Data Quality of Nortek Vector ADV in the North East Channel of the Gulf of
Maine May 2007.
29 June 2007
Analysis and Report Prepared by
David S. Ciochetto
Department of Oceanography
Dalhousie University
Halifax, N.S. Canada
Abstract
A Nortek Vector ADV was included in a benthic deployment on the continental
shelf (173 m water depth) as an examination of the instrument’s performance in
this environment. A previous deployment of this class of instrument in this
environment was unknown to date to the author. Based on previous experience
with the instrument, the performance was not as good as expected by the author.
Data quality results are presented which characterize the deployment. At the time
of this report, comparisons to the acoustically rich environment of Bedford Basin
and particle characteristics are not available. The experiment was not exhaustive
and the deployment settings were not optimal. Further investigation of the ADV
operation in this environment is encouraged. Based on the data taken here, an
ADV has a high probability (not quantified) of obtaining mean velocity
measurements in this environment. The main source of particles was hypothesized
to be sediment re-suspended from the bottom due to tides. The signal to noise
ratio appeared to slightly lag the spring-neap tidal variation indicating a history
effect in sediment suspension of a few tidal cycles.
1. Introduction
In May 2007 a Nortek Vector Acoustic Doppler Velocimeter (ADV) was deployed on the
RALPH frame among a suite of other instruments at a depth of 170 m. The deployment site was
at (42.05313o, -65.96678o) in the North East channel of the Gulf of Maine (Fig. 1). The ADV in
the experiment had probe serial number VEC 4527 and hardware serial number VEC 1072. Data
was provided to the author for comment on the quality of the data. This report details that work.
2. Results
Data were obtained from 13 May 2007 at 00:00 GMT to 29 May 2007 at 14:10 GMT.
The ADV was set to sample in burst mode at 2 Hz for the first 10 minutes of every 2 hour period.
During the time that the instrument was submerged, there were no error flags reported by the
instrument. Data bursts during the experiment were acquired either at depth or on deck. Data
reported in the sensor file (*.sen) were broken into the time on deck and the time of the
experiment via the error code, status code, compass and tilt sensor data. Prior to 28 May 2007 the
probe pointed down as indicated by the status code. The data files (*.dat) were analyzed as a
continuous time series rather than as bursts. In this format, the pressure signal was used to
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determine the time period that the instrument was on the bottom. Determination of the actual
experiment times was simpler and more reliable when determined from the pressure signal.
Matlab code was generated to read the important header info into a structure in the
Matlab workspace and to manipulate Nortek format ADV burst data. Scripts were written to
generate the time variable for the bursts based on the information provided in the header data.
The data was analyzed as a continuous time series rather than through the selection of specific
bursts. Matlab scripts to read the burst format ASCII data were started but to date have not been
completed. All times reported in figures in the remainder of this report will be in elapsed year
day relative to 1 May 2007 at midnight GMT (e.g. 12.25 indicates that 12 and ¼ days have gone
by so the calendar date would be 13 May 2007 at 06:00:00.)
The instrument settings included a nominal velocity range of 1 m s-1, a sampling volume
(length) of 14.9 mm, high power level, ENU coordinates for the velocity data, sound speed
calculated based on the measured temperature (T) from the ADV sensor and a fixed salinity (S)
of 35.0 ppt. The implication of some of these settings will be discussed later.
The sensor data (NEC70701.sen) are taken at 1 Hz and for a static deployment serve
primarily as a quality check on the deployment. The Lithium-Ion battery pack held steady at 10.7
+/- 0.1 V throughout the experiment with an initial and final on deck value of 11.3 and 11.0 V
respectively. The temperature and sound speed (Fig. 2) dropped over the period of the
experiment. The sound speed data is only used to post process the velocity data to correct for a
more accurate sound speed. Only ADV data existed for this analysis and the focus was not on the
velocity but rather the data quality, thus Fig. 2 is presented for reference and comparison to other
data from RALPH. The compass and tilt sensor data indicated significant variation prior to 13
May at 00:00:00 and two hours prior to the end time indicated by the error codes. This data
established the time of the start of the experiment with respect to the sensor data. As mentioned
above, it was simpler to determine the period of good data solely from the pressure sensor for
this analysis. The compass and tilt sensor were stable on the bottom with a heading of 87.3o +/0.13o, pitch of 1.4o +/- 0.08o and a roll of -0.9o +/- 0.1o where the best estimates are the means of
112998 samples and the reported uncertainties are simply the standard deviations.
