Quality control of ε estimates (QA2)

Quality control measures for each beam:

1. 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
2. Ensure sufficient [math]\displaystyle{ D_{ll} }[/math] samples were used in the regression.
3. 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.
4. 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
5. 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
6. 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
7. The goodness of fit (R²) for the regression provides a basic indication of the quality of the fit
8. 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
9. Examine the distribution of [math]\displaystyle{ \varepsilon }[/math] estimates - in most situations, this would be expected to be log-normal
10. 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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[In progress]

How ADCP structure function quality-control flags are applied

The Q (quality control) flags associated with shear-probe measurements are not compatible with the Ocean Sites Ocean Sites for quality control (QC) coding.

Every dissipation estimate from every probe must have Q flag. The numerical values of the Q flags are as follows:

Flag Mask	Bit	Flag Meaning	Example threshold value	Ex: True =1 / False =0	Ex: Q value
1	Bit 0	if FOM > FOM_limit	2	0	0
2	Bit 1	if despike_fraction > despike_fraction_limit	40%	0	0
4	Bit 2	log(e_max)-log(e_min)|> diss_ratio_limit X \sigma_{\ln\varepsilon}	N/A	1	4
8	Bit 3	if despike_iterations > despike_iterations_limit	To be confirmed	0	0
16	Bit 4	if variance resolved less than a threshold	50%	1	16
32	Bit 5	manual flag to be defined by user	N/A	0	0
64	Bit 6	manual flag to be defined by user	N/A	0	0
128	Bit 7	manual flag to be defined by user	N/A	0	0
					Final Q = 20

The Q flags are combined by their addition. For example a Q value of 20 means that the dissipation estimated failed both dissipation ratio limit test and the resolved variance test. A value of 255 means that all tests failed. The reasons for a failure can be decoded by breaking the value of Q down to its powers of 2.

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