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This thesis describes the deployment of MIRSL's X-band dual-polarization Phase-Tilt Weather Radar (PTWR) at the University of Texas at Arlington during spring 2014. While this radar has been used to observe weather in Western Massachusetts, more observations of severe weather were required to determine the limits of its abilities in sensing more rapidly evolving weather systems. This site was chosen also for its proximity to the Dallas-Fort Worth Urban Testbed Network set up by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), which provided the ability to compare and calibrate the PTWR data against another well-documented X-band weather radar. A data processing pipeline was developed for converting raw PTWR data to NetCDF format, which allows for easy sharing and mapping of weather data. Finally, this is the first in-depth documentation of the PTWR system and specifically the roof-mounted setup utilized for this deployment.

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.

Yeung Yun Sing Samson. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 64-67). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Applicability of Weather Derivative in Hong Kong: The Recre- ation Industry --- p.7 / Chapter 2.2 --- Types of Weather Risk --- p.9 / Chapter 3 --- Literature Review --- p.12 / Chapter 4 --- Basic Model --- p.17 / Chapter 4.1 --- Notations --- p.18 / Chapter 4.2 --- Assumptions --- p.21 / Chapter 4.3 --- The Profit Model --- p.22 / Chapter 5 --- Fundamental Analysis --- p.25 / Chapter 5.1 --- Sales Profit Analysis --- p.25 / Chapter 5.2 --- Option Analysis --- p.27 / Chapter 5.3 --- Profit Function Reformulation --- p.30 / Chapter 6 --- Objectivel: Lexicographic Optimization --- p.35 / Chapter 6.1 --- Equivalence between Lexicographic Optimization and Expected Utility Maximization --- p.38 / Chapter 6.2 --- Minimizing the Conditional Profit Variance given Q* --- p.39 / Chapter 6.3 --- Numerical Examples --- p.42 / Chapter 6.3.1 --- Convexity of conditional profit variance --- p.42 / Chapter 6.3.2 --- Correlation between Q* & N* --- p.47 / Chapter 7 --- Objective2: Mean-Variance Optimization --- p.52 / Chapter 7.1 --- Numerical Examples --- p.59 / Chapter 8 --- Conclusion and Future Work --- p.61 / Bibliography --- p.64 / Chapter A --- Weather Option Pricing --- p.68 / Chapter B --- Infeasibility of Perfect Hedge --- p.70

This thesis addresses the extratropical transition (ET) of tropical cyclones. ET is the process by which a tropical cyclone, upon encountering a baroclinic environment at higher latitudes, loses its tropical characteristics and transforms into an extratropical cyclone. The three main goals of the thesis are to develop a historical climatology of global ET occurrence, to examine future projections of ET using a global climate model, and to advance the predictive understanding of ET. A global climatology of ET from 1979-2017 is presented, which explores frequency of occurrence, geographical and seasonal patterns, climate variability, and environmental settings associated with different types of ET in global ocean basins. ET is defined objectively by means of tropical cyclones' trajectories through the Cyclone Phase Space (CPS), which is calculated using storm tracks from best track data and geopotential height fields from reanalysis datasets. Two reanalysis datasets are used and compared for this purpose, the Japanese 55-year Reanalysis (JRA-55) and the ECMWF Interim Reanalysis (ERA-Interim). Results show that ET is most common in the western North Pacific and the North Atlantic, where about half of the tropical cyclones transition into extratropical cyclones. Coastal regions in these basins also face the highest rates of landfalling ET storms. In the Southern Hemisphere basins, ET percentages range from about 20% to 40%. Different "ET pathways" through the CPS are linked to different geographical trajectories and environmental settings: A majority of ETs start with the tropical cyclone becoming thermally asymmetric and end with the formation of a cold core. This pathway typically occurs over warmer sea surface temperatures and takes longer than the reverse pathway, in which a tropical cyclone undergoes ET by developing a cold core before becoming asymmetric. The classifications of tropical cyclones into "ET storms" (tropical cyclones that undergo at some point in their lifetimes) and "non-ET storms" (tropical cyclones that do not undergo ET) obtained from JRA-55 and ERA-Interim are evaluated against the classification obtained from the best track records. In contrast to the CPS definition of ET, which is automated and objective, the best track definition of ET is given by the subjective judgment of human forecasters who take into account a wider range of data. According to the F1 score and the Matthews correlation coefficient, two performance metrics that balance classification sensitivity and specificity, the CPS classification agrees most with the best track classification in the western North Pacific and the North Atlantic, and least in the eastern North Pacific. The JRA-55 classification achieves higher performance scores than does the ERA-Interim classification, mostly because ERA-Interim has a bias toward cold-core structures in the representation of tropical cyclones. Future projections of ET are examined using a five-member ensemble of a coupled global climate model, the Flux-Adjusted Forecast-oriented Low Ocean Resolution (FLOR-FA) version of CM2.5 developed at the Geophysical Fluid Dynamics Laboratory. First, CPS is applied to 1979-2005 FLOR-FA output to develop a historical ET climatology, which is compared to the 1979-2005 ET climatology obtained from JRA-55. This comparison shows that FLOR-FA simulates many unrealistic low-latitude ET events, due to strong local maxima in the geopotential height fields used as input to calculate the CPS parameters. These local maxima, which arguably result from strong grid-scale convective updrafts, mislead the CPS to detect an upper-level cold core where one is not present. Three solutions to this problem are examined: changing the algorithm to compute the CPS parameters such that it uses 95th percentile values of geopotential instead of the maxima, a temporal smoothing of the CPS parameters, and a combination of the previous two. All three modifications largely correct the misdiagnosed cases. Future (2071-2100) projections of ET activity under the Representative Concentration Pathway 4.5 are then explored. A number of changes between the future and historical simulations are robust with respect to the different modifications to the CPS described above, though few are statistically significant. A statistical model that predicts ET in the western North Pacific and the North Atlantic is introduced. The model, a logistic regression with elastic net regularization, was developed with a focus on predictive performance as well as physical interpretability and thus resides at the interface between machine learning and traditional statistics. It uses eight predictors that characterize the storm and its environment, the most important ones being latitude and sea surface temperature. The model is shown to have skill in forecasting ET at lead times up to two days, and it can predict the phase evolution of storms that undergo ET as well as of storms that remain tropical throughout their lifetimes. When used as an instantaneous diagnostic of a storm's tropical/extratropical status, the model performs about as well as the CPS in the western North Pacific and better than the CPS in the North Atlantic, and it predicts the timings of the transitions better than the CPS in both basins. The model can be integrated into statistical tropical cyclone risk models, or may be applied to provide baseline guidance for operational forecasts.