NOAA TECHNICAL MEMORANDUM ERL PMEL-111

USING ECHO INTENSITY TO CORRECT MOORED ADCP DATA FOR FISH-BIAS

ERRORS AT 0(, 170(W

P.E. Plimpton1

H.P. Freitag1

M.J. McPhaden1

R.H. Weisberg2

1

 Pacific Marine Environmental Laboratory

 7600 Sand Point Way N.E.

 Seattle, WA 98115-0070

2

 Department of Marine Science

 University of South Florida

 140 7th Ave. S.

 St. Petersburg, FL 33701-5016

August 1997

Contribution No. 1843 from NOAA/Pacific Marine Environmental Laboratory
ii

NOTICE

Mention of a commercial company or product does not constitute an endorsement by

NOAA/ERL. Use of information from this publication concerning proprietary

products or the tests of such products for publicity or advertising purposes is not

authorized.

Contribution No. 1843 from NOAA/Pacific Marine Environmental Laboratory

__________________________________________________

For sale by the National Technical Information Service, 5285 Port Royal Road

Springfield, VA 22161
iii

CONTENTS

Page

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

2. Fish Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

3. Method for Postprocessing Rejection of Fish-Biased Data Using Echo Intensity . . . . . . . . 4 

4. Rejection of Fish-Biased Data at 170(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 

FIGURES

1. Echo-intensity range for deployment PR11: 0(,140(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

2. ADCP - MCM speed differences for deployment PR11: 0(,140(W . . . . . . . . . . . . . . . . . . . 7 

3. Echo-intensity range for deployment PR07: 0(,140(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

4. ADCP - MCM speed differences for deployment PR07: 0(,140(W . . . . . . . . . . . . . . . . . . . 9 

5. Echo-intensity range for deployment RTU1: 0(,170(W . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

6. Contours of the percentage of values rejected during data acquisition 

for deployment RTU1: 0(,170(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

7. Original and screened speeds for deployment RTU1: 0(,170(W . . . . . . . . . . . . . . . . . . . . 13 

8. Contours of zonal and meridional velocities after echo intensity screening

for deployment RTU1: 0(,170(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

9. Contours of the difference between the original and screened velocities 

for deployment RTU1: 0(,170(W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 
1

NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115-0070

2

Department of Marine Science, University of South Florida, 140 7th Ave. S, St. Petersburg, FL 33701-5016

Using Echo Intensity to Correct Moored ADCP Data

for Fish-Bias Errors at 0(, 170(W

P.E. Plimpton1, H.P. Freitag1, M.J. McPhaden1, and R.H. Weisberg2

ABSTRACT. Acoustic Doppler current profilers (ADCPs) have been deployed on both subsurface and

taut-line surface moorings to measure upper ocean currents in the equatorial Pacific. The moored ADCP

velocity measurements are included as part of the data base from the Tropical Atmosphere Ocean (TAO)

array, which provides measurements of upper ocean and atmospheric variability in support of climate

studies. Surface moorings tend to attract pelagic fish, which sometimes school around the mooring and bias

the ADCP velocity measurements. Therefore the fish-bias velocity errors, which have been as large as

80 cm s–1 in the surface moored ADCP data, must be eliminated as much as possible from the ADCP

velocity time series. In situ mechanical current meter (MCM) data have been used to correct the fish-bias

velocity errors in the surface moored ADCP data at 0(, 110(W and 0(, 140(W. The equatorial ADCP

mooring at 170(W has been deployed as a subsurface mooring since 1988, except for a 1-year deployment

as a surface mooring beginning in March 1993. No MCM velocity data are available to correct these surfacemoored ADCP velocities for fish-bias errors. In this study, a procedure is developed to reject fish-biased

velocity data in postprocessing based on the ADCP echo-intensity measurement. This method significantly

improves the accuracy of the velocity data, but at times so much of the hourly data is rejected that a daily

averaged velocity could not be computed. 

1. Introduction

As part of the Tropical Ocean-Global Atmosphere (TOGA) program, an array of moorings was

established in the tropical Pacific to measure upper ocean velocity, temperature, and surface winds.

These measurements provide long time series on upper ocean and atmospheric variability for

analysis of short-term climate variations, particularly those relating to the large-scale interaction of

the atmosphere and ocean. Presently, approximately 70 wind and thermistor chain moorings are

included in the Tropical Atmosphere Ocean (TAO) array, providing data from the tropical Pacific

in near real time via satellite (Hayes et al., 1991; McPhaden, 1993, 1995). Near-surface currents are

measured at five locations on the equator, either by ADCPs (acoustic Doppler current profilers) or

MCMs (mechanical current meters). 

