Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data. Issue 5 (19th May 2014)

• Record Type: Journal Article
• Authors: Boergens, Eva; Rangelova, Elena; Sideris, Michael G.; Kusche, Juergen
• Is Part Of: Journal of geophysical research. Volume 119:Issue 5(2014:May)
• Page Start: 4429
• Page End: 4447
• Publication Date: 2014-05-19
• Subjects: Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1002/2013JB010452
• Languages: English
• ISSNs: 2169-9313

Abstract

<abstract abstract-type="main" id="jgrb50660-abs-0001"> <title>Abstract</title> <p id="jgrb50660-para-0001">We investigate the potential of two independent component analysis (ICA) methods, i.e., the temporal and spatiotemporal ICA, for separating geophysical signals in Gravity Recovery and Climate Experiment (GRACE) data. These methods are based on the assumption of the statistical independence of the signals and thus separate the GRACE‐observed mass changes into maximal independent signals. These two ICA methods are compared to the conventional principal component analysis (PCA) method. We test the three methods with respect to their ability to separate a periodic hydrological signal from a trend signal originating in the solid Earth or the cryosphere with simulated and Center for Space Research GRACE mass changes for the time period of January 2003 to December 2010. In addition, we investigate whether the methods are capable of separating hydrological annual and semiannual mass variations. It is shown that both ICA methods are superior to PCA when non‐Gaussian mass variations are analyzed. Furthermore, the spatiotemporal ICA resolves successfully the lack of full temporal and spatial independence of the geophysical signals observed by GRACE both in global and regional simulation scenarios. Although the temporal and spatiotemporal ICA are nearly equivalent, both superior to PCA in the global GRACE analysis, the spatiotemporal ICA proves to be more efficient in regional applications by recover more reliably the postglacial rebound trend in North America and the bimodal total water storage variability in Africa.</p> </abstract>