Flow chart for shear probes: Difference between revisions

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==      Compute the [[dissipation rate estimates]] from shear probes. ==
==      Compute the [[dissipation rate estimates]] from shear probes. ==
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.
#      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.
==      Apply [[Shear_probes_quality_control_metrics|quality-control metrics]]. ==
==      Apply [[Shear_probes_quality_control_metrics|quality-control metrics]]. ==



Revision as of 21:49, 8 November 2021

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;

Conversion to physical units.

  • Determine the speed of profiling of the shear-probe through the water.

The speed of profiling can be determined with a variety of different methods. It must be determined because it is required for the conversion of the shear-probe samples into physical units and for transforming frequency spectra into wavenumber spectra. Methods for determining the speed of profiling include, but are not limited to:

  • Using the rate-of-change of pressure for a vertical profiler.
  • Using the rate-of-change of pressure, the pitch angle, and the angle of attack for a glider.
  • Using a flight model for a glider.
  • Using an onboard current meter.
  • Using a constant speed.
  • Using the speed recorded by an independent instrument after its record has been carefully aligned in time with the shear-probe data.
  • 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.

"Section" selection.

  1. 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. 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 profile selection are as follows:

Choosing the processing parameters for shear probes.

Compute the dissipation rate estimates from shear probes.

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

  1. Extract the section defined in step 2 ("Section" selection).
  2. High-pass filter the shear-probe and (optionally) the vibration data.
  3. Identify each diss-length segment in the profile.
  4. 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.
  5. Calculate the frequency spectra and cross-spectra of shear and vibrations for each diss-length segment.
  6. Extract the original and the vibration-coherent clean shear-probe frequency spectra with the Goodman algorithm.
  7. Correct shear and vibration frequency spectra for the high-pass filter.
  8. Correct the cleaned frequency spectra for the bias induced by the Goodman algorithm.
  9. Convert the frequency spectra into wavenumber spectra using the mean speed for each diss-length segment. That is, make the wavenumber [math]\displaystyle{ \begin{equation}k=f/U\end{equation} }[/math] and the wavenumber kinetic energy spectrum [math]\displaystyle{ \begin{equation}E(k)=UE(f)\end{equation} }[/math] .
  10. Correct the spectra of shear for the wavenumber response of the shear probe.
  11. Apply an iterative spectral integration algorithm to estimate the variance of shear.
  12. Calculate the turbulent dissipation rate by multiplying the shear variance by [math]\displaystyle{ \begin{equation} \frac{15}{2}\nu\end{equation} }[/math] where [math]\displaystyle{ \nu }[/math] is the temperature-dependent kinematic viscosity.
  13. Determine the figure of merit (FM) for each shear-probe spectrum using the method described here.
  14. Calculate the expected variance of each dissipation estimate using the method described here.


Apply quality-control metrics.

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