In May 1988 a subsurface RD Instruments’ ADCP, for measuring upper ocean currents, was

deployed in the vicinity of an ATLAS mooring measuring temperature and winds at 0(, 170(W.

This subsurface mooring was maintained until March 1993. At this time a PROTEUS mooring

(McPhaden et al., 1991) was deployed to facilitate real-time transmission of the velocity data via

satellite. PROTEUS consists of an ADCP mounted in a downward-looking position on a taut-line

surface mooring. Unfortunately, significant errors were apparent in the velocity data due to acoustic

reflections from fish which school around surface moorings. In March 1994, the surface mooring

was recovered and a subsurface mooring deployed in its place. Although a school of fish may, on
2

occasion, swim though the acoustic beam of a subsurface mooring, these moorings do not attract fish

and the ADCP data from subsurface moorings are essentially free of fish-bias errors. 

Freitag et al. (1992) and Plimpton et al. (1995, 1997) also found fish-bias errors in ADCP

velocities from equatorial PROTEUS moorings at 110(W and 140(W. Since these data were

collected concurrent with MCM velocity measurements at six or seven depths, extensive analysis

of the fish-biased measurements was possible. As a result, RD Instruments created a fish-rejection

algorithm in an attempt to detect and reject fish-biased data on an individual ping basis before

ensemble averaging. However, large fish-bias errors remained even after installation of the algorithm

in March 1992. Thus, the TAO project phased out PROTEUS moorings, and since March 1995 all

ADCPs in the TAO array have been deployed on subsurface moorings.

Significant fish-bias errors most likely occur in the data from the 0(, 170(W PROTEUS

mooring. However, no in situ velocity data are available for the detection or correction of errors in

these velocities. Evaluation of the ADCP measurements of percent good and echo intensity indicates

the presence of fish in the 170(W data. Thus, a procedure is presented in this report to reject fishbiased velocity data in postprocessing using only data collected by the ADCP.

2. Fish Bias

Downward-looking, 153.6 kHz, RD Instruments ADCPs were deployed on equatorial surface

moorings with the acoustic beams angled 30( from vertical. Data were collected with 8-m bin and

pulse lengths, at a 1-second sample rate for 6 minutes once per hour. The ADCP transmits an

acoustic signal, determines time-gated Doppler shifts along each of the four beams, and then

computes beam-direction velocities as a function of range. The beam velocities are converted to

Earth coordinates using beam geometry and direction from a KVH flux gate compass. The frequency

shift in the return signal is caused by the relative motion of oceanic scatterers with respect to the

ADCP transducers. For measurement of ocean currents, it is assumed that the movement of the

scatterers is due, on the average, to oceanic advection. 

In the absence of fish, the most significant ADCP velocity errors in the equatorial Pacific are

due to skew errors from misposition of the ADCP tracking filter. The magnitude of the skew error

depends on the strength and duration of significant horizontal velocity gradients and the ADCP setup

parameters (Chereskin and Harding, 1993). Beginning in fall 1991, filter skew error has been

minimized in the PROTEUS moorings by use of the 600-Hz low-pass filter bandwidth in the higher

shear portions of the water column. Even for extreme conditions where the horizontal velocity shear

approaches 0.1 s–1 for part of the water column, the filter skew error is less than 4 cm s–1. For more

typical equatorial velocity shears (less than .04 s–1) the skew error is less than 2 cm s–1. In contrast,

the standard deviation due to random error for a PROTEUS ensemble average over 360 pings is

0.7 cm s–1.

Pelagic fish are at times attracted to the vicinity of surface moorings. Since their mean

movement is not due to advection by currents, their presence in the acoustic beam will bias the
3

velocity measurements. For example, if the echo intensity of a fish is not much greater than that of

the surrounding water, the fish will be detected in one ADCP beam but not the opposing beam. In

this case, the measured horizontal velocity from the two beams would be equal to the average of the

true ocean current measured in one beam and the measurement from the second beam where the

ocean current velocity was biased by the fish velocity. The velocity of fish schooling around a

mooring would, in the mean, be small. Thus, the signals from scatterers advected by the ocean

currents would be averaged with the acoustic reflections from the fish, and the ensemble averaged

measurement of non-zero horizontal velocities would be biased low. Alternately, since the scattering

intensity from a fish can be much greater than the surrounding water, the same fish could be detected

in the side lobes of the opposing and neighboring beams. The beam side lobes have varying relative

amplitudes, with the largest side lobe down about 35 dB from the beam’s main lobe. In this situation,

the fish echo intensity would be larger than the scattering signal from the surrounding water in all

four beams. The horizontal velocity would then tend toward zero because the fish dominant signals

would tend to cancel in the computation of horizontal velocity. 

