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Title:
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INTENSITY PREDICTION MODEL FOR TROPICAL CYCLONE RAPID INTENSIFICATION EVENTS |
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Author(s):
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Hadil Shaiba, Michael Hahsler |
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ISBN:
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978-989-8533-20-3 |
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Editors:
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Hans Weghorn |
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Year:
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2013 |
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Edition:
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Single |
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Keywords:
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Tropical Cyclone, Rapid Intensification, Data Mining, Clustering, Bayesian Inference. |
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Type:
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Short Paper |
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First Page:
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173 |
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Last Page:
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177 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Tropical Cyclones (TC) create strong wind and rain and can cause significant human and financial losses. Many major hurricanes in the Atlantic Ocean undergo rapid intensification (RI). RI events happen when the strength of the storm increases rapidly within 24 hours. Improving the hurricanes intensity prediction model by accurately detecting the occurrence and predicting the intensity of an RI event can help avoid human and financial losses. In this paper we analyzed RI events in the Atlantic Basin and investigated the use of a combination of three different models to predict RI events. The first and second models are simple location and time-based models which use the conditional probability of intensification given the day of the year and the location of the storm. The third model uses a data mining technique which is based on the Extensible Markov Chain Model (EMM) which clusters the hurricanes life cycle into states and then uses the transition probabilities between these states for prediction. One of the main characteristics of a Markov Chain is that the next state depends only on the current state and by knowing the current state we can predict future states which provide us with estimates for future intensities. In future research we plan to test each model independently and combinations of the models by comparing them to the current best prediction models. |
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