Atomix - User contributions [en]

Dissipation rate estimates

2024-06-07T19:52:12Z

Marcus:

== dissipation rate estimates ==

The following items break down the derivation of the turbulent dissipation rate of kinetic energy (<math>\varepsilon</math>).
Explanations for each step can be found after.

# Extract the section defined in [[Flow_chart_for_shear_probes|step 2]] ("Section" selection).
# High-pass filter the shear-probe and (optionally) the vibration data.
# Identify each diss-length segment in the profile.
# [[De-spike the shear-probe data]], and track the fraction of data affected by de-spiking within each diss-length segment. This will become a quality-control metric.
# Calculate the [[frequency spectra and cross-spectra of shear and vibrations]] for each diss-length segment.
# Extract the original and the vibration-coherent clean shear-probe frequency spectra with [[the Goodman algorithm]].
# Correct shear and vibration frequency spectra for [[the high-pass filter]].
# Correct the cleaned frequency spectra for [[the bias induced by the Goodman algorithm]].
# Convert the frequency spectra into wavenumber spectra using the mean speed for each diss-length segment. That is, make the wavenumber <math> \begin{equation}k=f/U\end{equation}</math> and the wavenumber [[Here|kinetic energy spectrum]] <math> \begin{equation}E(k)=UE(f)\end{equation}</math> .
# Correct the spectra of shear for the [[wavenumber response of the shear probe]].
# Apply an [[iterative spectral integration algorithm]] to estimate the variance of shear.
# If the dissipation estimate is larger than shear inertial subrange fit use the method fit to the inertial subrange
# Calculate the turbulent dissipation rate by multiplying the shear variance by <math> \begin{equation} \frac{15}{2}\nu\end{equation}</math> where <math> \nu </math> is the temperature-dependent kinematic viscosity.
# Determine the [[figure of merit (FM)]] for each shear-probe spectrum using the method described here.
# Calculate the expected variance of each dissipation estimate using the method described here.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

De-spiking parameters

2024-06-07T19:26:14Z

Marcus:

The Goodman algorithm

2024-06-07T15:46:19Z

Marcus:

The procedure is well described in [https://journals.ametsoc.org/view/journals/atot/23/7/jtech1889_1.xml Goodman et al. 2006]
<ref name="goodmanetal2006">{{Cite journal
|authors= L. Goodman, E. Levine and R. Lueck
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= On measuring the terms of the turbulent kinetic energy budget from an AUV
|year= 2006
|volume= 23
|pages= 977-990
|doi= 10.1175/JTECH1889.1
}}</ref>.
Focusing on one specific direction, one specific shear probe, one can simply:

- compute the coherence squared <math>\Gamma^2(f)</math> between the observed velocity or shear frequency spectrum <math>E_{\mathrm{obs}}(f)</math> and the vibration frequency spectrum <math>E_{\mathrm{vib}}(f)</math>.

- and remove the vibration-coherent content of the shear spectrum using <math>E_{\mathrm{clean}}(f)=E_{\mathrm{obs}}(f)(1-\Gamma^2(f))</math>

where <math>E_{\mathrm{clean}}(f)</math> is the corrected shear frequency spectrum. Equation 3 in [https://journals.ametsoc.org/view/journals/atot/23/7/jtech1889_1.xml Goodman et al. 2006] presents the formalism for a correction using multiple directions (multivariate approach). The multivariate approach is more efficient and, almost a requirement for powered vehicles like AUVs. The number of vibration (or acceleration) signals used to correct the observed spectra of shear should be included in the quality control flag.

To obtain statistical significance, it is recommended to compute the coherence/cross-spectra over 7 fft-segments. The vibration-coherent noise removal algorithm [[The_bias_induced_by_the_Goodman_algorithm| biases low]] the spectrum of shear in a frequency independent manner, and can be corrected using the number of vibration (or other types) of signals used to correct the measured shear spectra and the number of fit-segments used to estimate the shear spectrum.
<ref name="luecketal2022">{{Cite journal
|authors= R. G. Lueck, D. MacIntyre, and J. MacMillan
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= The bias in coherent noise removal
|year= 2022
|doi=TBD
}}</ref>.