In an attempt to eliminate fish-biased data during data acquisition on an individual ping basis

before ensemble averaging, the RDI ADCP was equipped with a fish-rejection algorithm. This

algorithm compares the echo intensity, which is an indicator of the intensity of the backscattered

acoustic signal, for each of the four beams for each bin. When the echo intensity of a fish in one

beam is greater than the surrounding water, then the echo intensity seen in that beam would be

greater than detected in the other beams. Thus, the algorithm computes the echo intensity range

(EIR) for each bin as the difference between the highest beam echo intensity and the lowest beam

echo intensity. If the EIR exceeds a preset level, then the EIR is recomputed as the difference

between the highest beam echo intensity and the second lowest beam echo intensity. If the

recomputed EIR is also larger than the preset value, then the velocity data for the bin is set bad. The

two-step process was included in order to ensure that the fish-rejection algorithm would not flag all

data as bad in the event of one beam failing. The equatorial deployments used RDI command CF22

for fish rejection which rejected velocity data when the single-ping EIR was greater than 20 dB. 

Freitag et al. (1993) and Plimpton et al. (1995) found that the fish-rejection algorithm was

inadequate to eliminate the fish-biased velocity data from the surface-moored deployments. Many

equatorial deployments at 110(W and 140(W still contained large (order 80 cm s–1) fish-bias

velocity errors even after the RDI algorithm rejected data with EIRs greater than 20 dB. A lower CF

value would have removed more of the fish-affected data. However, Freitag et al. (1992) and

Plimpton et al. (1997) have shown that too low a value for the EIR threshold will result in rejection

of good data at depths below the fish-affected areas. In addition to the difficulty in setting the EIR

threshold, the fish-rejection algorithm will only work if fish are detected in only one or two of the

beams. Often all four beams have an elevated echo intensity because of the presence of fish as

discussed above.
4

Since the surface moored ADCP data at 110(W and 140(W contained significant errors due

to fish bias, the horizontal velocities at these sites were corrected in postprocessing using an EOF

correction scheme based on in situ mechanical current meter velocity measurements (Plimpton et

al., 1995). The accuracy of these corrections was limited by the velocity errors in the MCM

measurements, which are slightly larger than the fish-free ADCP data. One source of MCM velocity

error is high frequency noise induced by mooring motion and surface waves. EG&G Model 630

Vector Measuring Current Meters (VMCMs, deployed at 10 m on TAO moorings discussed here)

are relatively more effective at high frequency noise reduction than EG&G Model 610 Vector

Averaging Current Meters (VACMs, used primarily at 25 m and below). Mean VACM/VMCM

speed differences between instruments located at 13 m and 14 m on an equatorial taut-line mooring

were 7.4 cm s–1, with the VACM exceeding the VMCM mean by 12% (Halpern, 1987). At deeper

depths (100 m, 120 m, 160 m) where direct surface wave influence is less energetic, mean

VACM/VMCM speed differences ranged between 3.5 cm s–1 and 4.0 cm s–1). Thus, we cannot

specify the accuracy of ADCP data corrected for fish bias using algorithms based on or validated by

VACM and VMCM data to better than about 5 cm s–1.

3. Method for Postprocessing Rejection of Fish-Biased Data Using Echo

Intensity

No MCM velocity data were available for comparison with the 170(W surface moored ADCP

data. However, the failure of the fish-rejection algorithm to remove fish-biased data at 110(W and

140(W indicates that significant velocity errors probably exist in the 170(W data. Therefore, a

method was developed to identify and reject fish-biased hourly ensembles in postprocessing using

criteria based on the echo-intensity values. 

During data acquisition, only the velocity values were rejected when the EIR values were

greater than 20 dB for an individual ping. In contrast, the echo-intensity values for every ping were

retained and included in the ensemble average. Thus the ensemble-averaged echo-intensity range is

useful in identifying the periods when fish were present. 

