Atomix - User contributions [en]

Quality control coding

2021-06-25T19:16:27Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for quailty control (QC) coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf Intergovernmental Oceanographic Commission of UNESCO (2013)].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but detailed examination after processing (e.g. by visual examination) suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

 
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Shear probes quality control metrics

2021-05-28T17:48:38Z

Marcus Dengler:

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 matric 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]].

Shear probes quality control metrics

2021-05-28T17:48:20Z

Marcus Dengler:

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 matric 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 a 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]].

Quality control coding

2021-05-28T17:44:32Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for quailty control (QC) coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf IOC].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but detailed examination after processing (e.g. by visual examination) suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

 
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T16:32:20Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf IOC].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but detailed examination after processing (e.g. by visual examination) suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

 
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T16:30:40Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf IOC].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

 
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T16:28:08Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf IOC].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}
 
Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Shear probes quality control metrics

2021-05-28T16:26:58Z

Marcus Dengler:

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 matric 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 a 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]].

Quality control coding

2021-05-28T16:17:03Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging recommended by [http://www.ioccp.org/images/D4standards/IOC-OceanDataStandards54-3-2013.pdf IOC].

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T15:19:03Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging suggested by IOC

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || nominal value || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T15:17:16Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging suggested by IOC

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || bad data || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>. 
<PARAM>_QC 
<PARAM>_QC:long_name = “quality flag of <PARAM>”; 
<PARAM>_QC:conventions = “OceanSITES QC Flags”; 
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9; 
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T15:14:24Z

Marcus Dengler:

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging suggested by IOC

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || bad data || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

Climate and Forecast Metadata Convention (CF) requires that QC flags carry attributes. In netCDF (Network Common Data Form) data files, the following information for quality control flagging should be provided for each data variable <PARAM>.
<PARAM>_QC
<PARAM>_QC:long_name = “quality flag of <PARAM>”;
<PARAM>_QC:conventions = “OceanSITES QC Flags”;
<PARAM>_QC:flag_values = 0, 1, 2, 3, 4, 7, 8, 9;
<PARAM>_QC:flag_meanings = “0:unknown 1:good_data 2:probably_good_data 3:potentially_correctable_bad_data 4:bad_data 7:nominal_value 8:interpolated_value 9:missing_value”

Quality control coding

2021-05-28T13:17:45Z

Marcus Dengler: Created page with " '''Quality control coding''' We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level fla..."

'''Quality control coding'''

We suggest to follow [http://www.oceansites.org/ Ocean Sites] for QC coding. The flagging scheme is mostly compatible with the primary level flagging suggested by IOC

{| class=wikitable style=height:14em
|-
! Flag !! Meaning !! Comment
|-
| 0|| unknown || No QC was performed.
|-
| 1 || good data || All QC tests passed.
|-
| 2 || probably good data || Data have failed one or more QC tests but visual examination suggests data is good.
|-
| 3 || potentially correctable bad data || These data are not to be used without scientific correction or re-calibration (e.g. uncertain shear sensor sensitivity).
|-
| 4 || bad data || Data have failed one or more tests.
|-
| 5 || - || Not used
|-
| 6 || - || Not used
|-
| 7 || bad data || Data were not observed but reported (e.g. instrument target depth.).
|-
| 8 || interpolated value || Missing data may be interpolated from neighboring data in space or time.
|-
| 9 || missing value || This is a fill value
|}

Shear probes quality control metrics

2021-05-28T11:47:25Z

Marcus Dengler:

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 matric 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 a 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.

Shear probes

2021-05-28T11:45:39Z

Marcus Dengler:

== Flow chart for dissipation estimates using shear probes ==
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.
#:* 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.
# Profile selection.
#: Before you can process your shear-probe data to derive the rate of dissipation you must select the segment of data that you wish to process. You must make sure that the selection is meaningful and sensible. For example, the shear probe most 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:
#:* 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.
# Choosing the processing parameters.
# Dissipation rate estimation.
# Applying [[Shear_probes_quality_control_metrics|quality-control metrics]].

Shear probes quality control metrics

2021-05-28T10:48:04Z

Marcus Dengler:

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 matric 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
#:* FM (figure of merit)
#:* 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 a 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.

Shear probes quality control metrics

2021-05-28T10:47:18Z

Marcus Dengler:

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 matric 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
#:* FM (figure of merit)
#:* 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)
allows for flagging of individual epsilon estimates.

# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive a 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.

Shear probes quality control metrics

2021-05-28T10:46:02Z

Marcus Dengler:

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 matric 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

#:* FM (figure of merit)
#:* 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)
allows for flagging of individual epsilon estimates.

# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive a 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.

Shear probes quality control metrics

2021-05-28T10:45:40Z

Marcus Dengler: Created page with "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..."

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 matric 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

#: FM (figure of merit)
#: 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)
allows for flagging of individual epsilon estimates.

# Agreement between dissipation estimates from redundant sensors (i.e. two or more shear probes) does not exist.

Flagged data will receive a 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.