Quality control of ε estimates (QA2): Difference between revisions
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# 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> '''[LINK TO PREVIOUS PAGE]''' is negative yield an imaginary <math>\varepsilon</math> value, which should be rejected | # Data segments for which the regression coefficient a<sub>1</sub> '''[LINK TO PREVIOUS PAGE]''' is negative yield an imaginary <math>\varepsilon</math> value, which should be rejected | ||
# Data segments for which the regression coefficient a<sub>0</sub> '''[LINK TO PREVIOUS PAGE]''' is negative (implying a negative noise floor) are likely to be invalid and are typically rejected | # Data segments for which the regression coefficient a<sub>0</sub> '''[LINK TO PREVIOUS PAGE]''' is negative (implying a negative noise floor) are likely to be invalid and are typically rejected |
Revision as of 15:32, 15 November 2021
- 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.
- 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
- 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
- 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
- Evaluate the impact of varying rmax values (within the anticipated inertial range) on [math]\displaystyle{ \varepsilon }[/math]; an increase in [math]\displaystyle{ \varepsilon }[/math] with increasing rmax is likely to indicate that v’ retains a non-turbulent contribution to the velocity difference between bins
- The goodness of fit (R2) 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]\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
- 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
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