EIR was used for the RDI fish-rejection algorithm instead of echo intensity due to the

difficulty in predicting echo-intensity values for the different temperatures and biomass

concentrations found at various deployment locations. On the other hand, although the temperature

and biomass would be expected to change somewhat during the duration of a deployment, after

recovery a reasonable expected value for echo intensity can be determined from echo-intensity

values recorded for a deployment during periods when the presence of fish was minimal. These

echo-intensity averages can therefore be used to identify and reject fish-biased ensembles in

postprocessing, in addition to using the ensemble averaged echo intensity range.

To evaluate the effectiveness of using echo intensity for rejecting fish-biased ensembles, a

PROTEUS deployment (PR11) at 140(W from 28 April 1993 to 11 October 1993 was examined.

The echo-intensity range for PR11 (Fig. 1) indicates minimal evidence of fish in the first 2 months
Fig. 1. ADCP echo-intensity range from 28 April to 11 October 1993 at 0(, 140(W. 

5
6

of the deployment. Therefore, the data from 28 April to 28 June 1993 were used to compute mean

echo-intensity values not biased by fish. A mean echo intensity was computed for each of the 24

hours in a day for each depth bin to eliminate effects of the diurnal cycle. Testing and comparison

with mechanical current meters indicated that eliminating hourly data with echo-intensity values that

exceed the diurnal mean echo intensity plus 4 dB substantially removed the fish-biased velocities.

After this screening, individual bin data were set bad if the data both shallower and deeper were bad,

or for bin 1, if bin 2 data were bad. ADCP daily averages were then computed, for which three or

more hourly values were required for a good daily value. 

Figure 2 shows the mean daily speeds for the mechanical current meters and the ADCP data

both before and after the ADCP fish-bias screening. The upper and lower panels show the mean

speeds for the first half (28 April to 19 July 1993) and the second half (20 July to 11 October 1993)

of the PR11 deployment, respectively. In the first half of the deployment, where fish bias was

minimal, there was little change in the ADCP speeds due to the screening. The mean speed

difference after screening changed by less than 2 cm s–1 and there was little change in the standard

deviation of the speed differences. In the second half of the deployment, ADCP minus MCM speed

differences after screening were reduced by as much as 22 cm s–1 and the standard deviation of the

speed differences did not exceed 8.5 cm s–1, compared to 10–20 cm s–1 before screening. However,

during this period, where there was considerable evidence of fish, significant loss of daily averaged

data occurred with screening in the shallower depths. As many as 60 out of 84 days did not have the

three good hourly values required to compute a daily average at the depths most affected by fish. 

The fish-bias screening technique was also tested on PROTEUS deployment PR07 at 140(W

from 1 May to 12 September 1992. From the echo intensity range (Fig. 3), there appears to be

minimal presence of fish in the first third of the deployment, 1 May to 24 June 1992. The second

third of the deployment, 25 June to 30 July 1992, had some periods of significant fish presence.

Evidence of fish was significant for all of the last third of the deployment, 1 August to 12 September

1992. The mean diurnal echo intensity for the PR07 deployment was computed from the data in the

first third of the deployment. Fish-biased velocities were then rejected if the echo intensity exceeded

the mean diurnal echo intensity plus 4 dB. Means were computed from the daily speed values before

and after screening and are shown in Fig. 4 for the three periods of the deployment. The first third

of the deployment, which had minimal evidence of fish, shows little change after the screening. In

the second third of the deployment, both the ADCP minus MCM speed differences and the standard

deviation of the speed differences were reduced. During the last part of the deployment, where fish

bias was most significant, the screening reduced the mean speed differences from –38, –41, and –32

cm s–1 to –18, –15, and –6 cm s–1 at 10 m, 25 m, and 45 m, respectively. Although the screening

provided significant improvement, it appears that for severely biased data the screening technique

may not completely reject bad data in the first two ADCP depth bins at 14 and 22 m. However, so

much data were rejected in the last third of the deployment, that fewer than 13 ADCP-MCM

comparisons could be made for the screened data at the shallowest three depths.
7

Fig. 2. Averages of daily speeds measured at 0(, 140(W by a surface moored ADCP and by MCMs from 28 April to

19 July 1993 and from 20 July to 11 October 1993. (a) Mean MCM speeds (x) and mean ADCP speeds before

screening (solid line); (b) mean MCM speeds () and mean ADCP speeds after screening (dashed line); (c) mean

ADCP minus MCM speed difference before screening (x) and mean ADCP minus MCM speed difference after

screening (); (d) standard deviation of speed differences before screening (x) and after screening (); (e)

number of ADCP daily averaged speeds (requiring at least three hourly values) before screening (solid line) and

after screening (dashed line). 
Fig. 3. ADCP echo intensity range from 1 May to 12 September 1992 at 0(, 140(W. 

