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| Titel |
An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets |
| VerfasserIn |
J. v. Buttlar, J. Zscheischler, M. D. Mahecha |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 1 ; Nr. 21, no. 1 (2014-02-06), S.203-215 |
| Datensatznummer |
250120886
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| Publikation (Nr.) |
 copernicus.org/npg-21-203-2014.pdf |
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| Zusammenfassung |
| Spatiotemporal observations in Earth System sciences are often affected by
numerous and/or systematically distributed gaps. This data fragmentation is
inherited from instrument failures, sparse measurement protocols, or
unfavourable conditions (e.g. clouds or vegetation thickness in case of remote-sensing data). Missing values are problematic as they may cause analytic biases and
often inhibit advanced statistical analyses. Hence, gapfilling is an undesired
but necessary task in Earth System sciences.
State-of-the-art gapfilling algorithms based on Singular Spectrum Analysis
(SSA) exploit the information contained in periodic temporal patterns to fill
gaps in the observations. Here we propose an extension of this method in
order to additionally consider the spatial processes and patterns underlying
most geoscientific datasets. The latter has been made possible by including a
recently developed 2-D-SSA approach.
Using both artificial and real-world test data, we show that simultaneously
exploiting spatial and temporal patterns improves the gapfilling
substantially. We outperform conventional approaches particularly for large
and systematically recurring gaps. The new method is reasonably fast and can
be applied with a minimum of a priori assumptions regarding the structure of the data
and the distribution of gaps. The algorithm is available as a ready-to-use open source
software package. |
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