2.1 Background Noise
The ADV measures the ambient noise during the first cycle of every burst. The
instrument is powered up and activates its receivers for the first second to measure the
background noise level before each burst. The noise amplitude [counts] and correlation [%] are
summarized for the 188 noise samples in Fig. 3. The noise correlation distribution has a median
for all three receiver beams near 4%. The noise amplitude is steady near 47.5 counts. The
conversion from the instrument-referenced amplitude unit of counts to dB is x [counts] * 0.43 =
x [dB]. The noise amplitude measured prior to each burst is the reference used to determine the
SNR of each sample based on its amplitude
SNRi = amplitudei − amplitude noise j .
(1)
No significant temporal trend was discerned in the noise data throughout the extent of this
experiment.
2
2.2 Pressure signal and tides
As the instrument was mounted on a stationary frame, the pressure signal will reflect the
surface elevation of the ocean. The Matlab script sw_dpth.m (Morgan 1994) was used to convert
the pressure signal in db to a depth in m. The data are shown in Fig. 4. To compare this to the
surface tidal expression, Web Tide (Hannah and Chaffey 2007) was used to estimate the surface
elevation and currents due to the tides at the location shown in Fig. 1, (42.05313o, -65.96678o).
The pressure data, when compared to the prediction, illustrate that the major surface
expression was due to the tide. Only qualitative comparison was accomplished at this stage due
to the requirement to match the data prediction times to the pressure data or to average the
pressure data to the relevant tidal prediction times. Neither approach was followed in the analysis
for this report. The experiment captured the spring and neap variability as well as the semidiurnal variability in this location (Fig. 4a.).
A detail view of two days of data is illustrated in Fig. 4b. Qualitatively, the Web Tide
prediction and the pressure sensor data have a good match. Variability from the prediction will
arise from atmospheric forcing such as swell, chop and local storm influences. Meteorological
forcing data were not a focus of this analysis so that data were not sought or examined.
The pressure sensor produced non-zero pressure readings on the deployment and
recovery vessels. Prior to the experiment, the median of the pressure signal was 0.192 db. The
post-deployment pressure median was 0.113 db. Combining the data from these times produces a
bimodal histogram with peaks around 0.1 and 0.21 db with a median of 0.157 db. Since we are
near the surface, this is an offset of about 16 cm. It will not be considered for the remainder of
the analysis. The Nortek Vector ADV has a feature on deployment to zero the pressure. It is
recommended to zero the pressure sensor at the start of the deployment to remove this offset.
2.3 Correlation
Correlation is the primary measure of signal data quality in an ADV. Generally accepted
levels are 70% for most applications. No theoretical predictions are available in the literature
either for general or specific acoustic environments. The ADV measures the scattered sound
from consecutive pulse pairs. After signal processing, the phase difference determines the
velocity (Voulgaris and Trowbridge 1998) and the correlation is a measure of the similarity of
the two pulses. The echoes are generated by acoustic differences between particles in the sample
volume and the water in which they are suspended. Size and shape of the particle with respect to
the operating frequency of the sound are also a factor in the strength and character of the sound
energy returned to the instrument. Low correlation can indicate high levels of turbulence on the
scale of the sample volume, high advection of particles through the sample volume, particles that
are poor reflectors or a lack of particles.
