A method for multiscale optimal analysis with application to Argo data
A method for multiscale optimal analysis with application to Argo data
复制标题
一种应用于Argo数据的多尺度优化分析方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Riser
中科院分区：
文献类型：
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作者：
A. Gray;S. Riser
本研究提出了一种最佳分析方法，用于根据观测估计场的大尺度和小尺度分量。该技术依赖于迭代广义最小二乘法来直接从数据确定小规模波动的统计数据，因此在先验未知此类信息时特别有价值。建议使用球面径向基函数来拟合大规模信号，特别是当域足够大时。两个测试用例说明了该过程的几个属性，展示了其实用性，并为其使用提供了实用指南。然后将该方法应用于 Argo 剖面浮标阵列收集的观测结果，以生成全球网格化绝对地转速度估计值。
This study presents an optimal analysis method for estimating the large- and small-scale components of a field from observations. This technique relies on an iterative generalized least squares procedure to determine the statistics of the small-scale fluctuations directly from the data and is thus especially valuable when such information is not known a priori. The use of spherical radial basis functions in fitting the large-scale signal is suggested, particularly when the domain is sufficiently large. Two test cases illustrate several of the properties of this procedure, demonstrate its utility, and provide practical guidelines for its use. This method is then applied to observations collected by the Argo array of profiling floats to produce global gridded absolute geostrophic velocity estimates.