Smoothing data series by means of cubic splines: quality of approximation and introduction of an iterative spline approach

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Wüst, Sabine ; Wendt, Verena ; Linz, Ricarda ; Bittner, Michael (2017)
  • Journal: (issn: 1867-8548)
  • Related identifiers: doi: 10.5194/amt-2016-399
  • Subject:
    arxiv: Mathematics::Numerical Analysis | Computer Science::Graphics

Cubic splines with equidistant spline sampling points are a common method in atmospheric science for the approximation of undisturbed background conditions by means of filtering superimposed fluctuations from a data series. Often, not only the background conditions are of scientific interest but also the residuals &ndash; the subtraction of the spline from the original time series. <br><br> Based on test data sets, we show that the quality of approximation is not increasing continuously with increasing number of spline sampling points/decreasing distance between two spline sampling points. Splines can generate considerable artificial oscillations in the data. <br><br> We introduce an iterative spline approach which is able to significantly reduce this phenomenon. We apply it not only to the test data but also to TIMED-SABER temperature data and choose the distance between two spline sampling points in a way that we are sensitive for a large spectrum of gravity waves.
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