A Study of Tropical Cyclone Track Sinuosity in the Southwest Pacific

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A Study of Tropical Cyclone Track Sinuosity in the Southwest Pacific CLIMATIC VARIABILITY: A STUDY OF TROPICAL CYCLONE TRACK SINUOSITY IN THE SOUTHWEST PACIFIC by Arti Pratap Chand A supervised research project submitted in partial fulfillment of the requirements for the degree of Masters of Science (M.Sc.) in Environmental Sciences Copyright © 2012 by Arti Pratap Chand School of Geography, Earth Science and Environment Faculty of Science and Technology and Environment The University of the South Pacific October, 2012 DECLARATION Statement by Author I, Arti Pratap Chand, declare that this thesis is my own work and that, to the best of my knowledge, it contains no material previously published, or substantially overlapping with material submitted for the award of any other degree at any institution, except where due acknowledgement is made in the text. Signature……………………………………… Date…18th October 2012… Arti Pratap Chand Student ID No.: S99007704 Statement by Supervisors The research in this thesis was performed under our supervision and to our knowledge is the sole work of Ms Arti Pratap Chand Signature……………………………………… Date…18th October 2012….. Principal Supervisor: Dr M G M Khan Designation: Associate Professor in Statistics, University of the South Pacific Signature…… ……….Date……18th October 2012…… Co - supervisor: Dr James P. Terry Designation: Associate Professor in Geography, National University of Singapore Signature……………………………………… Date…18th October 2012…… Co - supervisor: Dr Gennady Gienko Designation: Associate Professor in Geomatics, University of Alaska Anchorage DEDICATION To all tropical cyclone victims. i ACKNOWLEDGEMENTS I am heartily thankful to my co – supervisor, Dr Gennady Gienko, whose trust, encouragement and initial discussions lead me to this topic. I would like to gratefully acknowledge my Principal Supervisor, Dr MGM Khan and my co – supervisor Dr James Terry for their advice, guidance and support from the initial to the final level enabling me to develop an understanding of the subject and statistical techniques. I would also like to acknowledge and thank Dr Gennady and Dr Shingo Takeda for helping me with displaying my results using ArcGIS software. My sincere thanks to them for their time and patience. My sincere thanks and appreciation goes to Dr MGM Khan and his student for helping me with C++ programming technique. I would also like to thank Dr Tony Weir and Mr Rajendra Prasad (former Director of the Fiji Meteorological Services) for discussions I had with them regarding my thesis topic. Special thanks to my family for their encouragement and moral support. ii ABSTRACT Tropical cyclones (TCs) are one of the most destructive natural hazards in the tropical Pacific, with large impacts on socio-economic and environmental sectors of island nations. Improved understanding of the characteristics of these intense storms is critical. A continuing problem lies in forecasting TC movement after formation. One way to add to existing knowledge in this area is to analyse available data on cyclone track shape, in order to identify any special patterns. In this context, this study examines statistical characteristics of several TC track parameters, using archived data from 1970 to 2008 for the South Pacific region. The dataset includes information on 292 TCs, which includes all storms with wind intensity of 35 knots and above that have their genesis in tropical waters. TC paths are analysed within the geographical grid covered by 0 – 25°S and 160° E – 120° W. The particular focus of this study is on track sinuosity values and how these may be characterised and grouped. River sinuosity has contributed a lot in understanding fluvial geomorphology (Terry and Feng, 2010) and therefore extending the technique to study TC track maybe useful. A sinuous track having loops and curves will affect many more islands than a TC moving along a straight path. Some Islands may be affected more than once or may be exposed to a TC for a longer time period if the TC makes a loop during its journey. Sinuosity values for all TC tracks were calculated by measuring the total distance travelled by each TC and then dividing this by the vector displacement between cyclogenesis and decay positions. In this study, the problem of categorising the TCs based on sinuosity index (SI) values obtained by transformation of sinuosity values allows the grouping of similar TCs. The SI categories are so constructed that the variance of groups is as small as possible. Thus in this thesis a technique is developed to construct the SI categories of the TCs that seek minimization of the sum of weighted deviations of SI from the mean of group. Then the problem is solved for determining the optimum boundary points of the groups by using a dynamic