Flow chart for shear probes

The processing of shear-probe data can be divided into five major steps, which 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

Expand

As a first step, the raw binary data needs to be transformed into physical units.

1. Determine the speed of profiling of the shear-probe through the water.
2. Determine the temperature of the water.
3. Convert the shear probe data samples into physical units
4. Convert all other signals per the recommendations of the manufacturer of the sensor or instruments that produce these signals.

Please note that most choices made must be included in a data set, as described in the list of meta data.

"Section" selection

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:

1. Choose the minimum speed of profiling.
2. Choose the direction of the vertical velocity of the profiler.
3. Choose the minimum depth.
4. Choose the maximum pitch and roll of the profiler.
5. Choose the minimum duration over which the minimum speed through maximum pitch and roll must be satisfied.

Please note that most choices made must be included in a data set, as described in the list of meta data.

Choosing the processing parameters

Expand

1. Choose the lowest wavenumber of spectral estimation (as a function of the vehicle's length) – fft-length.
2. Choose the length of data (in meters) used for each dissipation estimate – diss-length.
3. Choose a high-pass filter cut-off frequency to be consistent with duration of the fft-length.
4. Choose de-spiking parameters.
5. Choose vibration-coherent noise removal.

Please note that most choices made must be included in a data set, as described in the list of meta data.

Compute the dissipation rate estimates

Expand

The following steps are recommended to obtain estimates of the turbulent dissipation rate of kinetic energy ([math]\displaystyle{ \varepsilon }[/math]).

1. Extract the section to estimate dissipation time series ("Section" selection).
2. High-pass filter the shear-probe and (optionally) the vibration data.
3. Identify each diss-length segment in the section.
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 detrended 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, [math]\displaystyle{ U }[/math], for each diss-length segment. That is, make the wavenumber [math]\displaystyle{ k=f/U }[/math] and the spectrum [math]\displaystyle{ E(k)=UE(f) }[/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. If the dissipation estimate is larger than shear inertial subrange fit use the method fit to the inertial subrange
13. Calculate the turbulent dissipation rate by multiplying the shear variance by [math]\displaystyle{ \frac{15}{2}\nu }[/math] where [math]\displaystyle{ \nu }[/math] is the temperature-dependent kinematic viscosity.
14. Determine the figure of merit (FOM) for each shear-probe spectrum.
15. Calculate the expected variance of each dissipation estimate.

Please note that most choices made must be included in a data set, as described in the list of meta data.

Apply quality-control metrics

Expand

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 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 QC-flags.

Please note that most choices made must be included in a data set, as described in the list of meta data.

---

Return to Shear Probe Welcome Page