Covariance models on the surface of a sphere: when does it matter?
Covariance models on the surface of a sphere: when does it matter?
复制标题
球体表面的协方差模型：什么时候重要？
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Jun
中科院分区：
文献类型：
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作者：
J. Jeong;M. Jun
由于地球仪上的数据的广泛可用性，人们对开发球体表面过程的协方差函数越来越感兴趣。利用欧氏距离和大圆距离之间的一对一映射，欧氏空间中的各向同性和正定函数可以用作球面上的协方差函数。然而，这种方法可能导致球体上的物理上不切实际的失真，特别是对于大距离。我们考虑几类参数协方差函数的表面上的一个球体，定义与大圆距离或欧氏距离，并调查其对空间预测的影响。我们拟合了几个各向同性的协方差模型，模拟数据以及真实的数据从美国国家环境预报中心（NCEP）/美国国家大气研究中心（NCAR）的再分析的球。我们证明，协方差函数最初定义的欧氏距离可能是不够的一些全球性的数据。版权所有© 2015约翰威利父子有限公司.
There is a growing interest in developing covariance functions for processes on the surface of a sphere because of the wide availability of data on the globe. Utilizing the one‐to‐one mapping between the Euclidean distance and the great circle distance, isotropic and positive definite functions in a Euclidean space can be used as covariance functions on the surface of a sphere. This approach, however, may result in physically unrealistic distortion on the sphere especially for large distances. We consider several classes of parametric covariance functions on the surface of a sphere, defined with either the great circle distance or the Euclidean distance, and investigate their impact upon spatial prediction. We fit several isotropic covariance models to simulated data as well as real data from National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis on the sphere. We demonstrate that covariance functions originally defined with the Euclidean distance may not be adequate for some global data. Copyright © 2015 John Wiley & Sons, Ltd.