best-track positions than are standard resolution center fixes. A data and image set of every tropical cyclone worldwide observed by Seawinds on QuikSCAT or SeaWinds on ADEOS II from 1999 through 2005 is generated and made available to the scientific community at http://scp.byu.edu.

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Four modern shoals on the Louisiana continental shelf are proposed to have formed through transgression, marine reworking, and submergence of Mississippi River deltaic lobes. However, one of these shoals, the St. Bernard Shoals, is dissimilar to the other shoals in morphology and stratigraphy. Understanding the processes that lead to these differences resulted in the development of a wholly new model for subaqueous shoal evolution. The results of this study suggest that the St. Bernard Shoals are transgressive remnants of a near shelf-edge delta lobe that was transgressed and truncated by marine processes after fluvial abandonment. Subsequent to truncation, the shoals formed through subaqueous excavation and reworking of coarse grained sediment contained within underlying distributary channels by hurricane related marine currents. As a result the shoals are bound at their base by a ravinement surface and lie directly upon progradational facies associated with previously unrecognized southern progradation of the La Loutre distributary network.

The recent elevated North Atlantic hurricane activity has generated considerable interests in the interaction between tropical cyclones (TCs) and climate change. The possible connection between TCs and the changing climate has been indicated by observational studies based on historical TC records; they indicate emerging trends in TC frequency and intensity in some TC basins, but the detection of trends has been hotly debated due to TC track data issues. Dynamical climate modeling has also been applied to the problem, but brings its own set of limitations owing to limited model resolution and uncertainties. The final goal of this study is to project the future changes of North Atlantic TC behavior with global warming for the next 50 years using the Nested Regional Climate Model (NRCM). Throughout the course of reaching this goal, various uncertainties and limitations in simulating TCs by the NRCM are identified and explored. First we examine the TC tracking algorithm to detect and track simulated TCs from model output. The criteria and thresholds used in the tracking algorithm control the simulated TC climatology, making it difficult to objectively assess the model's ability in simulating TC climatology. Existing tracking algorithms used by previous studies are surveyed and it is found that the criteria and thresholds are very diverse. Sensitivity of varying criteria and thresholds in TC tracking algorithm to simulated TC climatology is very high, especially with the intensity and duration thresholds. It is found that the commonly used criteria may not be strict enough to filter out intense extratropical systems and hybrid systems. We propose that a better distinction between TCs and other low-pressure systems can be achieved by adding the Cyclone Phase technique. Two sets of NRCM simulations are presented in this dissertation: One in the hindcasting mode, and the other with forcing from the Community Climate System Model (CCSM) to project into the future with global warming. Both of these simulations are assessed using the tracking algorithm with cyclone phase technique. The NRCM is run in a hindcasting mode for the global tropics in order to assess its ability to simulate the current observed TC climatology. It is found that the NRCM is capable of capturing the general spatial and temporal distributions of TCs, but tends to overproduce TCs particularly in the Northwest Pacific. The overpredction of TCs is associated with the overall convective tendency in the model added with an outstanding theory of wave energy accumulation leading to TC genesis. On the other hand, TC frequency in the tropical North Atlantic is under predicted due to the lack of moist African Easterly Waves. The importance of high-resolution is shown with the additional simulation with two-way nesting. The NRCM is then forced by the CCSM to project the future changes in North Atlantic TCs. An El Nino-like SST bias in the CCSM induced a high vertical wind shear in tropical North Atlantic, preventing TCs from forming in this region. A simple bias correction method is applied to remove this bias. The model projected an increase both in TC frequency and intensity owing to enhanced TC genesis in the main development region, where the model projects an increased favorability of large-scale environment for TC genesis. However, the model is not capable of explicitly simulating intense (Category 3-5) storms due to the limited model resolution. To extrapolate the prediction to intense storms, we propose a hybrid approach that combines the model results and a statistical modeling using extreme value theory. Specifically, the current observed TC intensity is statistically modeled with the General Pareto distribution, and the simulated intensity changes from the NRCM are applied to the statistical model to project the changes in intense storms. The results suggest that the occurrence of Category 5 storms may be increased by approximately 50% by 2055.