The correlation for this experiment is shown in Fig. 5. Note that Fig. 5 is a plan view of a
two dimensional histogram where the intensity of the black color indicates the number of
samples in that correlation-time bin. To enhance the contrast of the figure, the black upper limit
was reduced to 70 samples per bin. There are a total of 676,800 samples represented in Fig. 5. By
limiting the color scale to 70, only 11 bins are set to black compromising their real magnitude
relative to the other bins. This represents 770 samples or 0.1 % of the data represented. As the
point of the figure is to indicate the temporal trend in correlation with the fact that the large
values are still represented, there is no compromise in the scientific integrity of the plot. The
data at the start of the experiment have a mean correlation near 75%, which drops steadily
toward the 15th of May. There is a significant rise in the correlation values from a mean of
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approximately 68% near 15 May 00:00 GMT to an approximate mean of 85% near 17 May
12:00 GMT. The data that follow the 17th of May are mostly above the 70% correlation limit
with a subjective mean of about 85%, which is relatively steady with time. Several spikes in
correlation occur; the most significant one reaches a strong mean correlation near 93% on
approximately 26 May 00:00 GMT.
The scattered sound propagates along the path of the receiving transducers. When the
data are converted into an orthogonal coordinate system, each beam contributes to each resulting
velocity component. Thus, if the data is deemed poor in one beam, then that entire velocity
sample must be deemed poor and discarded. Data with correlation less than 70% in any beam
were removed. Figure 6 illustrates the percentage of data in a given burst that had correlation >
70% in all three beams and hence “good” data for the first pass of data quality control. Data is
seen to be poor in the early part of the experiment prior to 15 May with “good” data per burst
ranging from 10 – 30%. A dramatic rise in data quality begins near 14 May 12:00 GMT and
concludes near the 16th of May near 12:00 GMT. On average, over 80% of the data taken after
this rise pass the correlation filter. Spikes in the percentage of good data occur as seen in Fig. 6.
2.4 SNR
The signal to noise ratio (SNR) is the relationship (Eq. 1) of the signal amplitude of each
sample to the ambient acoustic noise measured at the start of each burst (Fig. 3). The SNR serves
as a secondary data quality check. Loose recommendations by the manufacturers of ADVs state
that once SNR > 15 the data are unquestionably good (given good correlation) and that SNR >
10 will give acceptable data; one manufacturer states that data with SNR as low as 5 may be used
to estimate mean velocity values. As with the correlation, there have been no theoretical or
experimental evidence presented in the literature to support these rule of thumb limits. The SNR
gives an indication of the strength of the echoes relative to the local environment. High SNR
indicates acoustically favorable particles in the sample volume or a sufficient number of
scatterers of acoustic properties to generate a high return. Low SNR indicates either a lack of
acoustically favorable particles to the instrument, instrument damage or no particles in the
scattering volume at the desired sampling rate. The ADV was developed for use in a laboratory
and, as such, when SNR is low, additional seeding is added to the flow until the signal strength is
adequate. Field applications do not share this feature and are limited to working with available
marine particles. The literature concerning the use of the ADV in acoustically unfriendly
environments does not exist to date with the only submission coming close stating that the data
quality was low but it did not halt the publication of the data (Zhang, Streitlien, Bellingham, and
Baggeroer 2001). The ADV has been used successfully in the field for coastal benthic
applications (e.g. Anderson and Lohrmann 1995: Barbhuiya and Dey 2004: Voulgaris and
Meyers 2004).
The temporal results for SNR are presented in Fig. 7. The limits of SNR of 10 and 5
discussed above are indicated. The data presented in Fig. 7 only include data with correlation >
70%. The general average SNR for the experiment is around 7.5. Near the beginning of the
experiment, prior to 15 May, the SNR is generally low. The light colors indicate that much of the
data did not pass the 70% correlation threshold as seen in Fig. 6. Spikes of higher SNR and more
data (darker) visually correlate with the spikes in correlation seen in Figs. 5 and 6. Beginning
near the 15th of May, as seen previously, the SNR increases to a level with an approximate mean
of 10 until 23 May where it declines to an approximate mean of 7.5. The remainder of the
experiment maintains this SNR with the exception of a pulse of strong data quality near 25 May
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at 18:00. The mean and variability about the mean were calculated. They follow the darkest parts
of Fig. 7. To maintain clarity of the figure, this information is not included. No limitations were
placed on the color range in Fig. 7 as were applied to Fig. 5.
