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Analysis and Prediction of Integrated Kinetic Energy in Atlantic Tropical Cyclones Michael E Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2015 Analysis and Prediction of Integrated Kinetic Energy in Atlantic Tropical Cyclones Michael E. Kozar Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES ANALYSIS AND PREDICTION OF INTEGRATED KINETIC ENERGY IN ATLANTIC TROPICAL CYCLONES By MICHAEL E. KOZAR A Dissertation submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Spring Semester, 2015 Michael E. Kozar defended this dissertation on March 19, 2015. The members of the supervisory committee were: Vasubandhu Misra Professor Directing Dissertation Ming Ye University Representative Robert E. Hart Committee Member Philip Sura Committee Member Allan J. Clarke Committee Member Mark D. Powell Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements. ii ACKNOWLEDGMENTS First and foremost, I would like to thank my family for their loving support. Their unwavering encouragement has enabled me to chase my dreams of becoming a meteorologist. I am also grateful for the friendships that I have made here at Florida State and in Tallahassee, back at Penn State and at home in New Jersey, and everywhere else in between. All of the fun that we’ve had through the years has made the difficult task of earning a doctorate degree extremely enjoyable. In addition, I would like to thank my major professor, Dr. Vasu Misra, for his support and guidance throughout my entire time at Florida State. I am also thankful for the helpful advice given to me by my committee members Dr. Robert Hart, Dr. Philip Sura, Dr. Allan Clarke, Dr. Ming Ye, and Dr. Mark Powell. Furthermore, I owe thanks to Dr. Mark Bourassa for his help during my dissertation defense as well as several of my friends and colleagues at COAPS who gave me significant feedback on various presentations and research projects throughout my studies. Finally, I am indebted to all of my past teachers, advisors, professors, committee members, and colleagues who have helped make me a better meteorologist throughout my young career. This work was supported by grants from the National Oceanic and Atmospheric Administration (NA12OAR4310078, NA10OAR4310215, NA11OAR4310110) and the United States Geological Survey (G13AC00408). iii TABLE OF CONTENTS List of Tables ............................................................................................................................. vi List of Figures .......................................................................................................................... viii List of Abbreviations ................................................................................................................. xiv Abstract..................................................................................................................................... xv 1. INTRODUCTION 1 2. REVIEW OF TC STRUCTURE AND ANGULAR MOMENTUM 6 2.1 Conservation of Angular Momentum ............................................................................. 7 2.2 Relationships Between Intensity, Size, and Kinetic Energy ........................................... 9 2.3 Import of Angular Momentum from Trough Interactions .............................................. 14 3. PREDICTION OF IKE: A PROOF OF CONCEPT 27 3.1 Historical Integrated Kinetic Energy Record ................................................................ 28 3.2 Linear Regression Methodology .................................................................................. 30 3.3 Potential Model Predictors .......................................................................................... 32 3.4 Physical Interpretation of Selected Predictors ............................................................. 33 3.5 Linear Model Skill Evaluation from 1990 to 2011 ......................................................... 36 3.6 Validation Tests of Linear Model During 2012 Hurricane Season ................................ 40 3.7 Estimating Linear Model Skill in an Operational Setting .............................................. 42 4. PREDICTION OF IKE WITH NEURAL NETWORKS 52 4.1 Brief Introduction to Neural Networks and their Applications ....................................... 53 4.2 Data Used to Create Neural Networks ........................................................................ 56 4.3 Network Setup and Calibration .................................................................................... 58 4.4 Deterministic Network Skill .......................................................................................... 62 4.5 Probabilistic Network Skill ........................................................................................... 66 5. NEURAL NETWORK SENSITIVITY TESTS 81 5.1 Sensitivity Experiment Setup ....................................................................................... 82 5.2 Persistence Perturbation Tests ................................................................................... 84 5.3 Intensity Perturbation Tests ........................................................................................ 86 5.4 Position Perturbation Tests ......................................................................................... 88 5.5 Upper-level Divergence and Low-level Vorticity Perturbation Tests ............................. 91 5.6 Relative Humidity Perturbation Tests .......................................................................... 93 5.7 Deep-layer Vertical Wind Shear Perturbation Tests ................................................... 96 5.8 Sea Surface Temperature Perturbation Tests ............................................................. 98 5.9 Time Perturbation Tests ............................................................................................ 100 5.10 Potential Intensity Perturbation Tests ...................................................................... 101 5.11 Other Perturbation Tests ......................................................................................... 103 6. EVALUATION OF NEURAL NETWORKS FOR REAL-TIME USE 109 6.1 Data Used to Create Mock-Operational Network ....................................................... 110 6.2 Mock-Operational Neural Network Setup and Calibration.......................................... 114 6.3 Mock Operational Network Skill Evaluation ............................................................... 116 7. APPLYING IKE FORECASTS TO PREDICT STORM SIZE 136 7.1 Symmetric Wind Model Design ................................................................................. 137 iv 7.2 Evaluation of Symmetric Wind Model in a Perfect-Prog Mode ................................... 139 7.3 Considering Wind Field Asymmetries ........................................................................ 142 7.4 Assessment of Operational Skill in Symmetric Wind Model ....................................... 145 8. SUMMARY AND CONCLUSIONS 167 References ............................................................................................................................. 174 Biographical Sketch ................................................................................................................ 179 v LIST OF TABLES Table 3.1: Variables considered for use in the SPIKE models. Many of the variables (e.g. RHLO, SHRD) originated from SHIPS and are averaged over specific areas as noted. Others were specifically created from observations for the SPIKE model. Not all of these variables are used in the final models, as many of the regression coefficients fail significance tests in the backward screening methodology. ............................................................................................ 44 Table 3.2: Regression coefficients for each predictor in the SPIKE model. The coefficients listed in a blue font are significant at the 99% level for that forecast hour. Sample size for the training interval of 1990 to 2011 is shown below. Finally, shared variance between the SPIKE regression model, and observed kinetic energy changes are listed in the bottom row. ............................... 45 Table 4.1: Three sets of example input and corresponding output for the sample neuron setup shown in Figure 4.1. The bias value is by definition equal to one, but the other input values are free to vary. The weights within the neuron, which are identical in each example, are detailed in Equations 4.1. The propagation function is a weighted sum, and the activation function is a piecewise function, as explained in Equations 4.2. ................................................................... 74 Table 4.2: Variables used in the perfect prognostic version of the SPIKE2 neural networks. Many of the variables (e.g. RHLO, SHRD) originated from SHIPS and are averaged over specific areas as noted. Others were specifically created from observations for the SPIKE model. ...................................................................................................................................... 74 Table 4.3: Terms used to describe the probability of a certain event occurring given its likelihood from a probabilistic forecast. ....................................................................................
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