==References==
<references />

----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

The Goodman algorithm

2024-06-07T15:45:51Z

Marcus:

The procedure is well described in [https://journals.ametsoc.org/view/journals/atot/23/7/jtech1889_1.xml Goodman et al. 2006]
<ref name="goodmanetal2006">{{Cite journal
|authors= L. Goodman, E. Levine and R. Lueck
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= On measuring the terms of the turbulent kinetic energy budget from an AUV
|year= 2006
|volume= 23
|pages= 977-990
|doi= 10.1175/JTECH1889.1
}}</ref>.
Focusing on one specific direction, one specific shear probe, one can simply:

- compute the coherence squared <math>\Gamma^2(f)</math> between the observed velocity or shear frequency spectrum <math>E_{\mathrm{obs}}(f)</math> and the vibration frequency spectrum <math>E_{\mathrm{vib}}(f)</math>.

- and remove the vibration-coherent content of the shear spectrum using <math>E_{\mathrm{clean}}(f)=E_{\mathrm{obs}}(f)(1-\Gamma^2(f))</math>

where <math>E_{\mathrm{clean}}(f)</math> is the corrected shear frequency spectrum. Equation 3 in [https://journals.ametsoc.org/view/journals/atot/23/7/jtech1889_1.xml Goodman et al. 2006] Goodman et al. 2006 presents the formalism for a correction using multiple directions (multivariate approach). The multivariate approach is more efficient and, almost a requirement for powered vehicles like AUVs. The number of vibration (or acceleration) signals used to correct the observed spectra of shear should be included in the quality control flag.

To obtain statistical significance, it is recommended to compute the coherence/cross-spectra over 7 fft-segments. The vibration-coherent noise removal algorithm [[The_bias_induced_by_the_Goodman_algorithm| biases low]] the spectrum of shear in a frequency independent manner, and can be corrected using the number of vibration (or other types) of signals used to correct the measured shear spectra and the number of fit-segments used to estimate the shear spectrum.
<ref name="luecketal2022">{{Cite journal
|authors= R. G. Lueck, D. MacIntyre, and J. MacMillan
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= The bias in coherent noise removal
|year= 2022
|doi=TBD
}}</ref>.

==References==
<references />

----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

The Goodman algorithm

2024-06-07T15:44:28Z

Marcus:

The procedure is well described in [https://journals.ametsoc.org/view/journals/atot/23/7/jtech1889_1.xml Goodman et al. 2006]
<ref name="goodmanetal2006">{{Cite journal
|authors= L. Goodman, E. Levine and R. Lueck
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= On measuring the terms of the turbulent kinetic energy budget from an AUV
|year= 2006
|volume= 23
|pages= 977-990
|doi= 10.1175/JTECH1889.1
}}</ref>
Focusing on one specific direction, one specific shear probe, one can simply:

- compute the coherence squared <math>\Gamma^2(f)</math> between the observed velocity or shear frequency spectrum <math>E_{\mathrm{obs}}(f)</math> and the vibration frequency spectrum <math>E_{\mathrm{vib}}(f)</math>.

- and remove the vibration-coherent content of the shear spectrum using <math>E_{\mathrm{clean}}(f)=E_{\mathrm{obs}}(f)(1-\Gamma^2(f))</math>

where <math>E_{\mathrm{clean}}(f)</math> is the corrected shear frequency spectrum. Equation 3 in Goodman2006 presents the formalism for a correction using multiple directions (multivariate approach). The multivariate approach is more efficient and, almost a requirement for powered vehicles like AUVs. The number of vibration (or acceleration) signals used to correct the observed spectra of shear should be included in the quality control flag.

