An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting
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Linköping Studies in Science and Technology Dissertations. No. 1734 An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting by Chandan Roy Department of Computer and Information Science Linköping University SE-581 83 Linköping, Sweden Linköping 2016 Cover image: Hurricane Isabel (2003), NASA, image in public domain. Copyright © 2016 Chandan Roy ISBN: 978-91-7685-854-7 ISSN 0345-7524 Printed by LiU Tryck, Linköping 2015 URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123198 ii Abstract Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems. Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques’ prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results: A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents’ reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions: The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for further research, where biologically based ANNs can be employed for general-purpose weather forecasting, as well as for forecasting other severe weather phenomena, such as thunderstorms. iii iv Populärvetenskaplig sammanfattning Ett stort problem i cyklondrabbade områden är att människor ofta tvekar efter en första cyklonvarning mellan att utföra rekommenderat skyddsarbete hemmavid och försöka ta sig till ett skyddsrum, eller att fortsätta arbeta och stanna på plats och bevaka sitt hus mot inbrott. Problemet är att sedan, när cyklonen är nära, kan det vara för farligt att ge sig ut på vägarna. Ett delarbete i avhandlingen visar på att ett viktigt steg i att motivera människor att evakuera är att kunna erbjuda tillförlitliga tidiga cyklonvarningar. Många länder som är sårbara för cykloner, såsom Bangladesh, saknar de ekonomiska resurser och den informationsinfrastruktur som krävs för att köra avancerade numeriska väderprognosmodeller, och har därför svårt att tillhandahålla tillförlitliga tidiga cyklonvarningar. I avhandlingen utvecklas en ny teknik för att förutse hur en cyklon kommer att röra sig och hur dess intensitet kommer att utvecklas. Tekniken använder sig av biologiskt baserade artificiella neurala nätverk för att bearbeta data som ligger i ett rutmönster av geografiska punkter. Tekniken kan producera tidiga och tillförlitliga cyklonspår- och intensitetsprognoser, samtidigt som den använder fritt tillgängliga satellitdata och inte kräver superdatorer för att köras. En central egenskap hos tekniken är att den tar hänsyn till den rumsliga strukturen hos mätdata, som ger viktiga ledtrådar till spår och intensitetsförändringar hos cykloner. Tekniken baseras på djupa neurala nät, där ett antal dubbelriktat kopplade lager av noder ligger i en hierarkisk struktur som är specifikt utformad för att hantera data med rumslig struktur. Tekniken kombinerar oövervakad och övervakad inlärning på varje beräkningsnivå. För att optimera prestanda har värden för centrala parametrar, såsom storleken på receptiva fält, och förhållandet mellan oövervakat och övervakat inlärning, systematiskt testats. Dessutom har en detaljerad analys gjorts av de interna representationer som utvecklas i dessa nät under träning, och dessa ger vägvisning om vilka faktorer i mätdata som är avgörande för en tillförlitlig cyklonprognos. Den teknik och de resultat som presenteras i avhandlingen kommer att kunna förbättra effektiviteten i cyklonvarnings- och krishanteringssystem i länder som Bangladesh. Resultaten skapar också möjligheter för vidare forskning, där biologiskt baserade artificiella neurala nätverk kan användas för allmänna väderleksprognoser, samt för prognoser av svåra väderfenomen, såsom stormar och åskoväder. v vi Acknowledgements First and foremost I wish to thank my main advisor Rita Kovordányi. I would like to thank her for encouraging my research and for allowing me to grow intellectually. Her constructive guidance on both my research and thesis work, as well as on my career has been invaluable. She has been supportive since the days I started working on biologically based artificial neural networks as a master’s student. She guided me academically and emotionally when writing this thesis and the research articles. Thanks to her, I had the opportunity to develop a tropical cyclone track- and intensity-forecasting technique using biologically based artificial neural networks. My appreciation goes also to my co-advisors Mattias Villani and Henrik Eriksson. Their advices and comments on my thesis manuscript have been very helpful. I want to thank Saroje Kumar Sarkar, Rajshahi University, Bangladesh, and Johan Åberg for helping me with questionnaire formulation and survey data analysis. I am grateful to Raquib Ahmed, Rajshahi University, Bangladesh for his inspiring words and emotional support. Thank you Jalal Maleki and Sture Hägglund for occasionally asking questions about my research and providing constructive advice. Anne Moe, thank you for helping me with the administrative processes related to my thesis defense. I also want to thank all my fellow workers at the Department of Computer and Information Science (IDA) in general for the exceptional working environment that they have created. I always enjoyed my time at IDA. I would like to thank Rajshahi University, Bangladesh, for granting me a paid leave to pursue PhD studies at the Department of Computer and Information Science, Linköping University, Sweden. Finally, I want to thank each and every member of my family for their moral support during these years. Words cannot express how grateful I am to them for all of the sacrifices that they have made for my sake. To my beloved daughter Indira Roy, I would like to express my special thanks for being such a good girl and always cheering me up. Chandan Roy Linköping December, 2015 vii viii Contents Abstract .......................................................................................................................... iii Populärvetenskaplig sammanfattning ............................................................................... v Acknowledgements ............................................................................................................ vii Contents .......................................................................................................................... ix Chapter 1 Introduction ................................................................................................... 13 1.1 Problem description ............................................................................................. 14 1.2 Research objective ............................................................................................... 16 1.3 Studies conducted in this research ....................................................................... 16 1.4 Contributions ....................................................................................................... 17 1.5 Organization