ROTATED EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS FOR SPATIO-TEMPORAL DATA ANALYSIS

S. Debnath
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Abstract

Given any space-time field, Empirical orthogonal function (EOF) analysis finds a set of orthogonal spatial patterns along with a set of associated uncorrelated time series or principal components (PCs). Spatial orthogonality and temporal uncorrelation of EOFs and PCs respectively impose limits on the physical interpretability of EOF patterns. This is because physical processes are not independent, and therefore physical modes are expected in general to be non-orthogonal. Rotated empirical orthogonal functions (REOF) were introduced to generate general localized structures by compromising some of the EOF properties such as orthogonality. EOF and REOF analysis are carried out for the significant wave height (SWH) data for the Bay of Bengal (BOB) region for the period 1958 to 2001. Separate experiments were conducted for all the months together and also for July and December representing the southwest and northeast monsoon periods. The first eigenmodes account for 84%, 68%, and 59% of the total variability for the above three cases respectively. The REOF proved to be more effective than EOF for the above region.
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旋转经验正交函数分析用于时空数据分析
给定任何时空场,经验正交函数(EOF)分析可以找到一组正交空间模式以及一组相关的不相关时间序列或主成分(PCs)。EOF模式的空间正交性和时间不相关性分别限制了EOF模式的物理可解释性。这是因为物理过程不是独立的,因此物理模式通常是非正交的。引入旋转经验正交函数(REOF),通过牺牲其正交性等特性来生成一般局域结构。对1958 ~ 2001年孟加拉湾(BOB)地区的有效波高(SWH)资料进行了EOF和REOF分析。对所有月份以及代表西南和东北季风期的7月和12月进行了单独的实验。在上述三种情况下,第一特征模态分别占总变率的84%、68%和59%。在上述区域,REOF比EOF更有效。
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