North Atlantic Hurricane Trend Analysis using Parallel Coordinates and Statistical Techniques
One of the most challenging tasks in multidimensional multivariate data analysis is to identify and quantify the associations between a set of interrelated variables. In real-world climate studies, this task is even more daunting due to the uncertainty and complexity of dynamic, environmental data s...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.353.1828 http://cv.infowantstobeseen.org/papers/Steed-2008-NAH.pdf |
| Summary: | One of the most challenging tasks in multidimensional multivariate data analysis is to identify and quantify the associations between a set of interrelated variables. In real-world climate studies, this task is even more daunting due to the uncertainty and complexity of dynamic, environmental data sets. Notwithstanding the difficulty, the variability and destructiveness of recent hurricane seasons has invigorated efforts by weather scientists to identify environmental variables that have the greatest impact on the intensity and frequency of seasonal hurricane activity. In general, the goal of such efforts is to improve the accuracy of seasonal forecasts which should, in turn, improve preparedness and reduce the impact of these devastating natural disasters. One particularly useful method for predicting seasonal hurricane variability is based on the idea that there are predictors of the main dynamic parameters that affect storm activity which can be observed up to a year in advance. Using historical data, their importance is estimated using statistical regression techniques similar to those described by Vitart [1]. Klotzbach et al. [2] used these technique to determine the most important variables for predicting the frequency of North Atlantic tropical cyclone activity. Similarly, Fitzpatrick [3] applied stepwise regression analysis to the prediction of tropical cyclone intensity. Although sometimes complicated to establish, these techniques provide an ordered list of the most important predictors for the dynamic parameters. Scientists gain additional insight in these studies by evaluating descriptive statistics and performing correlation analysis. |
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