To obtain statistical significance, it is recommended to compute the coherence/cross-spectra over 7 fft-segments. The vibration-coherent noise removal algorithm [[The_bias_induced_by_the_Goodman_algorithm| biases low]] the spectrum of shear in a frequency independent manner, and can be corrected using the number of vibration (or other types) of signals used to correct the measured shear spectra and the number of fit-segments used to estimate the shear spectrum.
<ref name="luecketal2022">{{Cite journal
|authors= R. G. Lueck, D. MacIntyre, and J. MacMillan
|journal_or_publisher= J. Atmos. Oceanic Technol.
|paper_or_booktitle= The bias in coherent noise removal
|year= 2022
|doi=TBD
}}</ref>.

==References==
<references />

----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

De-spiking parameters

2024-06-06T20:09:45Z

Marcus:

The shear probe will collide with plankton and other particulates in the water, and it will occasionally hit jellyfish and even some fish that do not get out of its path. These collisions cause a very large and transient signal from the shear probe. These anomalies typically last for ~50ms, but can be longer in the case of jellyfish. They do not represent shear and bias high the variance of shear and the rate of dissipation. These anomalies should be replaced with data of constant value and the data so modified must be tracked because it is a quality control metric.
If more than a few percent of the data used for a particular estimate of <math>\varepsilon</math> have been modified by the de-spiking algorithm than such estimates are suspect.

A widely used algorithm, [[De-spike the shear-probe data]], identifies shear-probe signal anomalies by comparing the absolute shear against a smoothed version of the absolute shear.
It requires a threshold and smoothing parameter and the number of points to be replaced around a spike.
You may use other de-spiking algorithms but it important to keep track of the fraction of the data that has been modified.

<! –– * [thresh] Threshold value for the ratio of the instantaneous rectified signal to its smoothed version. A value of 8 is a good starting point for a VMP. ––>
<! –– * [smooth] The cut-off frequency of the first-order Butterworth filter that is used to smooth the rectified input signal. The time scale of smoothing is approximately 1/(2*smooth). A value of 0.5 is a good starting point for a VMP. ––>
<! –– * [Fs] Sampling rate (Hz). ––>
<! –– * [N] Spike removal scale. A total of 1.5*N data points are removed. N/2 points are removed before a spike, and N points are removed after a spike. The replaced data is an average of the adjacent neighbouthood. Averaging uses ~Fs/(4*smooth) points from each side of a spike. ––>

----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T19:10:09Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). '''Thus, the recommended choice for high-pass filtering of the shear data is''' <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

__TOC__
== [[Additional information]] ==
<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additionally, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.
</div>

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T19:08:38Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

__TOC__
== [[Additional information]] ==
<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additionally, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.
</div>

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T19:05:59Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

__TOC__
== [[Additional information]] ==
<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additionally, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T19:02:35Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

__TOC__
== [[Additional information]] ==
<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additional, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

<\div>

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T18:57:57Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additional, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

<\div>

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Processing parameters

2024-06-06T18:57:16Z

Marcus:

:# Choose the length of data (in meters) used for each dissipation estimate – [[diss-length]].
:# Choose the lowest wavenumber of spectral estimation – [[fft-length]].
:# Translate [[diss-length]] and [[fft-length]] into [[duration]] (time).
:# Round these up to [[nearest power-of-two number]] of samples.
:# Choose a [[high-pass filter cut-off frequency]] to be consistent with duration of the [[fft-length]].
:# Choose [[de-spiking parameters]].
:# Choose [[vibration-coherent noise removal]].

Please note that most choices made must be included in a data set, as described in the [[Netcdf meta data (shear probes)|list of meta data]].

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T18:54:46Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2 </math>.

