Quality control of ε estimates (QA2)

1. Data segments for which the regression coefficient a₁ [LINK TO PREVIOUS PAGE] is negative yield an imaginary [math]\displaystyle{ \varepsilon }[/math] value, which should be rejected
2. Data segments for which the regression coefficient a₀ [LINK TO PREVIOUS PAGE] is negative (implying a negative noise floor) are likely to be invalid and are typically rejected
3. 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
4. Evaluate the impact of varying r_max values (within the anticipated inertial range) on [math]\displaystyle{ \varepsilon }[/math]; an increase in [math]\displaystyle{ \varepsilon }[/math] with increasing r_max is likely to indicate that v’ retains a non-turbulent contribution to the velocity difference between bins
5. The goodness of fit (R²) for the regression provides a basic indication of the quality of the fit
6. 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
7. Examine the distribution of [math]\displaystyle{ \varepsilon }[/math] estimates - in most situations, this would be expected to be log-normal
8. 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

Return to ADCP Flow Chart front page