Segmenting datasets

Once the raw data has gone through QA/QC, then you must:

• Split the time series into shorter segments by considering:
  • time and length scales of turbulence
  • stationarity of the segment
  • Taylor's frozen turbulence hypothesis, etc ...

The act of chopping a time series into smaller subsets, i.e., segments, is effectively a form of low-pass (box-car) filtering. Hence, when estimating <math>\varepsilon</math> how to segment the time series is usually a more important consideration than detrending time series. This segmenting step dictates the minimum burst duration when setting-up your equipment.

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  Zoom of the first 512 s of the measured velocities shown above including the same trends

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  Example velocity spectra of the short 512 s of records before and after different detrending techniques applied to the original 6h time series. The impact of the detrending method can be seen at the lowest frequencies only