EOF-EEMD Statistical Analysis and Visualization Packagehttp://doi.org/10.18170/DVN/FZ1FDMMengyao LiuJintai LinYuchen WangPeking University Open Research Data Platform2020-01-312020-01-31T14:39:47Z<p><a href="http://www.phy.cqjj8.com/~acm/acmProduct.php#EOF-EEMD">Homepage of Lingroup</a></p> Many variables of interest, such as air pollutants and meteorological parameters, often exhibit complex spatial and temporal variabilities. In particular, these variables contain many temporal scales that are non-periodic and non-stationary, challenging proper quantitative characterization and visualization. The EOF-EEMD analysis-visualization package we complied aims to evaluate the spatiotemporal variability across scales, which can be periodic/stationary or not. As shown in the figure below, the package consists, in order, of an EOF analysis (Lorenz, 1956), an EEMD analysis (Wu et al., 2009), a Hilbert-Huang Transform (HT) with Marginal Spectrum Analysis (MSA), and a visualization step to quantitatively depict the spatial-temporal scales of measurement or model data. <p><a href="http://www.phy.cqjj8.com/~acm/acmProduct.php#EOF-EEMD">See here</a> (including user guide, a full IDL version, and a partial MATLAB version) </p> <p>Note that parts of the codes are adopted from <a href="http://www.coaps.fsu.edu/zhaohua-wu">Zhaohua Wu</a> in MATLAB, and parts are modified from Jinxuan Chen and May Fu's codes. See user guide for detailed credit descriptions. </p>Earth and Environmental Sciences2020-01-31CC0CC0 Waiver