<div class="mw-collapsible mw-collapsed" id="Additional information" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additional, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T18:49:42Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is <math>f_l = \tau_{fft}^{-1} </math>, where <math>\tau_{fft}</math> is the length of the FFT segments (in s) (add link to fft segment). Thus, the recommended choice for high-pass filtering of the shear data is <math> f_{HP} = \tau_{fft}^{−1}/2:

== Additional Information ==
<div class="mw-collapsible mw-collapsed" id="physical units" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additional, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

High-pass filter cut-off frequency

2024-06-06T18:44:16Z

Marcus:

Although the shear probe inherently senses only zero-mean fluctuations, its electronics may impart a non-zero mean that should be removed by digital high-pass filtering. Once the data have been cleaned by removing shear anomalies, it can be filtered. The cutoff frequency for digital high-pass filtering must be decided at this stage. The recommended high-pass filter is a first-order
Butterworth filter, applied forwards and backwards, with a cutoff frequency of approximately one-half of the lowest frequency
resolved by the spectra for dissipation estimates. The lowest frequency resolved is fl = t−1
f f t , where tfft is the length of the FFT segments (in s) (see Sec. 3.3.1). Thus, the recommended choice for high-pass filtering of the shear data is fHP = t−1f f t=2:

== Additional Information ==
<div class="mw-collapsible mw-collapsed" id="physical units" data-collapsetext="Collapse" data-expandtext="Expand">

The shear probe does not respond to a constant cross-axis velocity. It typically responds to fluctuations of cross-axis velocity with a frequency of 0.1 Hz and higher. Additional, high-pass filtering should be applied to minimize the spectral content of the data at frequencies lower than the frequency resolution of the spectrum, which equals the inverse of the duration of the fft-segments.
Thus, if <math>\tau_f</math> is the duration of an fft-segment, then a good choice is a first-order Butterworth high-pass filter with a cutoff frequency of <math>0.5\, \tau_f^{-1}</math> to <math>1\, \tau_f^{-1}</math>.
The spectra should be corrected for this high-pass filter.

For cases where there is vehicular motions with frequencies at or slightly above lowest wavenumber of spectral resolution, the cut-off frequency of the high pass filter could be increased to suppress the shear-probe signals induced by these motions.
However, the final spectrum of shear must be adjusted upwards to account for the high-pass filtering.
Hopefully, such undesirable motions will be detected by the accelerometers or vibration sensors on your instrument and will be removed by the Goodman [[vibration-coherent noise removal]] algorithm.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Shear probes quality control metrics

2024-06-06T18:21:23Z

Marcus:

Shear-probe data can be corrupted or compromised in several different ways. These include but are not limited to (''i'') collision with plankton and other materials, (''ii'') unremovable vibrational contamination. (''iii'') electronic noise, and (''iv'') interference from other instrumentation on a platform that carries the shear probes. This section describes the quality control metrics and the coding used to [[Quality control coding|identify]] them. Quality-control metrics that are currently identified include;

:* [[figure of merit (FOM)]]
:* [[fraction of shear-probe data altered by the de-spiking routine]]
:* number of [[iterations]] of the de-spiking routine required to clean the data
:* [[agreement between dissipation estimates]] from redundant sensors (i.e. two or more shear probes)

The numerical threshold for these metrics should depend, as much as possible, on the known statistical properties of a turbulence shear measurement. The numerical values of the QC codes (or flags) is found in [[Quality control coding|QC-flags]].

Please note that most choices made must be included in a data set, as described in the [[Netcdf meta data (shear probes)|list of meta data]].

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return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Nearest power-of-two number

2024-06-06T18:09:50Z

Marcus:

The rate of sampling can be used to convert these two durations (diss-length and fft-length) into a number of samples. Although many fast Fourier transform algorithms can transform data of any number of samples, '''the fastest execution occurs for lengths that are whole-number powers of two'''. It is recommended to round the number of samples up to the next power of two.

----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Minimum duration

2024-06-06T16:19:58Z

Marcus:

Finally, the above criteria must be valid for a certain minimum duration before one can consider the data to be a profile. For example, many of the above criteria may be satisfied by a vertical profiler dangling from its tether near the surface before it is launched. But they will only be satisfied for a few seconds. The appropriate choice of minimum duration depends on the typical duration of a real profile. In shallow water this may be ~10 seconds while for deeper deployments ~100 seconds may be more appropriate.

