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Atlantic Basin Tropical Cyclone Storm Track Analysis Desmond Carroll Advisor: Frank Hardisty

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Goals Analyze Tropical Cyclone susceptibility in the Caribbean. Enhance seasonal forecasts by examining correlation between basin wide activity and small island risk. Develop a web based visualization of the results.

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Background

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N. Atlantic Tropical Cyclone Season Not exclusively an American problem. Cuba The Bahamas Cayman

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The North Atlantic Basin (Google Earth, 2010)

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The NOAA HURDAT Dataset (NOAA, 2010)

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The Extended Best Track Dataset

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Synthetic Track Generation Augment the existing data. Analyze the extreme cases. Compare probabilistic versus deterministic.

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Problem Small densely populated islands. A line track may be too general. Neither the HURDAT nor EBT alone sufficient.

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Research Questions Will the inclusion of storm size in track modeling problems improve the quality of the results in the caribbean? Is there any link between the total number of named storms in the Atlantic basin and the susceptibility of individual islands in The Bahamas?

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Objectives

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Buffer Analysis Generate a method for creating buffers from EBT data (ESRI, 1997)

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Modeling Asymmetry Get closer to simulating the real variability in storm paths. (NASA, 2006)

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Track Analysis Augment the HURDAT with the data similar to EBT (Rogers and Spirnak, 2006)

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Visualization Provide a public outlet. Place the focus on the less studied islands. Explore HTML5s Canvas element.

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Methods

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Libraries Geospatial Libraries Open Source solutions Solve well known problems Python bindings

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Buffers GDAL Geospatial Data Abstraction Library Data manipulation Projections

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Buffers Shapely PostGIS Inspired Convex Hull Cascading Union Algorithms

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Initial Run

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Markov Chain Monte Carlo Unknown processes. Probability functions. Many applications.

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MCMC Random Walk (Patil et al., 2010)

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Application to HURDAT Back test model for validity. Augment storm tracks with asymmetry. Extreme events can be examined in more. detail.

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Current Web Visualization Technologies Javascript client HTML5 Canvas Element Openlayers framework Geoserver middleware PostGRESQL/PostGIS backend

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Site Layout http://loggedout.org

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Future Work Conduct analysis for various storm strengths. Investigate temporal effects. Couple with storm track model. Employ more deterministic factors.

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Questions?

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References Demuth, J., M. DeMaria, and J.A. Knaff, 2006: Improvement of advanced microwave sounder unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor., 45, 1573-1581. Emanuel et al. A statistical deterministic approach to hurricane risk assessment. Bulletin of the American Meteorological Society (2006) vol. 87 (3) pp. 299-314 Hall and Jewson. Statistical modelling of North Atlantic tropical cyclone tracks. Tellus A (2007) vol. 59 (4) pp. 486-498 Jiechen Wang et al. Review of Buffer Generation Algorithm Studies. Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on (2008) vol. 2 pp. 911 917 National Oceanic and Atmospheric Administration. (2005, December 9). National Hurricane Center. Retrieved May 25, 2010, from National Hurrican Center: http://www.nhc.noaa.gov/ http://www.nhc.noaa.gov/

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References Patil, A., D. Huard and C.J. Fonnesbeck. 2010. PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical Software, 35(4), pp. 1-81. Rumpf et al. Stochastic modelling of tropical cyclone tracks. Mathematical Methods of Operations Research (2007) vol. 66 (3) pp. 475-490 Rumpf et al. Tropical cyclone hazard assessment using model-based track simulation. Natural hazards (2009) vol. 48 (3) pp. 383-398 Wang Jiechen et al. A Novel Method of Buffer Generation Based on Vector Boundary Tracing. Information Technology and Applications, 2009. IFITA '09. International Forum on (2009) vol. 1 pp. 579 - 582