Raw data review (QA1): Difference between revisions

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== Raw data review ==
The objective of the raw data review is to ensure that the velocity data used for the calculation of the structure functions are of good quality before proceeding with the turbulence analysis. Bad data are typically identified from the velocity data themselves and other ancillary data (e.g. correlations). It is recommended that velocity data should be flagged if the following are observed:


This review is a comparison of data between beams, between bins and over time to determine possible anomalies. Note that consideration of spatial and temporal trends may be informed by data from other sensors e.g. meteorological, wave or CTD sensors. The following characteristics should be examined:  
== Data quality is poor ==
<div class="mw-collapsible mw-collapsed" id="poor quality" data-collapsetext="Collapse" data-expandtext="Expand">
Poor data qualifiers include:  
* Low correlation values
* Echo intensity anomalies
* Low percent good values
* Data return rate varies
</div>


# Correlation coefficient
== Unrealistic velocity values ==
## minimum threshold to identify bad data  
<div class="mw-collapsible mw-collapsed" id="unrealistic" data-collapsetext="Collapse" data-expandtext="Expand">
# Echo intensity
Velocities are unrealistic if there are
## false target / “fish” detection to identify bad data  
* Evidence of phase wrapping
# Orientation (heading, pitch, roll) and depth (if sensor installed)
* Velocities outside nominal measurement range
## deployment as planned?
* Velocities outside expected distribution
## indentify specific changes or periodic motion
</div>
# Along beam velocity
== Significant instrument motion and orientation ==
## data return rate
<div class="mw-collapsible mw-collapsed" id="motion" data-collapsetext="Collapse" data-expandtext="Expand">
## phase wrapping for pulse-pulse coherent observations
Reject data if there is:
## periodicity indicating waves or oscillatory motion
* High variability in pitch, heading and roll
## distribution outliers
* Orientation deviates from expected values
## burst variance spatial and temporal trends
</div>
# Temperature and salinity (if sensors installed)
== Wave or periodic motion contamination ==
## indication of changes in local stratification and/or internal wave activity
<div class="mw-collapsible mw-collapsed" id="waves?" data-collapsetext="Collapse" data-expandtext="Expand">
# Earth coordinate velocity 
## may need to be derived from along-beam velocity
## bin mapping if ADCP orientation isn’t vertical
## error velocity from 4-beam instruments
## comparison with ambiguity velocity to check for possible phase wrapping
## burst variance spatial and temporal trends
## shear over observation range


* Periodic motion at wave frequencies observed in velocity data
</div>
== Velocity shear is too large ==
<div class="mw-collapsible mw-collapsed" id="highshear" data-collapsetext="Collapse" data-expandtext="Expand">
* earth velocities indicate significant horizontal shear
</div>
== Stationary assumption may be violated ==
<div class="mw-collapsible mw-collapsed" id="nonstationary" data-collapsetext="Collapse" data-expandtext="Expand">
* variance of velocity [[Burst sampling|bursts]] shows spatial or temporal trends
</div>
== Stratification is too large ==
<div class="mw-collapsible mw-collapsed" id="toostratified" data-collapsetext="Collapse" data-expandtext="Expand">
* Temperature and salinity (if available) indicate local stratification
</div>
-----
Next step: [[Processing your ADCP data using structure function techniques | Compute structure functions and dissipation estimates]]<br></br>
Previous step: [[ Deployment ]] <br></br>
Return to [[ADCP structure function flow chart| ADCP Flow Chart front page]]
Return to [[ADCP structure function flow chart| ADCP Flow Chart front page]]
[[Category:Velocity profilers]]

Latest revision as of 19:21, 8 March 2022

The objective of the raw data review is to ensure that the velocity data used for the calculation of the structure functions are of good quality before proceeding with the turbulence analysis. Bad data are typically identified from the velocity data themselves and other ancillary data (e.g. correlations). It is recommended that velocity data should be flagged if the following are observed:

Data quality is poor

Poor data qualifiers include:

  • Low correlation values
  • Echo intensity anomalies
  • Low percent good values
  • Data return rate varies

Unrealistic velocity values

Velocities are unrealistic if there are

  • Evidence of phase wrapping
  • Velocities outside nominal measurement range
  • Velocities outside expected distribution

Significant instrument motion and orientation

Reject data if there is:

  • High variability in pitch, heading and roll
  • Orientation deviates from expected values

Wave or periodic motion contamination

  • Periodic motion at wave frequencies observed in velocity data

Velocity shear is too large

  • earth velocities indicate significant horizontal shear

Stationary assumption may be violated

  • variance of velocity bursts shows spatial or temporal trends

Stratification is too large

  • Temperature and salinity (if available) indicate local stratification

Next step: Compute structure functions and dissipation estimates

Previous step: Deployment

Return to ADCP Flow Chart front page