'''It is important to plot the pressure and some other parameters for a few of the profiles obtained by your choice of parameters to make sure that the results are reasonable and consistent with your expectations.'''

'''<br />'''

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Minimum depth

2024-06-06T16:10:09Z

Marcus:

The minimum depth is important for several reasons:

# Vertical profilers need about one body length to accelerate to about 80% of their asymptotic speed. Thus, a '''minimum depth of about two body lengths is recommended'''.
# The hull of the ship used to deploy a vertical profiler may reach several meters and data from less than hull depth should be excluded. While a glider is at the surface, shear probe data has no value.
# '''A minimum depth of 1 to 2 m should be applied to gliders''' (in dives) to exclude data that are not useful for dissipation rate estimation.

-----------------------------
return to [[Flow chart for shear probes]]

[[Category:Shear probes]]

Talk:Level 1 data (shear probes)

2022-06-22T16:33:28Z

Marcus: Created page with "I think we should add water_pressure variable as Optional Level 1 variable here as well. We need it to determine average pressure of spectra in L3 and in L4 later."

I think we should add water_pressure variable as Optional Level 1 variable here as well. We need it to determine average pressure of spectra in L3 and in L4 later.

Talk:Quality control coding

2022-03-28T15:24:34Z

Marcus:

During our shear probe group meeting on March 28, 2022, we discussed the possibility of using a different flagging scheme for the shear probe data. Suggestions were to use a binary system that allows to relate the flagging number to one or more specific data quality control tests that would be unique even if multible quality control tests were failed by the data set. However, this flagging scheme would only apply to our data and international data centres or data display display servers (e.g. https://www.ocean-ops.org/board) would not recognize this.
Perhaps a solution would be to use a two-level scheme, as also suggeted by the Intergovernmental Oceanographic Commission (IOC). On the primary level, we use a common quality flagging as shown below. The secondary level could then be a unique number that points to the quality test the data has failed. The flagging scheme below is taken from a best practice IOC document ([https://repository.oceanbestpractices.org/handle/11329/413 link]). The secondary level could then complement the primary level flags by reporting the results of specific QC tests performed and data processing history. As suggested by IOC best practices "The secondary level content varies in number and description and is chosen by those who implement the scheme, representing information on the applied quality tests (e.g., excessive spike check, regional data range check) and data processing history (e.g., interpolated values, corrected values)."

{| class="wikitable"
|+ IOC Quality flagging primary level
|-
! Value !! Primary-level flag short name !! Definition
|-
|1 || Good || Passed documented required QC tests
|-
| 2 || Not evaluated, not available or unknown || Used for data when no QC test performed or the information on quality is not available
|-
| 3 || Questionable/suspect || Failed non-critical documented metric or subjective test(s)
|-
|4 || Bad || Failed critical documented QC test(s) or as assigned by the data provider
|-
|9 || Missing data || Used as place holder when data are missing
|}

Would that work?

Talk:Quality control coding

2022-03-28T15:22:12Z

Marcus: Created page with "During our shear probe group meeting on March 28, 2022, we discussed the possibility of using a different flagging scheme for the shear probe data. Suggestions were to use a b..."

During our shear probe group meeting on March 28, 2022, we discussed the possibility of using a different flagging scheme for the shear probe data. Suggestions were to use a binary system that allows to relate the flagging number to one or more specific data quality control tests that would be unique weven if multible quality control tests were failed by the data set. However, this flagging scheme would only apply to our data and international data centres or data display display servers (e.g. https://www.ocean-ops.org/board) would not recognize this.
Perhaps a solution would be to use a two-level scheme, as also suggeted by the Intergovernmental Oceanographic Commission (IOC). On the primary level, we use a common quality flagging as shown below. The secondary level could then be a unique number that points to the quality test the data has failed. The lagging scheme below is taken from a best practice IOC document ([https://repository.oceanbestpractices.org/handle/11329/413 link]). The secondary level could then complement the primary level flags by reporting the results of specific QC tests performed and data processing history. As suggested by IOC best practices "The secondary level content varies in number and description and is chosen by those who implement the scheme, representing information on the applied quality tests (e.g., excessive spike check, regional data range check) and data processing history (e.g., interpolated values, corrected values)."