2.5 Velocity
The velocity is the desired data product from an ADV. It is derived from the speed of
sound in the water, the probe geometry and the phase difference between pulse pairs. After
filtering the data for low correlation or SNR, the data must be checked for phase errors. With the
Nortek Vector ADV, the setup software indicates the actual velocity range that it can detect
based on the selected nominal velocity range. As the phase difference between pulses in pulsepairs determines the velocity of the particles in the sample volume, if a particle travels too fast, a
velocity ambiguity may occur due to the inability to determine the proper phase. Any data that
exceeds the limits is therefore known to be bad. It and the others in that velocity vector sample
must be discarded during post-processing.
A preliminary analysis of the ADV velocity data was performed. Barotropic tidal
velocities were predicted by Web Tide. Figure 8 illustrates the comparison between the
prediction and the measurements. The ADV velocities are significantly less than the Web Tide
prediction, even in magnitude. The ADV did not measure a superposition of local velocity on the
barotropic tidal velocity. The reduction in velocity is suspected to result from the presence of the
bottom boundary layer. Only a point velocity record at the ADV sample volume exists for this
analysis so this assumption can not be verified. Figure 8 indicates that the magnitude of the
Northward velocity exceeds that of the Eastward velocity where Web Tide predicts similar
magnitudes for these velocity components. The difference is assumed to be due to topographic
and stratification effects not accounted for in the model. The amplitude of u in Fig. 8c indicates
difficulty in determining phase differences between the ADV and the prediction. Figure 8d
however indicates that the ADV data actually lead the prediction by approximately 0.05 days or
1.2 hours. This difference is assumed to be due to topographical or stratification effects. In Figs.
8b and 8d the velocity tends toward a Southward mean rather than zero mean. This is indication
of a persistent flow out of the North East Channel or a geostrophic flow about George’s Bank.
3. Discussion
3.1 Comparison to expected instrument response
Data taken by the author previously at station 2 of the Halifax Section of the Atlantic
Zonal Monitoring Program in 155 m of water indicated better data quality than were observed in
the current experiment (Ciochetto 2007). These previous data were taken on 3 Sep 2003 from a
CTD cage with cast depths to 140 m. The site was located 30 km from the coast of Nova Scotia.
Figure 2 of Ciochetto (2007) illustrates that SNR rose to 10 at a depth of 128 m and linearly
increases from 12 to 16 (average of all three beams). Figure 2 (Ciochetto 2007) indicated that
correlation in this depth range started near 87, quickly reached a constant 94 near 128 m and
remained constant. The trial indicated in Ciochetto (2007) clearly predicted better data quality
than was observed in the present experiment.
There are several reasons why the data quality may have differed in the data in this
report. The possibilities include a smaller sampling volume, different particle characteristics,
different particle load in suspension, different ADV probe and different turbulence levels.
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The setup of the instrument indicated that the sampling volume was set to 14.7 mm
height where in the previous experiment, the sampling volume was set to 18.0 mm. The sampling
volume is determined by the diameter of the transmit pulses and the geometry of the return paths
(Voulgaris and Trowbridge 1998). The beams may be considered cylindrical in the acoustic nearfield (Borg 2002). Thus the sampling volume was 0.82 of the previous. No research in the
literature indicates how an increased sampling volume of 22% impacts the data quality.
Acoustic instruments rely on sound scattering from particles. There are many
characteristics of the seawater and the particles that are important (Hay 1991). It is assumed that
there are sufficient numbers of particles in the sampling volume such that their individual
orientations are insignificant. It is also assumed that the particles are mainly re-suspended from
the bottom. Figure 2 of Ciochetto (2007) supports this assumption in the fact that the data quality
increases greatly as the bottom boundary layer is approached. This assumption will be
investigated further in section 3.2. Future data massaging should include comparison of the tidal
forcing as predicted by Web Tide for the data of Ciochetto (2007). A look at the OBS data
should indicate the particle load for this experiment and can be compared to Ciochetto (2007).
