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Improved Short-Term Atlantic Hurricane Intensity Forecasts Using Reconnaissance- Based Core Measurements David Andrew Murray

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Improved Short-Term Atlantic Hurricane Intensity Forecasts Using Reconnaissance- Based Core Measurements David Andrew Murray Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2009 Improved Short-Term Atlantic Hurricane Intensity Forecasts Using Reconnaissance- Based Core Measurements David Andrew Murray Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES IMPROVED SHORT-TERM ATLANTIC HURRICANE INTENSITY FORECASTS USING RECONNAISSANCE-BASED CORE MEASUREMENTS By DAVID ANDREW MURRAY A Thesis submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Fall Semester, 2009 The members of the committee approve the thesis of David Andrew Murray defended on November 6, 2009. __________________________________ Robert Hart Professor Directing Thesis __________________________________ Carol Anne Clayson Committee Member __________________________________ Philip Sura Committee Member The Graduate School has verified and approved the above-named committee members. ii This thesis is dedicated to my wonderful, loving wife, Marisa Murray, for her tremendous support throughout the process of the research and the writing of the manuscript. Her acceptance of my working late hours and her support at home made completion of this work possible. My appreciation to her for all the sacrifices she made cannot be expressed in words. Thank you, Marisa. iii ACKNOWLEDGEMENTS I would like to thank my committee members, both Dr. Carol Anne Clayson for her advice as well as Dr. Philip Sura for his input regarding the statistical methods involved in this research. I also would like to thank the Hart/Reasor/Ruscher lab for their support and for making days in the lab more enjoyable. Thanks are also due to my parents for their continued support throughout my education. I would also like to thank my best friend, Tim Kurtz, for helping me keep my sanity. Special recognition is due to Ben Schenkel for all the time he invested reviewing my work, offering excellent suggestions, assisting me with coding problems, and proofreading my thesis. His efforts are greatly appreciated. Finally, I would like to recognize my major professor, Dr. Robert Hart, for all of his assistance and advice over the last year and a half. He has been not only a great mentor and advisor but also a good friend, and I thank Bob for everything. This research was funded by NASA Grant #NNX09AC43G and by the Florida Catastrophic Storm Risk Management Center of the Florida State University College of Business. iv TABLE OF CONTENTS List of Tables ............................................................................................. vii List of Figures ............................................................................................ viii Abstract ................................................................................................ xv 1. INTRODUCTION ................................................................................. 1 1.1 Datasets .......................................................................................... 2 1.2 Intensity Forecast Models .............................................................. 3 1.2.1 Climatology and Persistence (CLIPER) Models ............... 3 1.2.2 Dynamical Models ............................................................. 4 1.2.3 Statistical-synoptic and Statistical-dynamical Models ...... 6 1.3 Eyewall Contraction and ERC Theory .......................................... 10 1.4 Previous TC Intensity Forecasting Studies .................................... 12 1.4.1 Rapid Intensity Index ......................................................... 12 1.4.2 Annular Hurricanes ............................................................ 14 1.4.3 Logistic Growth Equation Model (LGEM) ....................... 15 1.4.4 Secondary Eyewall Formation Probability ........................ 16 1.4.5 Inner-core Studies .............................................................. 17 1.5 Objectives ...................................................................................... 18 2. DATA ................................................................................................ 25 2.1 Vortex Data Messages ................................................................... 25 2.2 NHC ATCF Archives .................................................................... 28 2.3 Manual Corrections to ATCF Data ................................................ 29 3. VORTEX DATA MESSAGE CLIMATOLOGY ................................. 32 3.1 Development .................................................................................. 32 3.2 Distribution of VDM Parameters ................................................... 34 3.2.1 Distribution of VDM Reports ............................................ 34 3.2.2 Distribution of Variables Reported in VDMs .................... 36 4. EYE STRUCTURE FORECAST TOOL .............................................. 46 4.1 Development .................................................................................. 46 4.2 Application ..................................................................................... 47 4.2.1 Pattern of Climatological Intensity Change ....................... 47 4.2.2 Relation to Sawyer-Eliassen Nonlinear Balance ............... 51 4.3 Hindcasting Results ....................................................................... 52 5. STATISTICAL REGRESSION FORECAST MODEL (ASPIRE) ...... 60 5.1 Development of Database for Regression ...................................... 60 5.2 Formulation of Forecast Equations ................................................ 62 5.2.1 Overview of Stepwise Multiple Regression ...................... 62 5.2.2 Forecast Equation Sets ....................................................... 63 v 5.3 Predictor Variables ......................................................................... 65 5.4 Interpretation of Equations ............................................................ 68 6. ASPIRE RESULTS ............................................................................... 86 6.1 Testing of Developmental Equations on Independent Datasets .... 87 6.2 Improvement through the Use of Intensity Bins ............................ 89 6.3 Comparison to SHIPS Benchmark ................................................. 90 6.3.1 ASPIRE Performance Stratified by Intensity .................... 90 6.3.2 ASPIRE Performance Stratified by Geography ................. 94 6.4 Implications .................................................................................... 97 7. CASE STUDIES .................................................................................... 113 7.1 Well-Hindcast Case: Hurricane Ivan (2004) ................................. 114 7.1.1 Storm History ..................................................................... 114 7.1.2 Performance and Analysis of ASPIRE Hindcasts ............. 115 7.2 Poorly-Hindcast Case: Hurricane Katrina (2005) ......................... 116 7.2.1 Storm History ..................................................................... 116 7.2.2 Performance and Analysis of ASPIRE Hindcasts ............. 117 8. CONCLUSIONS AND FUTURE WORK ............................................ 126 REFERENCES .......................................................................................... 129 BIOGRAPHICAL SKETCH ...................................................................... 134 vi LIST OF TABLES 1.1: 2003 SHIPS model predictors from DeMaria et al. (2005) ............................ 24 2.1: Explanation of Vortex Data Message along with its field location in the ATCF f-deck archives. .................................................................................... 30 2.2: List of manual changes made to Vortex Messages in ATCF f-deck archives. For storms with more than two reported eyewalls, only the first two are provided below. For eye shape data, 1=Circular; 2=Concentric; 3=Elliptical. ..................................................................................................... 30 3.1: Summary of differences between the VDM climatology presented in this study and that of Piech (2007). ........................................................................ 45 4.1: Summary of the types of TC intensity forecast guidance. .............................. 59 5.1: Summary of types of regression equation sets developed in the ASPIRE technique. ........................................................................................................ 83 5.2: Table of predictors chosen for stepwise regression (NSNC, NS, and TOTAL). Before regression is performed, all predictor distributions were examined for normality and transformed using natural logarithms or exponentials as necessary. Italicized predictor names denote variables which were transformed to achieve approximate normality. The number of times the predictors were selected during the NSNC, NS, and TOTAL methods using the step indicated by the stopping rules is also listed, with a maximum possible of 133 times selected (19 intensity bins by 7 forecast times). .............................................................................................................. 83 5.3: Julian day/Calendar day equivalent. ............................................................... 85 7.1: Number of VDMs by forecast hour for Hurricanes Ivan and Katrina along with the average number of VDMs per TC in the ASPIRE database
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