Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 187-192).

Thesis (Ph. D.)--Texas State University-San Marcos, 2006. / Vita. Appendices: leaves 119-142. Includes bibliographical references (leaves 143-147).

Approved for public release, distribution is unlimited / This thesis investigated the hypothesis that variations in tropical cyclone (TC) activity in the western North Pacific (WNP) may affect the teleconnection between the tropical WNP and North America. The teleconnection patterns of the 500 hPa geopotential height between a base point in the WNP (20 N 115 E) and a domain over North America (30 - 45 N, 70 -90 W) from 1951-2001 were examined. The 25 most active and the 25 least active TC years for two regions with the highest climatological average of TC activity, near the Philippines and Taiwan, respectively, were compared to determine if stronger teleconnection patterns occur during the more active years. For both regions, the correlation pattern is significant during active years and insignificant during inactive years, with the results based on TC activity in the Philippines region showing a larger difference. An analysis of 500 hPa mean winds showed weaker winds in the midlatitudes during active TC years when the teleconnection is stronger, which suggests that the teleconnection may consist mainly of Lau and Weng's (2000) zonally-elongated mode (Mode 1). Further cross correlations of the geopotential height and TC frequency parameters with the tropical eastern and western Pacific sea-surface temperatures (SST's) showed a significant correlation between TC activity and tropical eastern Pacific SST's, but the North America-WNP correlation is unlikely to be a result of a direct influence of SST's on the two regions. / Captain, United States Air Force

When a tropical cyclone (TC) is within 360 nautical miles of Kadena AB, the Air Force's Typhoon Determination (TYDET) program is used to estimate TC-induced winds expected at the base. Best-track data and Joint Typhoon Warning Center (JTWC) forecasts are used to evaluate systematic errors in TYDET. The largest contributors to errors in TYDET are a systematic error by which wind speeds are too large and the lack of size and symmetry parameters. To examine these parameters, best-track and forecasts are used to classify TCs as small or large and symmetric or asymmetric. A linear regression technique is then used to adjust TYDET forecasts based on the best-track and forecast position, size, and symmetry categories. Using independent data, over 65 percent of the overall cross-wind forecasts were improved and more than 60 percent of the cross-wind forecasts were improved when verifying conditions noted a cross-wind of 20 knots or greater. The effectiveness of the corrections and implications for TYDET forecasts are examined in relation to errors in forecast data used to initialize TYDET. A similar approach as developed here for the TYDET model at Kadena AB is proposed for other bases within the Pacific theater.

Abstract not available

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 181-193).

No description available.

No description available.

Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems. Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques’ prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results: A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents’ reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions: The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for further research, where biologically based ANNs can be employed for general-purpose weather forecasting, as well as for forecasting other severe weather phenomena, such as thunderstorms.

Thesis (Ph.D.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.