Another possible problem might be that too many particles are in the sampling volume
and the echoes are too strong. This can be ruled out based on Fig. 7 and Fig. 9 (a summary of the
data quality parameters which will be discussed in more detail in part 3 of this section). A lower
correlation and a high SNR would support this hypothesis but the data clearly show that the
correlation and SNR drop together.
A 6 MHz acoustical instrument is most sensitive to particles with a diameter of 78 μm.
Particle size spectra were determined for the data presented in Ciochetto (2007). The maximum
particle diameter of a single particle in the sample volume at 140 m depth was measured to be 30
μm which increased from 20 μm at 100 m depth. The particles decreased in diameter with a
power law. The particle load was estimated to be 230 +/- 22 ml-1. If a dominant particle
generation mechanism exists, a Gaussian particle size spectrum will result. Without an estimate
of the particle size spectra it is difficult to predict the particles near the bottom in the Gulf of
Maine. The assumption will be made that the particle size spectra is a power law similar to the
previous experiment.
The most difficult acoustical qualities to access are the density and speed of sound
contrast between the particles and the water in which they are suspended. This would require a
sample of the suspected sound scattering material for every deployment and throughout time.
This is impractical and technically difficult. For that reason, ADVs can not be calibrated to
characterize the particles in suspension and rely on the rule of thumb levels set out above. The
assumption is made here that the characteristics of the particles observed by the instrument are
similar to those of Ciochetto (2007).
High levels of turbulence in laboratory settings have been shown to cause de-correlation
in ADV measurements (Lohrmann, Cabrera, and Kraus 1994). In these situations, the lateral
motion of the particles and motion into and out of the sample volume in the time between pulses
cause significant differences in the echoes received reducing the pulse-pair correlation. In this
case, the correlation would be independent of the SNR. Comparing Figs. 5 and 7 and in the
summary shown in Fig. 9, it is obvious that this is not the case. For this data, correlation is a
strong function of SNR or signal strength. Thus high levels of turbulence are ruled out as a cause
of poor data quality. Other data quality issues may be considered but are not included in this
report as they are deemed secondary causes of loss of signal quality.
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Without a measure of particles in the water column, the particle hypotheses presented
here can not be investigated further. There is no evidence at this time to support differences in
particle composition or number. In fact, as the measurements were taken closer to the bottom
boundary, one would expect a greater particle load with particles of a greater density and
compressibility contrast to water, i.e. more acoustically friendly. If the assumption is made that
the particles and suspension due to forcing are similar to Ciochetto (2007), the remaining cause
for difference in data quality is the smaller sampling volume. As the instrument employs various
internal signal processing techniques when the data are taken at sampling rates < 64 Hz, only a
lab experiment with similar particles can determine the difference.
The final difference between the experiments is the fact that different probes were used.
The probe used in Ciochetto (2007) was hand picked for applications in low particle
environments by the owner of Nortek and co-developer of the ADV class of instrument. If we
assume that manufacturing quality differences are small, then the only conclusion that can be
made with regard to the differences between these two experiments was due to the difference in
the sampling volume of 22%.
3.2 Temporal variability
The temporal data represented in Figs. 5 – 7 present a mystery. Apart from the variability
in each burst of data seen throughout the record or the strong spikes of high quality data, the
most striking feature is the significant increase in data quality near 15 May 2007 in each figure.
The RALPH frame was seated on the seabed and did not move perceptibly during the
experiment. There are a few reasons why this pattern may have occurred. The observed change
does not result from any instrument failure or settings change. The only remaining change can
come from the suspended particles. The particles may have several sources from either the lateral
or vertical directions. Lateral sources could include turbidity currents or advection of water
masses. Vertical sources may include re-suspension due to bottom shear and vertical particle flux
due to detritus, atmospheric dust or vertical migration of plankton.
Lateral movement of particles would be accompanied by a change in the water mass
properties. CTD data were not available for this analysis and characterization of the water mass
was not attempted.