programming technique. iii Three TCs from the dataset were found to have very high SI values and therefore were grouped in a separate SI category as an outlier category. Then the remaining TCs were grouped into five homogeneous sinuosity index categories using proposed method within which the TCs were very similar. The results from above method were compared with the SI categories obtained by hierarchical cluster analysis with Ward’s method. The comparison results show that the SI categories constructed by the proposed method are more homogenous with respect to the sinuosity index values of the TC tracks. The homogenous SI categories obtained was further explored using GIS tool to study the geographical distribution of these SI categories in the study area. Keywords: Track Sinuosity, Cyclogenesis and decay positions, Homogeneous Categories iv ABBREVIATIONS IPCC Intergovernmental Panel on Climate Change SI Sinuosity Index TC Tropical Cyclones v TABLE OF CONTENTS DEDICATION i ACKNOWLEDGMENT ii ABSTRACT iii ABBREVIATIONS v TABLE OF CONTENTS vi LIST OF FIGURES ix LIST OF TABLES x LIST OF APPENDICES xi CHAPTER 1: INTRODUCTION 1 1.1. Tropical cyclones in the Pacific Region 1 1.2. Tropical cyclone variability 3 1.3. Tropical cyclone classification 4 1.4. Tropical cyclone tracks 6 1.5. Sinuosity of cyclone tracks 6 1.6. Research objectives 12 1.7. Chapter organizations 12 CHAPTER 2: LITERATURE REVIEW 14 CHAPTER 3: DATA AND METHODS 20 3.1 Study area and data collection 20 3.2 Sinuosity calculation 22 3.3 Distribution of Sinuosity values 22 3.3.1 Analysis of extreme Tropical Cyclones from sinuosity data 24 3.3.2 Sinuosity index 26 3.3.3 Analysis of extreme Tropical Cyclones from sinuosity index data 26 3.4 Correlation of sinuosity index with other parameters 28 3.5 Methodology for grouping the sinuosity index: a proposed technique 29 vi 3.5.1 Estimate of the distribution of sinuosity index values 31 3.5.2 Estimate of the parameters of distribution 32 3.5.3 Determination of optimum grouping using dynamic programming technique 32 3.6 Alternative methodology for grouping the sinuosity index using Hierarchical Cluster Analysis 34 3.7 A comparison study of grouping methods 35 CHAPTER 4: RESULTS AND INTERPRETATIONS 36 4.1 Tropical Cyclone frequency 36 4.2 Average sinuosity index 36 4.3 Correlation of average sinuosity index with southern oscillation index 37 4.4 Correlation of sinuosity with other tropical cyclone parameters 39 4.4.1. Correlation of sinuosity index with start latitude 39 4.4.2. Correlation of sinuosity index with start longitude 39 4.4.3. Correlation of sinuosity index with end longitude 39 4.4.4. Correlation of sinuosity index with time 40 4.4.5. Correlation of sinuosity index with duration 40 4.5 Grouping the sinuosity index values 40 4.6. Geographical distribution of the tropical cyclone genesis and decay positions 41 4.7 Tropical Cyclone frequency and percentages in different tropical cyclone months for the five categories 45 4.8 Mean values for other parameters of the tropical cyclone tracks in relation to the sinuosity index category mean 46 vii CHAPTER 5: DISCUSSION 47 5.1 Tropical cyclone genesis position and sinuosity index 48 5.2 Tropical cyclone decay position and sinuosity index 49 5.3 Tropical cyclone journey and sinuosity index 50 5.4 Sinuosity Index categories 50 CHAPTER 6: CONCLUSIONS 52 REFERENCES 55 APPENDICES 60 viii LIST OF FIGURES Figure 1 An aerial photograph of Nadi during March 2012 flooding 2 Figure 2 Flooding in Nadi in April 2012 2 Figure 3 Tropical Cyclone Henrieta track of sinuosity value 1.01 7 Figure 4 Tropical Cyclone Daman track of sinuosity value 1.07 8 Figure 5 Tropical Cyclone Tomas track of sinuosity value 1.16 8 Figure 6 Tropical Cyclone Gavin track of sinuosity value 1.34 9 Figure 7 Tropical Cyclone Xavier track of sinuosity value 1.75 9 Figure 8 Tropical Cyclone Rewa track of sinuosity value 4.36 10 Figure 9 Tropical Cyclone Rewa (28 December 1993 – 21 January 1994) 16 Figure 10 Tropical Cyclone Zaka (1995) 17 Figure 11 Tropical Cyclone Rae, Olaf, Meena, Percy and Nancy 18 Figure 12 Map of study area 21 Figure 13 Map of study area with 291 TC tracks during 1969/70 – 2007/08 cyclone seasons 21 Figure 14 Sinuosity values for each tropical cyclone track was calculated 22 Figure 15 Histogram for the sinuosity values 23 Figure 16 Boxplot analysis of sinuosity values 24 Figure 17 Boxplot analysis of sinuosity index 27 Figure 18 Dotplot of the sinuosity index 28 Figure 19 P-P plots of sinuosity index 31 Figure 20 Frequency distribution of sinuosity index 32 Figure 21 Tropical Cyclone frequency against tropical cyclone seasons (1969/70 – 2007/08) 36 Figure 22 Graph of average sinuosity index against tropical cyclone seasons 37 Figure 23 Tropical Cyclone displacement tracks for the 291 tropical cyclones that occurred between (1969/70 – 2007/08) 42 Figure
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