Parallel Implementation of the Ensemble Empirical Mode Decomposition (PEEMD) and Its Application for Earth Science Data Analysis - Shen, Bo-Wen; Cheung, Samson; Wu, Yu-ling; Li, Jui-Lin; Kao, David (Computing in science & engineering, 2017)
To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional data sets, we have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble Empirical Mode Decomposition (EMD), achieving a parallel speedup of 720x using 200 eight-core processors. In this study, we discuss the implementation of the PEEMD and its application for the analysis of Earth science data, including the solution of Lorenz model, an idealized terrain-induced flow and real case Hurricane Sandy (2012), the latter of which is the second costliest hurricane in the US history.