Authors: E Forootan J Kusche
Publish Date: 2011/11/22
Volume: 86, Issue: 7, Pages: 477-497
Abstract
The Gravity Recovery and Climate Experiment GRACE products provide valuable information about total water storage variations over the whole globe Since GRACE detects mass variations integrated over vertical columns it is desirable to separate its total water storage anomalies into their original sources Among the statistical approaches the principal component analysis PCA method and its extensions have been frequently proposed to decompose the GRACE products into space and time components However these methods only search for decorrelated components that on the one hand are not always interpretable and on the other hand often contain a superposition of independent source signals In contrast independent component analysis ICA represents a technique that separates components based on assumed statistical independence using higherorder statistical information If one assumes that independent physical processes generate statistically independent signal components added up in the GRACE observations separating them by ICA is a reliable strategy to identify these processes In this paper the performance of the conventional PCA its rotated extension and ICA are investigated when applied to the GRACEderived total water storage variations These analyses have been tested on both a synthetic example and on the real GRACE level2 monthly solutions derived from GeoForschungsZentrum Potsdam GFZ RL04 and Bonn University ITG2010 Within the synthetic example we can show how imposing statistical independence in the framework of ICA improves the extraction of the ‘original’ signals from a GRACEtype superposition We are therefore confident that also for the real case the ICA algorithm without making prior assumptions about the longterm behaviour or on the frequencies contained in the signal improves over the performance of PCA and its rotated extension in the separation of periodical and longterm components
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