Quality control of ε estimates (QA2)

Quality control measures for each beam (flagged in benchmark datasets):

Data segments for which the regression coefficient a₁ (see previous step) is negative yield an imaginary [math]\displaystyle{ \varepsilon }[/math] value, which should be rejected
Ensure sufficient [math]\displaystyle{ D_{ll} }[/math] samples were used in the regression.
Use the coefficient [math]\displaystyle{ a_0 }[/math] (the intercept of the regression) to estimate the noise of the velocity observations and compare to the expected value based on the instrument settings. If noise is too high, [math]\displaystyle{ \epsilon }[/math] are rejected.
Data segments for which the regression coefficient a₀ (see previous step) is negative (implying a negative noise floor) are likely to be invalid and are typically rejected
In the case of [math]\displaystyle{ \epsilon }[/math] estimated using the modified regression method that accounts for oscillatory motion, reject data for invalid values of [math]\displaystyle{ a_3 }[/math].
A better indication of the quality of the fit is usually provided by looking at the ratio of the estimated [math]\displaystyle{ \varepsilon }[/math] value to that based on the 95%-ile confidence interval estimate of the a₁ regression coefficient e.g. reject values where the ratio exceeds a specified threshold
The goodness of fit (R²) for the regression provides a basic indication of the quality of the fit, data with low R² are typically rejected.

Other measures (not flagged):

Examine the distribution of [math]\displaystyle{ \varepsilon }[/math] estimates - in most situations, this would be expected to be log-normal
Comparison of observed values with nominal values based on established boundary-forced scalings may also be informative and help to identify observation or processing issues

Quality control measures for final [math]\displaystyle{ \epsilon }[/math] estimate:

Examine the consistency of [math]\displaystyle{ \varepsilon }[/math] between bins (if evaluated) and between beams as an indication of estimate reliability - the geometric mean between beams is frequently used as the representative value

To see how the data flags are applied, go to Velocity Profiler Data Flags

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