Segmenting datasets

Once the raw observations have been quality-controlled, 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 ...

and}}. 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series. PNAS. doi:10.1073/pnas.0701020104 </ref>, linear trend, and a 2nd order low-pass Butterworth filter. A cut-off period of 10 min was targeted by both the filter and EMD

Application to measured velocities

Measurements are typically collected in the following two ways:

• continuously, or in such long bursts that they can be considered continuous
• short bursts that are typically at most 2-3x the expected largest turbulence time scales (e.g., 10 min in ocean environments)

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.

• 

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

• 

  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

Notes

<references/>