Conclusions

This study illustrated implementation of tropical cyclone simulation methodology in two stages. In the Chapter 2, the first stage (formation -- genesis) of the tropical cyclone simulation methodology was described. This chapter conducted a literature review and critique of existing statistical models for simulating storm genesis locations. The existing models did not accurately estimate the spatial or temporal distributions of historical genesis points. Improved accuracy for

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historical genesis points has been achieved with the synthetic genesis creation methodology described in this chapter. Based on the literature review, stratified-Monte Carlo (SMC) sampling technique utilized as a component of storm genesis creation methodology provides significant improvement in data representation and the spatial sampling of genesis locations. These sampled locations are utilized as spatial input to estimate the genesis date from temporal surface created by Inverse Distance Weighting (IDW) methodology. The combination of stratified-MC and IDW methodologies provide improvement for the spatial-temporal accuracy of synthetic genesis locations over the mentioned methods because of the better space-filling property of this new sampling approach. Also, the proposed methodology is a more flexible and faster running model than the pseudo-Monte Carlo implementations. Output from the implemented genesis creation methodology stratified-MC component and output from the IDW component are then input to the proposed track propagation methodology. This approach has not been implemented in any other genesis location prediction methods. There is no statistically significant difference with 95% level of confidence between the 1945 to 2008 period and the 1970 to 2008 period for the distribution of genesis locations. Furthermore, the 1945 to 2008 period is suitable for representing a full cycle of low and high activity of the Atlantic Multi-Decadal Oscillation (AMO). This permits the development of density probability regions with larger genesis data by utilizing better representation of AMO and spatial distribution. The 1945 to 2008 period is more suitable than other investigated periods to combine spatial and temporal periods for the genesis locations simulations for representing historical distribution and improving model performance (accuracy). Also, the spatial extent of regions that are under-represented in genesis location records are discovered and mapped in the North Atlantic basin. This is the first map of its kind.

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By knowing these regions, better sampling methodologies can be developed or biases in the existing models can be addressed.

The second stage (track propagation) of tropical cyclone simulation methodology is presented in the Chapter 3. This chapter conducted a literature review and critique of existing storm track forecast, non-forecast, and intensity estimation models for storm tracks simulations. The existing track models did not qualify for implementation due to restrictions, limitations, or performance related criteria. Also, existing track propagation models did not provide a fast- running, geo-database assisted track prediction framework. The proposed framework is an improvement for statistical track modeling approaches. The over/underestimation of the original track model, HURRAN, has been reduced with the implemented intensity adjustment model as a part of the proposed track propagation methodology in the Gulf of Mexico. Another computational improvement has been achieved in spatial calculations (e.g. positional accuracy and geo-spatial statistics) by using the proper projection conversions along with the appropriate GIS libraries for computations. Also, inclusion of additional storm intensity parameters (RMW and Holland B) provided have been correlated which are with surge elevation so that the track and the storm surge estimation methodology are integrated. This integrated methodology is implemented in an GIS environment by combining independent genesis and track simulation modules for more accurate and faster running model development. Also, GIS improves the accuracy of spatial calculations and reduces spatial errors related to projection distortions and conversions. This improved track propagation model is used to expand storm tracks with statistically representative synthetic ones in the Gulf of Mexico.

Chapter 4 focused on accurate estimation of storm surge elevation along coastal regions of the southwestern Louisiana. This chapter provides a literature review and critique of existing

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Ocean Circulation Models (OCMs) for storm surge estimation. There are two main issues with the existing models: 1) models did not accurately estimate surge elevations for a storm event; and 2) the models are costly and time consuming to execute. Improved operational cost and the faster running storm surge elevation estimation methodology have been achieved by combining Joint Probability Method (JPM) and Artificial Neural Network (ANN) method. The JPM methodology for storm surge estimation is provided a means to identify key storm track and intensity parameters effecting the surge elevation for reducing computational requirements. In addition, a fast-running and fault tolerance to input noise modeling methodology has been achieved by utilization of ANN methodology. Finally, utilization of GIS computational environment and additional storm parameters (RMW and Holland B intensity parameters) increases the accuracy of the spatial calculations and storm surge estimation.

Chapter 5 presents a literature review and critique of existing storm and surge track databases. The existing databases do not provide an integrated data management framework for both storm track and surge elevation data. There is only one published study for creating a database for the historical storm surge elevation at point locations for the Gulf of Mexico. Additionally, the single existing integrated storm track and surge estimation model is for the island of Oahu, Hawaii, only. The developed geodatabase framework has improved speeds of data access, retrieval, related calculations, and visualization because of better database management system integration with GIS. The first of its kind modeling framework for computations and storage of historical and synthetic storm tracks with storm surge elevations in the North Atlantic basin has been created.

This study expands the family tree of Ocean Circulation Models (OCMs) by including latest publications (especially for the GFDL model). In addition, major milestones in tropical

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cyclone observing, data processing, and communication systems are updated in similar fashion (After McAdie, 2009).