The vertical sources include re-suspension from the bottom, particle ‘rain’ from detritus
and Aeolian input or biological migration. The spring bloom was observed in the Northwest Arm
of Halifax Harbor on 8 March and terrestrial sources caused an echo bloom on 3 April. The
spring bloom on the Scotian Shelf typically consists of diatoms which have a silicious shell.
Diatoms are on the order of 10 – 50 μm in diameter. They may provide adequate particles to an
ADV. The characteristics of the spring bloom for this year over the NE Channel in the Gulf of
Maine are unknown to the author. If the timing were similar to Halifax Harbor then by 15 May
one may not expect to see particles at 178 m. The end of the spring bloom is mitigated by
zooplankton grazing as well as loss of nutrients from consumption. The quantity of diatoms
required to raise the SNR from 5 to 10 is unknown. If they are large diatoms, it is possible that
one test per pulse-pair in the 2.8 x 10-6 m3 sample volume could produce good signal. Fallout
from the spring bloom should show the opposite trend than that observed in Figs. 5 – 7, the
bloom snow would be expected to arrive earlier at 178 m and be falling off rather than ramping
up. Vertical migration by plankton of a size favorable to the ADV is rare. Zooplankton have
effective spherical diameters > 100 μm and smaller marine particles don’t migrate that deep.
Migrations are typically on a diel cycle contrary to the change observed in Figs. 5 – 7. The other
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vertical source of particles is due to shear from tidal currents. Depending on the strength of the
flow, various sizes of sediment are re-suspended. Comparison of Figs. 4a and 7 indicate that the
spring-neap cycle of the tides at this location may be the main forcing of the SNR signal. The
SNR starts low as the tidal elevations are increasing in magnitude with the spring cycle and then
slowly decrease as the neap tide is approached. The SNR data appear to lag the tidal elevation
spring-neap cycle by 2.5 days. The more energetic tides would produce shear that can re-suspend
larger particles at the height of the ADV. Note that the correlation remains high throughout the
later part of the data. It is hypothesized that a few smaller than optimal particles of sufficient
acoustic properties will generate a good correlation with a low SNR.
A competing hypothesis was the result of previous work by the author where high
correlation and low amplitude data results from a cloud of small particles with acoustically
undesirable properties scattering sound not unlike light scattering from fog. The present data are
not able to test these hypotheses. Based on the comparison of Figs. 4a and 7, it is strongly
suggested by this data that the tides are the strongest source of particles during the experiment
through sediment re-suspension. The data record nearly encompasses one full spring-neap cycle.
A stronger case for this conclusion could be made if the data record were at least 1.5 – 2 springneap cycles.
Analysis of the velocity lends support to the above conclusions. A comparison of the
ADV data that pass the > 70% correlation filter weakly support the tides as the main source for
scatterers as illustrated in the joint histograms in Fig.s 10 and 11. SNR as a function of velocity
was interrogated. It was discovered that no relationship existed between SNR and |uADV|, uADV
(Eastward velocity), |uWebTide|, uWebTide or vWebTide (Northward velocity). However a discernable
rise in SNR is indicated when compared to vADV (Fig. 10). SNR is stronger with more “good”
data when the Southward velocity is higher. The joint-histogram of SNR and vADV (Fig. 10)
approximately follows a parabolic shape where the mean of the lowest values is near a SNR = 8
for vADV = 0 m s-1 and gets greater as the measured velocities approach + 0.2 m s-1. Both extreme
velocities illustrate a rise but the rise in SNR for southward velocity was more prominent.
Considering the bathymetry in Fig. 1, flow along the topography, which is mostly North-South at
the measurement location would be expected. Flow mediated by the tides and including a
Southward discharge from the continental shelf to the continental rise would account for the
enhanced performance with the Southward flow. The Southward discharge may be transporting
particles offshore with it. This evidence supports the hypothesis of scatterer supply due to bottom
re-suspension by tidal and current friction.
