Atlantic Basin tropical cyclone storm track analysis

Atlantic Basin Tropical Cyclone Storm Track Analysis Desmond CarrollAdvisor: Frank Hardisty

1GoalsAnalyze 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.

2Background3

N. Atlantic Tropical Cyclone Season Not exclusively an American problem.

CubaThe BahamasCaymanContrast the magnitude of the Caribbean basins susceptibility to that of the United States. Mention that techniques and approaches for analysis wlll naturally steer toward the

4

The North Atlantic Basin(Google Earth, 2010)

5The NOAA HURDAT Dataset

(NOAA, 2010)6The Extended Best Track Dataset

7Synthetic Track GenerationAugment the existing data.Analyze the extreme cases.Compare probabilistic versus deterministic.

8ProblemSmall densely populated islands.A line track may be too general.Neither the HURDAT nor EBT alone sufficient.9Research QuestionsWill 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?

10ObjectivesBuffer AnalysisGenerate a method for creating buffers from EBT data

(ESRI, 1997)12Modeling Asymmetry

Get closer to simulating the real variability in storm paths.(NASA, 2006)13Track AnalysisAugment the HURDAT with the data similar to EBT

(Rogers and Spirnak, 2006)14VisualizationProvide a public outlet.Place the focus on the less studied islands.Explore HTML5s Canvas element.

15MethodsLibrariesGeospatial LibrariesOpen Source solutionsSolve well known problemsPython bindings

BuffersGDALGeospatial Data Abstraction LibraryData manipulationProjections

18

BuffersShapelyPostGIS InspiredConvex HullCascading Union Algorithms

19Initial Run

20Markov Chain Monte CarloUnknown processes.Probability functions.Many applications.21

MCMC Random Walk

(Patil et al., 2010)(Patil et al., 2010)Application to HURDATBack test model for validity.Augment storm tracks with asymmetry.Extreme events can be examined in more. detail.23Current Web Visualization TechnologiesJavascript clientHTML5 Canvas ElementOpenlayers frameworkGeoserver middlewarePostGRESQL/PostGIS backend

24Site Layout

http://loggedout.org25Future WorkConduct analysis for various storm strengths.Investigate temporal effects.Couple with storm track model.Employ more deterministic factors.

26Questions?ReferencesDemuth, 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. 299-314Hall 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 917National Oceanic and Atmospheric Administration. (2005, December 9). National Hurricane Center. Retrieved May 25, 2010, from National Hurrican Center: http://www.nhc.noaa.gov/

ReferencesPatil, 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. 383-398Wang 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