Decomposing velocity measurements: Difference between revisions
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This decomposition enables determining [[Velocity past the sensor|mean flow past the sensor]] and [[Surface wave statistics|surface wave statistics]], which are necessary for later choosing the appropriate [[Velocity inertial subrange model| inertial subrange model]] for [[Spectral fitting|spectral fitting]]. | |||
Measured velocities are [[Detrending time series|detrended]], and then further analysed to determine the velocity of the flow passing the sensor and the surface wave statistics. | |||
==Overview== | ==Overview== | ||
This step involves separating the [[quality-controlled Data processing of raw measurements| raw velocities]] into: | This step involves separating the [[quality-controlled Data processing of raw measurements| raw velocities]] into: | ||
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* high-frequency component comprising of the turbulence and surface wave signals. This component may also include unwanted contributions from the measurement platforms, such as wakes and vibrations associated with the frame. | * high-frequency component comprising of the turbulence and surface wave signals. This component may also include unwanted contributions from the measurement platforms, such as wakes and vibrations associated with the frame. | ||
==Methods for decomposing== | ==Methods for decomposing== | ||
Revision as of 17:50, 29 November 2021
This decomposition enables determining mean flow past the sensor and surface wave statistics, which are necessary for later choosing the appropriate inertial subrange model for spectral fitting.
Measured velocities are detrended, and then further analysed to determine the velocity of the flow passing the sensor and the surface wave statistics.
Overview
This step involves separating the raw velocities into:
- low-frequency component comprising of the background large-scale flow
- high-frequency component comprising of the turbulence and surface wave signals. This component may also include unwanted contributions from the measurement platforms, such as wakes and vibrations associated with the frame.
Methods for decomposing
Long continuous sampling
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.
- Remove the arithmetic mean of the burst to obtain the high-frequency content
- Linear trend removal
- Empirical mode decomposition (nonlinear and/or non-stationary signal)