Figure 11 illustrates SNR as a function of the ADV pressure signal. The pressure data is
presented as depth – mean depth for the deployment in meters. The color bar is limited to a count
of 200 bin-1 to enhance the contrast. Figure 11 does not indicate an increase in SNR with any
depth (phase of the tide). However it does indicate that for depths ranging from -3 m to 3 m that
the SNR is of higher quality. The dark patches in the joint-histogram indicate regions where
more data are retained as good either due to passing the correlation filter or due to a peaky
SNR d − d i histogram at that ith location. Considering Fig.s 4a and 7, the dark spots could
indicate that the SNR was simply greater during the neap cycle of the tide beginning near 21
May. The maximum tidal excursion during this time was on the order of + 0.5 m (Fig. 4a) and
SNR was lower near a mean of 8 (Fig. 7). Both of these effects are not reflected in Fig. 11.
Figure 7 indicates shorter time scale fluctuations in SNR which appear to be on a time scale
shorter than diurnal variability. It is hypothesized that this variability is the significant
contributor to the pattern seen in Fig. 11. If the SNR is enhanced for a particular phase of the
(
)
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tide, then an increase in SNR would be expected at a particular elevation corresponding to that
phase. The semi-diurnal nature of the tides with the spring-neap variability would produce
variability in that elevation. Repeated cycles would enhance the signal at semi-discrete
elevations. These arguments with respect to Fig. 11 lend support to the hypothesis that the main
source of scattering material is tidal re-suspension of sediment leading to enhanced signal
quality.
The prediction from Web Tide was averaged to the ADV burst periods and the mean
SNR in a burst was compared to these elevations. The results are similar to Fig. 11. They
indicate a weak trend that the SNR is greater for d − d < 0 when sea level is lower than the
mean. The trend may be due to the location of the station with respect to a tidal amphidrome.
The magnitude of the difference ranges from a SNR of 7 for the high elevation to 9 for the low
elevation.
(
)
3.3 Overall performance
The data are generally filtered based on correlation and signal strength or SNR. Figure 9
summarizes these data for the entire experiment. The rule of thumb limits are indicated with a
line for both SNR of 5 and 10 since there does not appear to be general consensus on this limit.
In this figure, good data fall in the upper right hand quadrant, data on the lower half are
discarded in primary correlation filtering and data in the upper left quadrant have suspect signal
strength. The data from this experiment are seen to primarily lie in the good correlation but
questionable signal strength region with SNR range from 8 to 9 and correlation from 80% to
82%. Correlation is seen to be a function of signal strength with the correlation decreasing as
SNR decreases. The function, for these particles and state of turbulence, pass through the 70%
correlation limit at a SNR just above 5. If correlation is trusted as the only indicator of poor data
quality, this experiment suggests that the mean functional dependence supports SNR as low as
5.5. This is simply suggested by the correlation. To verify this result, tests with similar particles
would be required in a controlled facility where the velocity can be verified against another
source. Figure 9 indicates that most of the data in this experiment fall into the category of good
data quality with weak signal strength.
The desired data product from an ADV is velocity. The question that is asked in this
analysis is do we trust the velocity measurements. Without calibration with the exact particles in
a controlled manner, this is a very difficult question. The Web Tide prediction can be used as a
comparison, however it is not a calibration. Included in the ADV velocity signal are motions on
time scales that differ from that of the tides. This question can not be answered with significance
from the data available from this report. Thus users of ADVs in the field must learn how to trust
the data quality parameters in the results, the correlation and SNR. This analysis and report is a
step toward that goal.
Overall, based on the data quality analysis presented here, the ADV data from this
experiment appear reliable enough to estimate the mean velocity. A sampling rate of 2 Hz is not
deemed sufficient to resolve the scales of turbulence that may be expected. If the data are
trimmed with respect to poor correlation, they should represent the mean velocity. Further
averaging may be required as analysis dictates.
Any repeat of this experiment in similar conditions should double check that the
sampling volume is as large as possible, currently 18.0 mm in length. The sampling rate of 2 Hz
may be increased but that will depend on data storage capacity and deployment length. It would
be interesting to deploy the instrument for more than one spring-neap cycle to test the hypothesis
9
that the SNR follows this cycle indicating that the primary forcing of suspended sediment in the
bottom boundary layer at this location is the envelope of the tide signal rather than the diurnal
tidal forcing.
