This chapter discussed the TC development, including track and intensity forecasting. The accurate simulation of TC track and intensity requires high resolution (in space and time) atmospheric conditions. Given the limited amount of atmospheric data, one obvious method to improve the prediction accuracy of NWP models is to use remotely sensed data to enhance their initial conditions. Undoubtedly, the numerical accuracy of the algorithms and consequently eventual predictions of the NWP models are also closely related to the computational mesh on which the model calculations are performed. Thus, the goal is to implement simulation models with as fine a grid resolution as possible.
In recent years, new grid techniques have been developed to improve prognostic meteorological model predictions. Grid nesting, grid refinement, and unstructured grid techniques all promise to improve the quality of the computed solution in these models. When these new techniques coupled with the adaptive data (particularly remote sensing data) assimilation, the desired improvements in hurricane track and intensity forecast may be achieved.
Acknowledgements This research was supported by the National Science Foundation (NSF) under Grant ATM-0543330, and by NASA grant NX06AF30G.
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