Decomposing velocity measurements: Difference between revisions
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==Methods for detrending== | ==Methods for detrending== | ||
There is no exact definition for what consists of a "trend", nor any set algorithm for identifying the trend. The following techniques can be used for detrending <ref name=Wuetal_PNAS/>: | |||
# Linear trend removal | |||
# Low-pass linear filters (e.g., butterworth filter) | |||
# Empirical modal decomposition | |||
The first two methods presume the original time series is [[Stationarity|stationary]]. | |||
[[File:Long timeseries.png|thumb|none|600px|Measured velocities at 4 Hz from an [[Acoustic-Doppler Velocimeters]] have been detrended using three different techniques. Empirical modal decomposition (EMD) <ref name="Wuetal_PNAS">{{Cite journal | [[File:Long timeseries.png|thumb|none|600px|Measured velocities at 4 Hz from an [[Acoustic-Doppler Velocimeters]] have been detrended using three different techniques. Empirical modal decomposition (EMD) <ref name="Wuetal_PNAS">{{Cite journal | ||
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===Short burst sampling=== | ===Short burst sampling=== | ||
A short burst is typically at most 2-3x the expected largest turbulence length scales. As a rule of thumb, turbulence estimates from the inertial subrange of velocity rely on 5 to 15 min long-segments. | A short burst is typically at most 2-3x the expected largest turbulence length scales. As a rule of thumb, turbulence estimates from the inertial subrange of velocity rely on 5 to 15 min long-segments. ``` Act of segmenting is effectively a form of filtering''' | ||
* Linear trend removal | * Linear trend removal | ||
* Empirical mode decomposition (nonlinear and/or non-stationary signal) | * Empirical mode decomposition (nonlinear and/or non-stationary signal) | ||
==Notes== | |||
---- | ---- | ||
Return to [[Velocity_point-measurements|Velocity point-measurements' welcome page]] | Return to [[Velocity_point-measurements|Velocity point-measurements' welcome page]] | ||
Revision as of 18:50, 29 November 2021
The quality-controlled velocities are first detrended before being further analysed to determine mean flow past the sensor and surface wave statistics. These quantities are necessary for later choosing the appropriate inertial subrange model for spectral fitting.
Methods for detrending
There is no exact definition for what consists of a "trend", nor any set algorithm for identifying the trend. The following techniques can be used for detrending [1]:
- Linear trend removal
- Low-pass linear filters (e.g., butterworth filter)
- Empirical modal decomposition
The first two methods presume the original time series is stationary.
Long continuous sampling
Info needs simplifying after convening as a subgroup Different techniques dependent on whether measurements were collected continuously or in long bursts (define here). The high-frequency content can be obtained by:
- High-pass filtering (linear and stationary signals)
- Empirical mode decomposition (nonlinear and/or non-stationary signal)
Short burst sampling
A short burst is typically at most 2-3x the expected largest turbulence length scales. As a rule of thumb, turbulence estimates from the inertial subrange of velocity rely on 5 to 15 min long-segments. ``` Act of segmenting is effectively a form of filtering
- Linear trend removal
- Empirical mode decomposition (nonlinear and/or non-stationary signal)
Notes
Return to Velocity point-measurements' welcome page
- ↑ Jump up to: 1.0 1.1 Zhaohua Wu, Norden E. Huang, Steven R. Long and and Chung-Kang Peng. 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series. PNAS. doi:10.1073/pnas.0701020104
