STATISTICAL PREDICTION OF HURRICANE STORM SURGE

Grid Convergence Studies for the Prediction of Hurricane Storm Surge

The focus of this paper is a systematic determination of the relationship between grid resolution and errors associated with computations of hurricane storm surge. A grid structure is sought that provides the spatial resolution necessary to capture pertinent storm surge physics and does not overdiscretize. A set of numerical experiments simulating storm surge generation over 14 grid discretizat...

Risk assessment of hurricane storm surge for New York City

[1] Hurricane storm surge presents a major hazard for the United States. We apply a model‐based risk assessment methodology to investigate hurricane storm surge risk for New York City (NYC). We couple a statistical/deterministic hurricane model with the hydrodynamic model SLOSH (sea, lake, and overland surges from hurricanes) to generate a large number of synthetic surge events; the SLOSH model...

Prediction of Hurricane Motion by Statistical Methods

Equations for the prediction of hurricane tracks have been developed by use of statistical methods. Data at sea level, 700 mb., and 500 mb. were selected as predictors. Forecasts are prepared in 12-hr. steps for periods up to 48 hr. The forecast equations have been tested on an operational basis during the 1964 and 1965 hurricane seasons. The accuracy of these forecasts compares favorably with ...

Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks

Scalability of an Unstructured Grid Continuous Galerkin Based Hurricane Storm Surge Model

This paper evaluates the parallel performance and scalability of an unstructured grid Shallow Water Equation (SWE) hurricane storm surge model. We use the ADCIRC model, which is based on the generalized wave continuity equation continuous Galerkin method, within a parallel computational framework based on domain decomposition and the MPI (Message Passing Interface) library. We measure the perfo...