Quality control of ε estimates (QA2) - Revision history

Yuengdjern at 20:58, 3 June 2022

2022-06-03T20:58:09Z

@@ Line 1: / Line 1: @@
-Quality control measures for each beam:
+Quality control measures for each beam (flagged in benchmark datasets):
 # 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
 # Ensure sufficient <math> D_{ll} </math> samples were used in the regression.
@@ Line 16: / Line 16: @@
 ------
 To see how the data flags are applied, go to [[Velocity Profiler data flags| Velocity Profiler Data Flags]]

Yuengdjern at 20:56, 3 June 2022

2022-06-03T20:56:44Z

← Older revision		Revision as of 20:56, 3 June 2022
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	# 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		# 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
	------		------
			To see how the data flags are applied, go to [[Velocity Profiler data flags\| Velocity Profiler Data Flags]]
	'''[In progress]		'''[In progress]
	'''		'''
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	~~[[Category: Shear probes]]~~

Yuengdjern at 20:51, 3 June 2022

2022-06-03T20:51:11Z

← Older revision		Revision as of 20:51, 3 June 2022
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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. If noise is too high, <math> \epsilon </math> are rejected.		# 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. If noise is too high, <math> \epsilon </math> are 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		# 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
	# In the case of <math> \epsilon </math> estimated using the modified regression method that accounts for oscillatory motion, reject data for invalid values of <math> ~~A_3~~ </math>.		# In the case of <math> \epsilon </math> estimated using the modified regression method that accounts for oscillatory motion, reject data for invalid values of <math> a_3 </math>.
	# 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		# 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
	# The goodness of fit (R<sup>2</sup>) for the regression provides a basic indication of the quality of the fit, data with low R<sup>2</sup> are typically rejected.		# The goodness of fit (R<sup>2</sup>) for the regression provides a basic indication of the quality of the fit, data with low R<sup>2</sup> are typically rejected.

Yuengdjern at 20:50, 3 June 2022

2022-06-03T20:50:15Z

← Older revision		Revision as of 20:50, 3 June 2022
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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. If noise is too high, <math> \epsilon </math> are rejected.		# 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. If noise is too high, <math> \epsilon </math> are 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		# 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~~		# In the case of <math> \epsilon </math> estimated using the modified regression method that accounts for oscillatory motion, reject data for invalid values of <math> A_3 </math>.
	~~# 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		# 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
			# The goodness of fit (R<sup>2</sup>) for the regression provides a basic indication of the quality of the fit, data with low R<sup>2</sup> are typically rejected.

			Other measures (not flagged):
	# Examine the distribution of <math>\varepsilon</math> estimates - in most situations, this would be expected to be log-normal		# 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		# 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

			Quality control measures for final <math> \epsilon </math> estimate:
			# 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
	------		------
	'''[In progress]		'''[In progress]

Yuengdjern at 20:41, 3 June 2022

2022-06-03T20:41:27Z

@@ Line 1: / Line 1: @@
-# 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.
+Quality control measures for each beam:
 # 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
@@ Line 10: / Line 12: @@
 ------
 Return to [[ADCP structure function flow chart| ADCP Flow Chart front page]]
 [[Category:Velocity profilers]]

CynthiaBluteau at 16:03, 23 November 2021

2021-11-23T16:03:58Z

← Older revision		Revision as of 16:03, 23 November 2021
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	# 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		# 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 structure function flow chart\| ADCP Flow Chart front page]]		Return to [[ADCP structure function flow chart\| ADCP Flow Chart front page]]

			[[Category:Velocity profilers]]

Yuengdjern at 16:42, 15 November 2021

2021-11-15T16:42:07Z

← Older revision		Revision as of 16:42, 15 November 2021
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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.		# 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> (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> ~~'''~~[~~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> (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		# 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		# 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

Yuengdjern at 15:32, 15 November 2021

2021-11-15T15:32:17Z

← Older revision		Revision as of 15:32, 15 November 2021
Line 1:		Line 1:
			# 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

Brian scannell at 14:37, 10 November 2021

2021-11-10T14:37:21Z

← Older revision		Revision as of 14:37, 10 November 2021
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	# ~~Reject ensembles where~~ the ~~slope is negative (i.e. ε~~<sub>i</sub><0)		# 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
	# ~~Reject ensemble where Ni values are unreasonable (I.e. N~~<sub>~~Subscript text~~</sub>i<0).		# 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
	# ~~Estimate~~ the ~~uncertainty in ε~~<sub>i</sub> as the ~~95% confidence interval~~ on the ~~slope. Reject values that are too large~~ (~~e.g. delta ε~~<~~sub~~>i</~~sub~~> / ε<sub>i</sub> >0.6).		# 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
	# ~~Average~~ the ε<~~sub~~>i</~~sub~~> estimates ~~from the diverging beams (if applicable)~~ to ~~determine ε.~~		# 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
	# ~~Check if distribution~~ of ~~ε is lognormal~~		# 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


	Return to [[ADCP structure function flow chart\| ADCP Flow Chart front page]]		Return to [[ADCP structure function flow chart\| ADCP Flow Chart front page]]

Yuengdjern at 13:39, 22 September 2021

2021-09-22T13:39:49Z

← Older revision		Revision as of 13:39, 22 September 2021
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	~~== Quality Control of Dissipation Estimates ==~~

	# Reject ensembles where the slope is negative (i.e. ε<sub>i</sub><0)		# Reject ensembles where the slope is negative (i.e. ε<sub>i</sub><0)
	# Reject ensemble where Ni values are unreasonable (I.e. N<sub>Subscript text</sub>i<0).		# Reject ensemble where Ni values are unreasonable (I.e. N<sub>Subscript text</sub>i<0).