Airborne radar quality control and analysis of the rapid intensification of Hurricane Michael (2018)

2020 Fall.Includes bibliographical references.Improvements made by the National Hurricane Center (NHC) in track forecasts have outpaced advances in intensity forecasting. Rapid intensification (RI), an increase of at least 30 knots in the maximum sustained winds of a tropical cyclone (TC) in a 24 hour period, is poorly understood and provides a considerable hurdle to intensity forecasting. RI depends on internal processes which require detailed inner core information to better understand. Close range measurements of TCs from aircraft reconnaissance with tail Doppler radar (TDR) allow for the retrieval of the kinematic state of the inner core. Fourteen consecutive passes were flown through Hurricane Michael (2018) as it underwent RI on its way to landfall at category 5 intensity. The TDR data collected offered an exceptional opportunity to diagnose mechanisms that contributed to RI. Quality Control (QC) is required to remove radar gates originating from non meteorological sources which can impair dual-Doppler wind synthesis techniques. Automation of the time-consuming manual QC process was needed to utilize all TDR data collected in Hurricane Michael in a timely manner. The machine learning (ML) random forest technique was employed to create a generalized QC method for TDR data collected in convective environments. The complex decision making ability of ML offered an advantage over past approaches. A dataset of radar scans from a tornadic supercell, bow echo, and mature and developing TCs collected by the Electra Doppler Radar (ELDORA) containing approximately 87.9 million radar gates was mined for predictors. Previous manual QC performed on the data was used to classify each data point as weather or non-weather. This varied dataset was used to train a model which classified over 99% of the radar gates in the withheld testing data succesfully. Creation of a dual-Doppler analysis from a tropical depression using ML efforts that was comparable to manual QC confirmed the utility of this new method. The framework developed was capable of performing QC on the majority of the TDR data from Hurricane Michael. Analyses of the inner core of Hurricane Michael were used to document inner core changes throughout RI. Angular momentum surfaces moved radially inward and became more vertically aligned over time. The hurricane force wind field expanded radially outward and increased in depth. Intensification of the storm became predominantly axisymmetric as RI progressed. TDR-derived winds are used to infer upper-level processes that influenced RI at the surface. Tilting of ambient horizontal vorticity, created by the decay of tangential winds aloft, by the axisymmetric updraft created a positive vorticity tendency atop the existing vorticity tower. A vorticity budget helped demonstrate how the axisymmetric vorticity tower built both upward and outward in the sloped eyewall. A retrieval of the radial gradient of density temperature provided evidence for an increasing warm core temperature perturbation in the eye. Growth of the warm core temperature perturbation in upper levels aided by subsidence helped lower the minimum sea level pressure which correlated with intensification of the near-surface wind field

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Feasibility of Using Classification Analyses to Determine Tropical Cyclone Rapid Intensification

Tropical cyclone intensity techniques developed by Dvorak have thus far been regarded by tropical meteorologists as the best identification and forecast schemes available using satellite imagery. However, in recent years, several ideologies have arisen which discuss alternative means of determining typhoon rapid intensification or weakening in the Pacific. These theories include examining channel outflow patterns, potential vorticity superposition and anomalies, tropical upper tropospheric trough interactions, environmental influences, and upper tropospheric flow transitions. It is now possible to data mine these atmospheric parameters thought partly responsible for typhoon rapid intensification and weakening to validate their usefulness in the forecast process. Using the latest data mining software tools, this study used components of NOGAPS analyses along with selected atmospheric and climatological predictors in classification analyses to create conditional forecast decision trees. The results of the classification model show an approximate R2 of 0.68 with percent error misclassifications of 13.5% for rapidly weakening typhoon events and 21.8% for rapidly intensifying typhoon events. In addition, a merged set of suggested forecast splitting rules was developed. By using the three most accurate predictors from both intensifying and weakening storms, the results validate the notion that multiple parameters are responsible for rapid changes in typhoon development

Master of Science

thesisGlobal analysis fields, infrared and passive microwave satellite observations, lightning data, and airborne radar reflectivity and dual-Doppler wind analyses show the evolution of environmental conditions, precipitation characteristics, and kinematic structure before, during, and after the rapid intensification (RI) of Hurricane Earl (2010). The relationship between the RI and environmental conditions, intense inner-core convection, inner-core precipitation coverage, core cold-cloud precipitation symmetry, and the radial distribution of convection is examined. The onset of RI occurs despite moderate vertical wind shear. An episode of intense convection occurs before the RI onset, but an examination of the mesoscale and convective-scale kinematic processes during this convective ‘burst' suggests that the strength of convection alone did not cause the onset of RI. Instead, the dual-Doppler, lightning, and microwave data suggest that the precipitation characteristic that ultimately led to the onset of RI was an increasing trend in cold-cloud precipitation symmetry following the migration of inner-core convection into the northeastern and northern quadrants of the storm within a few hours before RI onset. The evolution of precipitation during the RI suggests that the most important inner-core precipitation characteristics supporting RI are the cold-cloud precipitation symmetry and the predominance of strong convective updrafts within (instead of outside of) the radius of maximum wind (RMW). The wind and precipitation data from Earl indicate that the RMW at multiple levels must be examined. When the RMW is substantially slanted, only considering the low-level RMW can lead to the false conclusion that the strongest convection is located outside of the RMW

Global Environmental Microelectromechanical Systems Sensors: Advanced Weather Observation System

The technological developments in microelectromechanical systems (MEMS) have resulted in conceptualisation of a next generation observation system called global environmental MEMS sensors (GEMS). GEMS consists of a large number of airborne probes that will remain suspended in the atmosphere for long durations and take in situ measurements of pressure, temperature, humidity, wind direction and velocity as these are carried by air currents. It is envisaged that GEMS network would provide a systematic understanding of the earths atmosphere and would improve weather forecast accuracy, well beyond the current capability. In addition to gathering meteorological data, probes could be used for environmental monitoring of particulate emissions, organic and inorganic pollutants, ozone, carbon dioxide, and chemical, biological, or nuclear contaminants. The GEMS concept requires integration of communication engineering and instrumentation with other evolving technologies. This review describes in detail the new observation system designed for environmental monitoring and its potential application in predicting cyclones and monsoon, and measurement of urban air pollution in India. The possible application of the GEMS system during military operations has also been brought out.Defence Science Journal, 2009, 59(6), pp.659-665, DOI:http://dx.doi.org/10.14429/dsj.59.157

Lessons Learned from Oil Pipeline Natech Accidents and Recommendations for Natech Scenario Development - Final Report

Natural hazards can impact oil transmission pipelines with potentially adverse consequences on the population and the environment. They can also cause significant economic impacts to pipeline operators. Currently, there is only limited historical information available on the dynamics of natural hazard impact on pipelines and Action A6 of the EPCIP 2012 Programme aimed at shedding light on this issue. This report presents the findings of the second year of the study that focused on the analysis of onshore hazardous liquid transmission pipeline natechs, with special emphasis on natural hazard impact and damage modes, incident consequences, and lessons learned for scenario building. Due to the limited amount of data available on European pipeline natech incidents, the study was supplemented with information from U.S. pipeline natech incidents.JRC.G.5-Security technology assessmen

NASA/MSFC FY-85 Atmospheric Processes Research Review

The two main areas of focus for the research program are global scale processes and mesoscale processes. Geophysical fluid processes, satellite doppler lidar, satellite data analysis, atmospheric electricity, doppler lidar wind research, and mesoscale modeling are among the topics covered