4. Conclusions
This report documents Nortek Vector ADV data taken on the continental shelf and
discusses the data with respect to its quality. The following conclusions were arrived at in this
analysis.
· Data obtained in this deployment should be adequate to estimate the mean velocity.
· Correlation was found to be a function of SNR.
· Data with SNR > 8 produce acceptable correlation in this instrument with the particles
encountered.
· The difference in data quality from Ciochetto (2007) is due to the smaller sampling volume and
that with a larger sampling volume, the data quality would be significantly increased.
· The main source of particles was re-suspension of benthic particles due to the tide.
· The temporal behavior of the SNR followed the tidal Spring-Neap variation.
Acknowledgments
This work was accomplished in collaboration with Angus Robertson of the Bedford Institute of
Oceanography (BIO) and Eric Siegel of Nortek USA. Funding was not provided for the work by the author but
contributions in kind toward the instrument were made by both collaborators and Nortek AS. This data and work
will contribute to a J. Atm. And Ocean. Tech. paper on the use of the ADV instrument in acoustically unfriendly
environments. Even though the manufacturer of the instrument have collaborated and contributed, the author attests
that the results presented here are unbiased in any manner and reflect on the true performance of the instrument. The
author would like to thank Angus Robertson, Eric Siegel, Dr. Barry Ruddick (Dalhousie University, DAL) for
permission to include the instrument in the experiment and for resources to accomplish this work, Dr. Michael Li
(BIO), Dr. Dan Kelley (DAL) for data interpretation consultation, Audrey Barnett (DAL) for financial support and
to the DFO crews of the vessels that launched and recovered the RALPH frame. This report will be included with
comments and corrections as an appendix in the author’s dissertation. All feedback is appreciated and can be sent to
david.ciochetto@dal.ca
10
Figures
Fig. 1. Location of the deployment (square) generated by Web Tide.
11
Fig. 2. Sound speed (calculated) and temperature (recorded).
150
Histograms of Noise Amplitude [counts]
150
150
100
100
100
50
50
50
0
0
47 48
0
47 48
Histograms of Noise Correlation [%]
40
40
40
30
30
30
20
20
20
10
10
10
0
0
46 47 48
5
10
Beam 1
15
0
0
5
10
Beam 2
15
0
0
5
10
Beam 3
Fig. 3. Noise characteristics for the ADV shelf deployment in May 2007.
12
15
Fig. 4a. Depth from ADV pressure signal (dots) and Web Tide prediction for tidal elevation
(line) at (42.05313o, -65.96678o) as indicated in Fig. 1. The tidal prediction is shifted to the mean
depth from the ADV pressure sensor of 172.5 m.
Fig. 4b. Detail of tidal elevation comparison with ADV pressure data.
13
Fig. 5. Correlation as a function of time represented by the plan view of a two dimensional
histogram. The time is binned into 1-minute bins. The data are taken at 2 Hz. For each segment
of time, the data from all three beams are combined and then binned at the correlation bit
resolution. The color bar indicates the number of samples that fall into each bin.
Fig. 6. Summary of the data that pass the correlation filter as a function of burst.
14
Fig. 7. SNR v. time represented by a two-dimensional histogram with 0.43 dB bin resolution in
SNR and 1 minute bin resolution in time. Only data with correlation > 70% is shown. The color
bar indicates the number of samples that fall in a bin.
Fig. 8. ADV velocity data and Web Tide barotropic tidal velocity prediction. The data presented
have passed the correlation > 70% filter.
15
Fig. 8 (cont.). Detail view of velocity comparison.
90
3500
80
3000
Correlation [%]
70
2500
60
2000
50
40
1500
30
1000
20
500
10
5
10
15
SNR
20
25
Fig. 9. Correlation as a function of SNR represented by a two-dimensional histogram. The color
bar represents the number of samples in a respective bin.
16
Fig. 10. SNR as a function of Northward Velocity. Gaps in the axes appear where data exist.
Fig. 11. SNR as a function of surface elevation.
17
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