A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation
A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation
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DOI：
10.5194/os-10-845-2014
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发表时间：
2014-10
期刊：
影响因子：
3.2
通讯作者：
J. Beckers;A. Barth;I. Tomaz̆ić;A. Álvera-Azcárate
中科院分区：
文献类型：
--
作者：
J. Beckers;A. Barth;I. Tomaz̆ić;A. Álvera-Azcárate
抽象的。本文提出了一种方法，在该方法中，多尺度过程的最优插值可以扩展为一系列简单的插值。首先，我们证明了如何叠加的两个过程的最佳分析，可以通过不同的数学公式，涉及迭代和分析集中在一个单一的过程。从不同的数学等价公式，然后我们选择最有效的分析行为的不同的可能性在一个简单的和良好的控制测试用例。从这个实验推导出的明确的指导方针，然后应用到一个真实的情况下，我们结合联合收割机大规模分析每小时旋转增强可见光和红外成像仪（SEVIRI）卫星图像使用数据插值经验正交函数（DINEOF）与局部最优插值使用高斯协方差。结果表明，最佳组合确实提供了最好的重建，因此可以利用从原始数据中提取最大量的有用信息。
Abstract. We present a method in which the optimal interpolation of multi-scale processes can be expanded into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the different mathematical equivalent formulations, we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well-controlled test case. The clear guidelines deduced from this experiment are then applied to a real situation in which we combine large-scale analysis of hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite images using data interpolating empirical orthogonal functions (DINEOF) with a local optimal interpolation using a Gaussian covariance. It is shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data.