The spatial and temporal complexity of tropical cyclones (TCs) raises a number of scientific questions regarding their genesis, movement, intensification, and variability. In this dissertation, the principal goal is to determine the current state of predictability for each of these processes. To quantify the current extent of tropical cyclone predictability, we assess probabilistic forecasts from the most advanced global numerical weather prediction system to date, the ECMWF Variable Resolution Ensemble Prediction System (VarEPS). Using a new false alarm clustering technique to maximize the utility of the VarEPS, the ensemble system is shown to provide well-calibrated probabilistic forecasts for TC genesis through a lead-time of one week, and pregenesis track forecasts with similar skill compared to the VarEPS's postgenesis track forecasts. To quantify the predictability of TCs on intraseasonal time scales, forecasts from the ECMWF Monthly Forecast System (ECMFS) are examined for the North Atlantic Ocean. From this assessment, dynamically based forecasts from the ECMFS provide forecast skill exceeding climatology out to weeks three and four for portions of the southern Gulf of Mexico, western Caribbean and the Main Development Region. Forecast skill in these regions is traced to the model's ability to capture correctly the variability in deep-layer vertical wind shear, the relative frequency of easterly waves moving through these regions, and the intraseasonal modulation of the Madden-Julian Oscillation. On interannual time scales, the predictability of TCs is examined by considering their relationship with tropical Atlantic easterly waves. First, a set of easterly wave climatologies for the CFS-R, ERA-Interim, ERA-40, and NCEP/NCAR Reanalysis are developed using a new easterly wave-tracking algorithm. From the reanalysis-derived climatologies, a moderately positive and statistically significant relationship is seen with tropical Atlantic TCs. In relation to large-scale climate modes, the Atlantic Multidecadal Oscillation (AMO) and Atlantic Meridional Mode (AMM) exhibit the strongest positive covariability with Atlantic easterly wave frequency. Besides changes in the number of easterly waves, the intensification efficiency of easterly waves has also been evaluated. These findings offer a plausible physical explanation for the recent increase in the number of NATL TCs, as it has been concomitant with an increasing trend in both the number of tropical Atlantic easterly waves and intensification efficiency. The last component of this dissertation examines how the historical variability in U.S. landfalling TCs has impacted the annual TC tornado record. To reconcile the inhomogeneous, historical tornado record, two statistical tornado models, developed from a set of a priori predictors for TC tornado formation, are used to reconstruct the TC tornado climatology. While the synthetic TC tornado record reflects decadal scale variations in association with the AMO, a comparison of the current warm phase of the AMO with the previous warm phase period shows that the median number of tornadoes per Gulf TC landfall has significantly increased. This change likely reflects the increase in median TC size (by 35%) of Gulf landfalling TCs along with an increased frequency of large TCs at landfall.

Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclones (TCs). Differences between the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) retrievals are found to be related to the storm region (inner core vs. rainbands) and the convective nature of the precipitation as measured by radar reflectivity and ice scattering signature. In landfalling TCs, the algorithms perform differently depending on whether the rainfall is located over ocean, land, or coastal surfaces. Various statistical techniques are applied to quantify these differences and identify the discrepancies in rainfall detection and intensity. Ground validation is accomplished by comparing the landfalling storms over the Southeast US to the NEXRAD Multisensor Precipitation Estimates (MPE) Stage-IV product. Numerous recommendations are given to algorithm users and developers for applying and interpreting these algorithms in areas of heavy and widespread tropical rainfall such as tropical cyclones.

Presented within this thesis are three studies on three rare and imperiled Southeastern woody plant species: mountain stewartia (Stewartia ovata), Atlantic white-cedar (Chamaecyparis thyoides), and Miller's witch-alder (Fothergilla milleri). This work contributes to the ecological understanding of these three species allowing for better-informed conservation decision-making. Machine learning habitat suitability models are presented for mountain stewartia and Miller's witch-alder. These models can direct limited conservation dollars and manpower towards areas of the highest habitat suitability. This work also utilizes field-based data to assess the habitat needs, species associations, and regeneration potential of both Atlantic white-cedar and Miller's witch-alder. Understanding the habitat and regeneration potential of these species allows conservationists to make more tailored land management decisions for the species. As plant species continue to be threatened with extinction, more basic and applied research is needed to lessen the impacts of the 6th mass extinction on native flora.