Multivariate Spatial Temporal Statistical Models for Applications in Coastal Ocean Prediction

Abstract

Estimating the spatial and temporal variation of surface wind fields plays an important role in modeling atmospheric and oceanic processes. This is particularly true for hurricane forecasting, where numerical ocean models are used to predict the height of the storm surge and the degree of coastal flooding. We use multivariate spatial-temporal statistical methods to improve coastal storm surge prediction using disparate sources of observation data. An Ensemble Kalman Filter is used to assimilate water elevation into a three dimension primitive equations ocean model. We find that data assimilation is able to improve the estimates for water elevation for a case study of Hurricane Charley of 2004. In addition we investigate the impact of inaccuracies in the wind field inputs which are the main forcing of the numerical model in storm surge applications. A new multivariate spatial statistical framework is developed to improve the estimation of these wind inputs. A spatial linear model of coregionalization (LMC) is used to account for the cross-dependency between the two orthogonal wind components. A Bayesian approach is used for estimation of the parameters of the multivariate spatial model and a physically based wind model while accounting for potential additive and multiplicative bias in the observed wind data. This spatial model consistently improves parameter estimation and prediction for surface wind data for the Hurricane Charley case study when compared to the original physical wind model. These methods are also shown to improve storm surge estimates when used as the forcing fields for the coastal ocean model. Finally we describe a new framework for estimating multivariate nonstationary spatial-temporal processes based on an extension of the LMC model. We compare this approach to other multivariate spatial models and describe an application to surface wind fields from Hurricane Floyd of 1999.