View source for Quality control of ε estimates (QA2)

# Use the coefficient <math>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.
# Data segments for which the regression coefficient a<sub>1</sub> (see [[Processing your ADCP data using structure function techniques | previous step]]) is negative yield an imaginary <math>\varepsilon</math> value, which should be rejected
# Data segments for which the regression coefficient a<sub>0</sub> (see [[Processing your ADCP data using structure function techniques | previous step]]) is negative (implying a negative noise floor) are likely to be invalid and are typically rejected
# 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
# Evaluate the impact of varying r<sub>max</sub> values (within the anticipated inertial range) on <math>\varepsilon</math>; an increase in <math>\varepsilon</math> with increasing r<sub>max</sub> is likely to indicate that v’ retains a non-turbulent contribution to the velocity difference between bins
# The goodness of fit (R<sup>2</sup>) for the regression provides a basic indication of the quality of the fit
# 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 a<sub>1</sub> regression coefficient e.g. reject values where the ratio exceeds a specified threshold
# Examine the distribution of <math>\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

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[[Category:Velocity profilers]]