{| class="wikitable"
|+ IOC Quality flagging primary level
|-
! Value !! Primary-level flag short name !! Definition
|-
|1 || Good || Passed documented required QC tests
|-
| 2 || Not evaluated, not available or unknown || Used for data when no QC test performed or the information on quality is not available
|-
| 3 || Questionable/suspect || Failed non-critical documented metric or subjective test(s)
|-
|4 || Bad || Failed critical documented QC test(s) or as assigned by the data provider
|-
|9 || Missing data || Used as place holder when data are missing
|}

Would that work?

Flow chart for shear probes

2021-11-15T12:02:36Z

Marcus:

The processing of shear-probe data can be divided into the following five major steps and these steps apply to data collected with any platform or vehicle. There are many sub-steps to these major steps. The major steps are:

__TOC__

== Conversion to physical units ==
<div class="mw-collapsible" id="physical units" data-collapsetext="Collapse" data-expandtext="Expand">
As a first step, the raw binary data needs to be transformed into physical units.
:* [[Determine the speed of profiling]] of the shear-probe through the water.
:* [[Determine the temperature of the water]].
:* [[Convert the shear probe data]] samples into physical units
:* Convert all other signals per the recommendations of the manufacturer of the sensor or instruments that produce these signals.
</div>

== "Section" selection ==
<div class="mw-collapsible" id="Section" data-collapsetext="Collapse" data-expandtext="Expand">

Before you can process your shear-probe data to derive the rate of dissipation you must select the [[section]] of data that you wish to process. For a vertically profiling instrument, this is traditionnally referred to as a "profile". We adopt the term "section" as this is platform independent and will include time series for dissipation estimates along horizontal or slanted trajectories as well as from moored shear probes. You must make sure that the selection is meaningful and sensible. For example, the shear probe must be profiling through the water with a speed, direction, and orientation that is fairly stationary. The selection of data can be partially automated by requiring that the kinematics of your instrument achieve certain minimum criteria. The steps to section selection are as follows:
:* Choose the [[minimum speed]] of profiling.
:* Choose the [[direction of the vertical velocity]] of the profiler.
:* Choose the [[minimum depth]].
:* Choose the [[maximum pitch and roll]] of the profiler.
:* Choose the [[minimum duration]] over which the [[minimum speed]] through [[maximum pitch and roll]] must be satisfied.
</div>

== Choosing the [[processing parameters]] ==
<div class="mw-collapsible" id="processing parameters" data-collapsetext="Collapse" data-expandtext="Expand">

:* Choose the length of data (in meters) used for each dissipation estimate – [[diss-length]].
:* Choose the lowest wavenumber of spectral estimation – [[fft-length]].
:* Translate [[diss-length]] and [[fft-length]] into [[duration]] (time).
:* Round these up to [[nearest power-of-two number]] of samples.
:* Choose a [[high-pass filter cut-off frequency]] to be consistent with (b).
:* Choose [[de-spiking parameters]].
:* Choose [[vibration-coherent noise removal]].
</div>

== Compute the [[dissipation rate estimates]] ==

<div class="mw-collapsible" id="estimates" data-collapsetext="Collapse" data-expandtext="Expand">

The following steps are recommended to obtain estimates of the turbulent dissipation rate of kinetic energy (<math>\varepsilon</math>).

