A statistical framework to combine multivariate spatial data and physical models for Hurricane surface wind prediction

Abstract

Storm surge is the onshore rush of seawater associated with hurricane force winds. Storm surge can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for predicting which coastal areas are most likely to be impacted by storm surge. These numerical physics-based models are driven primarily by the surface wind forcings which are currently specified by a deterministic formula. Although these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. This article develops a new multivariate spatial statistical framework to improve the estimation of these wind field inputs while accounting for potential bias in the observations. We find that this spatial model consistently improves parameter estimation and prediction for surface wind data for a case study of Hurricane Charley of 2004 when compared to the original physical model. These methods are also shown to improve storm surge estimates when used as the forcing fields for a numerical three-dimensional coastal ocean model.