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>\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>\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>\varepsilon</math>; an increase in <math>\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>\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>\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

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