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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 15
Atmos. Chem. Phys., 11, 7905–7923, 2011
https://doi.org/10.5194/acp-11-7905-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 11, 7905–7923, 2011
https://doi.org/10.5194/acp-11-7905-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Aug 2011

Research article | 04 Aug 2011

Technical Note: Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series

J. P. Musial1,*, M. M. Verstraete1, and N. Gobron1 J. P. Musial et al.
  • 1European Commission, Joint Research Centre, Institute for Environment and Sustainability, 21027 Ispra (VA), Italy
  • *current address: Geographisches Institut der Universität Bern (GIUB), 3012 Bern, Switzerland

Abstract. Geophysical time series often feature missing data or data acquired at irregular times. Procedures are needed to either resample these series at systematic time intervals or to generate reasonable estimates at specified times in order to meet specific user requirements or to facilitate subsequent analyses. Interpolation methods have long been used to address this problem, taking into account the fact that available measurements also include errors of measurement or uncertainties. This paper inspects some of the currently used approaches to fill gaps and smooth time series (smoothing splines, Singular Spectrum Analysis and Lomb-Scargle) by comparing their performance in either reconstructing the original record or in minimizing the Mean Absolute Error (MAE), Mean Bias Error (MBE), chi-squared test statistics and autocorrelation of residuals between the underlying model and the available data, using both artificially-generated series or well-known publicly available records. Some methods make no assumption on the type of variability in the data while others hypothesize the presence of at least some dominant frequencies. It will be seen that each method exhibits advantages and drawbacks, and that the choice of an approach largely depends on the properties of the underlying time series and the objective of the research.

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