Raw data review (QA1): Difference between revisions

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== Raw data review ==
== Raw data review ==


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:  
The raw data review seeks to identify the Level 1 data that satisfies a range of quality control criteria.  Typical criteria used to identify possible bad data include:


# Velocity limits
## Check for values outside the nominal measurement range for the instrument configuration
## Check for evidence of phase wrapping (ambiguity velocity) issues
# Correlation coefficient  
# Correlation coefficient  
## minimum threshold to identify bad data
## minimum threshold
# Echo intensity  
# Echo intensity  
## false target / “fish” detection to identify bad data
## false target / “fish” detection
# Percent good
## applies to measurement modes with in-instrument averaging across multiple pings per ensemble
# Orientation (heading, pitch, roll) and depth (if sensor installed)  
# Orientation (heading, pitch, roll) and depth (if sensor installed)  
## deployment as planned?  
## deployment as planned?  
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## burst variance spatial and temporal trends  
## burst variance spatial and temporal trends  
## shear over observation range
## shear over observation range
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:


Return to [[ADCP structure function flow chart| ADCP Flow Chart front page]]
Return to [[ADCP structure function flow chart| ADCP Flow Chart front page]]

Revision as of 13:11, 9 November 2021

Raw data review

The raw data review seeks to identify the Level 1 data that satisfies a range of quality control criteria. Typical criteria used to identify possible bad data include:

  1. Velocity limits
    1. Check for values outside the nominal measurement range for the instrument configuration
    2. Check for evidence of phase wrapping (ambiguity velocity) issues
  2. Correlation coefficient
    1. minimum threshold
  3. Echo intensity
    1. false target / “fish” detection
  4. Percent good
    1. applies to measurement modes with in-instrument averaging across multiple pings per ensemble
  5. Orientation (heading, pitch, roll) and depth (if sensor installed)
    1. deployment as planned?
    2. indentify specific changes or periodic motion
  6. Along beam velocity
    1. data return rate
    2. phase wrapping for pulse-pulse coherent observations
    3. periodicity indicating waves or oscillatory motion
    4. distribution outliers
    5. burst variance spatial and temporal trends
  7. Temperature and salinity (if sensors installed)
    1. indication of changes in local stratification and/or internal wave activity
  8. Earth coordinate velocity
    1. may need to be derived from along-beam velocity
    2. bin mapping if ADCP orientation isn’t vertical
    3. error velocity from 4-beam instruments
    4. comparison with ambiguity velocity to check for possible phase wrapping
    5. burst variance spatial and temporal trends
    6. shear over observation range

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:

Return to ADCP Flow Chart front page