摘要翻译：
风暴潮是由飓风引起的强风和低压引起的海水在岸上的涌动，它可以加剧降雨引起的内陆洪水的影响，导致沿海地区居民的财产损失和生命损失。数值海洋模式对于建立沿海地区的风暴潮预报至关重要。这些模型主要由表面风强迫驱动。目前，海洋模式所使用的网格风场是由基于中心气压和风暴中心位置的确定性公式指定的。虽然这些方程包含了关于飓风表面风场结构的重要物理知识，但它们并不总是捕捉到飓风的不对称和动态性质。提出了一种新的贝叶斯多元空间统计建模框架，将风场数据与物理知识相结合，改进了风向量的估计。许多空间模型假设数据服从高斯分布。然而，对于经常显示不稳定行为的风场数据，如时间或空间的突然变化，这可能会限制太多。本文对这些数据建立了一个半参数多元空间模型。我们的模型建立在断棒先验的基础上，这是贝叶斯建模中经常使用的方法，以获取结果的参数形式的不确定性。通过给每个位置分配一个不同的未知分布，并用一系列核函数平滑空间分布，将断棒先验扩展到空间设置。与常用的贝叶斯克立格方法相比，该半参数空间模式对伊万飓风风场的预报有了较大的改进。
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英文标题：
《A multivariate semiparametric Bayesian spatial modeling framework for
  hurricane surface wind fields》
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作者：
Brian J. Reich, Montserrat Fuentes
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, 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 creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While 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. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.
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PDF链接：
https://arxiv.org/pdf/709.0427