Wu Zhaohua, Feng Jiaxin, Qiao Fangli, Tan Zhe-Min
Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA
Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA.
Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150197. doi: 10.1098/rsta.2015.0197.
In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
在这个大数据时代，解决两个主要问题比以往任何时候都更加紧迫：（i）能够促进从非本地源访问数据的快速数据传输方法，以及（ii）能够从可用数据中揭示特定目的关键信息的快速高效数据分析方法。尽管不同领域解决这两个问题的方法可能有很大差异，但共同部分必须涉及数据压缩技术和快速算法。本文介绍了最近开发的自适应时空局部分析方法，即快速多维总体经验模态分解（MEEMD），用于分析大型时空数据集。原始的MEEMD使用总体经验模态分解在每个空间网格上分解时间序列，然后将气候变率和变化在自然分离时间尺度上的时空演变拼凑起来，这在计算上是昂贵的。通过利用主成分分析/经验正交函数分析对时空相关数据进行高效表达，我们设计了一种气候数据有损压缩方法，以促进其非本地传输。我们还通过分解主成分而不是原始的网格时间序列来解释快速MEEMD背后的基本原理，以加快MEEMD的计算速度。以一个典型的气候数据集为例，我们证明我们新设计的方法可以（i）以一到两个数量级的压缩率压缩数据；以及（ii）将MEEMD算法加速一到两个数量级。
