The Federal Aviation Administration (FAA) has been modernizing the United States' air transportation system within a series of initiatives called the Next Generation Air Transportation System (NextGen). The goal of NextGen is to increase the safety, efficiency, capacity, predictability, and resiliency of American Air Traffic Control (ATC) by implementing satellite-based communication, and navigation systems. Because of the vast oceanic areas controlled by Oakland, New York, and Anchorage air traffic control centers, improving oceanic operations is significant for the United States. According to the FAA, oceanic flights generate 31% of passenger revenue and 40% of cargo revenue in U.S.-controlled airspace. New NextGen procedures offer the opportunity for aircraft to save fuel consumption by allowing oceanic flights to fly at more efficient routes and flight levels. This dissertation investigates three areas to improve flight operations over oceanic airspace. The first area studies the operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace. This research effort uses two approaches as follows: 1) statistical flight analysis, and 2) simulation-based analysis. The second area provides an optimization technique to improve the current procedures for assigning flights to the Organized Track System (OTS) in the Atlantic Ocean based on the Collaborative Decision Making (CDM) concept. The third area investigates the potential savings of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. To quantify the operational benefits of the proposed procedures, a fast-time simulation tool, the Global Oceanic (GO) model, is developed and employed. The GO model is a microscopic flight simulation tool that has been developing by the Air Transportation Systems Laboratory at Virginia Tech offering realistic and inexpensive evaluations of novel technologies and procedures to improve flight operations over global oceanic airspace. the results of these studies are analyzed in terms of fuel consumption, travel distance, travel time, level of service, and potential air traffic controllers' workload. / Doctor of Philosophy / The economic growth and social connectivity of nations are highly correlated to effective and efficient air transportation systems. The Federal Aviation Administration (FAA) has initiated a program to modernize America's air transportation system and make flight operations safer, and more efficient. This program is called the Next Generation Air Transportation System (NextGen) and its goal is transforming the communication and navigation technologies to satellite-based systems. Improving oceanic flights is one of the main concerns of the NextGen program since the United States controls massive oceanic areas in the Atlantic and the Pacific Ocean. The FAA needs to evaluate the benefits and costs of advanced technologies and procedures to justify the NextGen initiatives. The FAA has employed computer simulation tools to support decisions for future infrastructure investments and encourage airlines to equip their aircraft with more advanced avionics. The Global Oceanic (GO) model is a microscopic flight simulation tool developed jointly by the Air Transportation Systems Laboratory at Virginia Tech and the FAA providing quick, realistic, and inexpensive evaluations of advanced procedures to improve flight operations over oceans. This dissertation investigates the operational benefit of three advanced procedures using the GO model. The areas to improve flight operations over oceanic airspace are as follows: 1) operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace, 2) operational benefits of an optimization technique for flight assignments to the Organized Track System (OTS) in the Atlantic Ocean, 3) operational benefits of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. These studies quantify the potential savings of these procedures in terms of reducing fuel consumption, travel distance, travel time, greenhouse gas emissions, and potential air traffic controllers' workload.

The use of numerical weather prediction (NWP) has brought significant improvements to severe weather outbreak forecasting; however, determination of the primary mode of severe weather (in particular tornadic and nontornadic outbreaks) continues to be a challenge. Uncertainty in model runs contributes to forecasting difficulty; therefore it is beneficial to a forecaster to understand the sources and magnitude of uncertainty in a severe weather forecast. This research examines the impact of data assimilation, microphysics parameterizations, and planetary boundary layer (PBL) physics parameterizations on severe weather forecast accuracy and model variability, both at a mesoscale and synoptic-scale level. NWP model simulations of twenty United States tornadic and twenty nontornadic outbreaks are generated. In the first research phase, each case is modeled with three different modes of data assimilation and a control. In the second phase, each event is modeled with 15 combinations of physics parameterizations: five microphysics and three PBL, all of which were designed to perform well in convective weather situations. A learning machine technique known as a support vector machine (SVM) is used to predict outbreak mode for each run for both the data assimilated model simulations and the different parameterization simulations. Parameters determined to be significant for outbreak discrimination are extracted from the model simulations and input to the SVM, which issues a diagnosis of outbreak type (tornadic or nontornadic) for each model run. In the third phase, standard synoptic parameters are extracted from the model simulations and a k-means cluster analysis is performed on tornadic and nontornadic outbreak data sets to generate synoptically distinct clusters representing atmospheric conditions found in each type of outbreak. Variations among the synoptic features in each cluster are examined across the varied physics parameterization and data assimilation runs. Phase I found that conventional and HIRS-4 radiance assimilation performs best of all examined assimilation variations by lowering false alarm ratios relative to other runs. Phase II found that the selection of PBL physics produces greater spread in the SVM classification ability. Phase III found that data assimilation generates greater model changes in the strength of synoptic-scale features than either microphysics or PBL physics parameterization.