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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 1 | Copyright
Nonlin. Processes Geophys., 21, 203-215, 2014
https://doi.org/10.5194/npg-21-203-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Feb 2014

Research article | 06 Feb 2014

An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets

J. v. Buttlar1, J. Zscheischler1,2,3, and M. D. Mahecha1 J. v. Buttlar et al.
  • 1Max Planck Institute for Biogeochemistry, P.O. Box 100164, 07701 Jena, Germany
  • 2Max Planck Institute for Intelligent Systems, P.O. Box 2169, 72012 Tübingen, Germany
  • 3ETH Zürich, Rämistraße 101, 8092 Zürich, Switzerland

Abstract. 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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