# Extract the section to estimate dissipation time series ("Section" selection).
# High-pass filter the shear-probe and (optionally) the vibration data.
# Identify each diss-length segment in the section.
# [[De-spike the shear-probe data]], and track the fraction of data affected by de-spiking within each diss-length segment. This will become a [[Shear_probes_quality_control_metrics|quality-control metric]].
# Calculate the [[frequency spectra and cross-spectra of shear and vibrations]] for each diss-length segment.
# Extract the original and the vibration-coherent clean shear-probe frequency spectra with [[the Goodman algorithm]].
# Correct shear and vibration frequency spectra for [[the high-pass filter]].
# Correct the cleaned frequency spectra for [[the bias induced by the Goodman algorithm]].
# Convert the frequency spectra into wavenumber spectra using the mean speed for each diss-length segment. That is, make the wavenumber <math> \begin{equation}k=f/U\end{equation}</math> and the wavenumber [[Here|kinetic energy spectrum]] <math> \begin{equation}E(k)=UE(f)\end{equation}</math> .
# Correct the spectra of shear for the [[wavenumber response of the shear probe]].
# Apply an [[iterative spectral integration algorithm]] to estimate the variance of shear.
# Calculate the turbulent dissipation rate by multiplying the shear variance by <math> \begin{equation} \frac{15}{2}\nu\end{equation}</math> where <math> \nu </math> is the temperature-dependent kinematic viscosity.
# Determine the [[figure of merit (FM)]] for each shear-probe spectrum.
# Calculate the expected variance of each dissipation estimate.
</div>

== Apply [[Shear_probes_quality_control_metrics|quality-control metrics]] ==

<div class="mw-collapsible" id="quality_control" data-collapsetext="Collapse" data-expandtext="Expand">

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* figure of merit (FM)
#:* fraction of shear-probe data altered by the de-spiking routine
#:* number of iterations of the de-spiking routine required to clean the data
#:* agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes)

Flagged data will receive quality control coding 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

</div>
--------------------
Return to [[ Shear probes | Shear Probe Welcome Page]]
[[Category: Shear probes]]

Shear probes quality control metrics

2021-11-15T11:57:03Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the [[De-spike_the_shear-probe_data|de-spiking routine]] required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive [[quality control coding]] 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive [[quality control coding]] 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:54:40Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the [[De-spike_the_shear-probe_data|de-spiking routine]] required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive [[quality control coding]] 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive [[quality control coding]] 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:53:21Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the [[De-spike_the_shear-probe_data|de-spiking routine]] required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive [[quality control coding]] 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:52:16Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the [[De-spike_the_shear-probe_data|de-spiking routine]] required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive quality control coding 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:51:35Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the d[[De-spike_the_shear-probe_data|e-spiking routine]] required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive quality control coding 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:49:14Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the [[De-spike_the_shear-probe_data|de-spiking routine]]
#:* number of iterations of the de-spiking routine required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive quality control coding 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]

Shear probes quality control metrics

2021-11-15T11:46:14Z

Marcus:

Spikes in epsilon estimates arise from a number of causes such as collisions of sensor tips with suspended particles (e.g. detritus, plankton, jelly fish, seaweed), electronic noise due to other sensors, or mechanical platform vibrations. This section describes quality control measures and its coding.

In a ''first step'', epsilon estimates are flagged based on quality control metric and disagreement between dissipation estimates from redundant sensors.

# Quality-control metrics (see also Processing Steps section V) that accompanied dissipation estimates are used to flag individual estimates. In particular, quality control thresholds for
#:* [[figure of merit (FM)]]
#:* fraction of shear-probe data altered by the de-spiking routine
#:* number of iterations of the de-spiking routine required to clean the data
#:* (more to be discussed)
# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive quality control coding 4.

In a ''second step'' of quality control, a review of ensembles that have been flagged is performed. Individual shear spectra, associated tilt and acceleration data and microstructure thermistor data/spectra are visually examined for consistency. Previously flagged data that appears to be good data will receive quality control coding 2.

Details for QC coding can be found [[Quality_control_coding|here]].

-----------------------------
return to [[Flow chart for shear probes]]