Modlin Danny, Fuentes Montse, Reich Brian
Statistics PhD student at North Carolina State University (NCSU).
Professor of Statistics at NCSU. Tel: (919) 515-1921.
Environmetrics. 2012 Feb 1;23(1). doi: 10.1002/env.1133.
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.
随着飓风逼近登陆，沿海居民必须防范多种危险。破坏性的大风、暴雨和龙卷风会对沿海和内陆地区造成严重破坏；但是，对生命和财产构成最大威胁的是风暴潮。风场被用作数值预报沿海海洋对飓风风力响应的主要驱动力，比如风暴潮的高度和沿海洪水的程度。不幸的是，这些驱动力的确定性建模进展受到计算成本的阻碍。在本文中，我们提出了一种用于矢量场的多元空间模型，并将其应用于飓风风场。我们在极坐标中对每个地点的风矢量进行参数化，并为矢量方向指定一个圆形条件自回归（CCAR）模型，为风速指定一个空间自回归（CAR）模型。我们将矢量场框架应用于1999年飓风弗洛伊德的飓风表面风场，并将我们的CCAR模型与之前将风速和风向分解为南北和东西基本分量的方法进行比较。