8
9

Fig. 4. Averages of daily speeds measured at 0(, 140(W by a surface moored ADCP and by MCMs from 1 May to 24

June 1992, from 25 June to 30 July 1992, and from 1 August to 12 September, 1992. (a) Mean MCM speeds

(x) and mean ADCP speeds before screening (solid line); (b) mean MCM speeds () and mean ADCP speeds

after screening (dashed line); (c) mean ADCP minus MCM speed difference before screening (x) and mean

ADCP minus MCM speed difference after screening (); (d) standard deviation of speed differences before

screening (x) and after screening (); (e) number of ADCP daily averaged speeds (requiring at least three hourly

values) before screening (solid line) and after screening (dashed line).
10

4. Rejection of Fish-Biased Data at 170(W

The echo-intensity range at 170(W for PROTEUS deployment RTU1 from 1 April 1993 to

20 March 1994 (Fig. 5) suggests possible fish bias to depths of 150 m. However, the maximum daily

averaged EIR at 170(W for the RTU1 deployment is 15 dB, compared with maximums of 25 dB and

21 dB at 140(W for PR07 and PR11, respectively. There is no direct correspondence between EIR

and the magnitude of the fish bias, but the smaller EIR values at 170(W suggest that the fish bias

may have been less extreme at 170(W than at 140(W. The ADCP on RTU1 was equipped with the

fish-rejection algorithm set to reject velocity data during acquisition when the EIR was greater than

20 dB. In the upper 150 m (where backscattered signal strengths are high and where fish are likely

to be found) data were most likely rejected by the fish-rejection algorithm (Fig. 6). The percentages

of rejected pings in the second half of the deployment are large (up to 72% for a daily average and

as large as 96% for an hourly ensemble) and correspond to the times of larger EIR values in Fig. 5.

Below 175 m, data were eliminated due to low signal strengths. In equatorial regions, scattering

target strengths tend to be lower at depth than near the surface due to the decrease in biological

activity at depth. More significantly, with increased distance from the transducer, the backscattered

signal strength diminishes due to beam spreading and attenuation. When the strength of the

backscattered frequency signal is too low in comparison with the noise, the frequency shift (and

beam velocity) cannot be determined.

Although the acquisition algorithm rejected significant percentages of fish-biased velocities

(Fig. 6), it is likely that significant velocity errors remain, as was the case at 110(W and 140(W.

Much of this error would likely be due to the failure of the algorithm to reject fish-biased pings when

more than two beams exhibited elevated echo intensities. In order to identify the fish-biased data at

170(W that may not have been rejected by the fish-rejection algorithm, a mean diurnal echo

intensity was computed for the period 1 June to 15 August 1993. From the EIR in Fig. 5, this period

appeared the least affected by the presence of fish during the deployment. The ADCP data were then

flagged bad if the echo intensity exceeded the mean diurnal echo intensity plus 4 dB. After this,

individual bin data were set bad if the data both shallower and deeper were bad, or for bin 1, if bin

2 data were bad. ADCP daily averages were then computed, for which three or more hourly values

were required for a good daily value. 

Figure 7 shows the original and the screened mean daily speeds for the first half (1 April to

24 September 1993) and the second half (25 September 1993 to 20 March 1994) of the RTU1

deployment. In the first half of the deployment, where there was minimal evidence of fish, the

screening resulted in very little change in the mean speeds and all daily averages were computed.

In the second half of the deployment, the mean speed of the unscreened data clearly indicates the fish

bias towards smaller velocities when compared with the screened speeds. As a result of the

screening, as many as 74 out of 177 daily averages, requiring three hourly values, could not be

computed in the upper 40 m. For the periods when a screened daily value could be computed, the

largest time-averaged difference between original and screened daily speeds was at 22 m and was
Fig. 5. ADCP echo intensity range from 1 April 1993 to 20 March 1994 at 0(, 170(W. 

11
Fig. 6. Percentage of ADCP velocity values rejected during data acquisition at 0(, 170(W. Velocities below 175 m were rejected due to 

Shallower velocities were most likely rejected by the fish-rejection algorithm which rejected pings with EIRs greater than 20 

12
13

Fig. 7. Averages of ADCP daily speeds measured at 0(, 170(W from 1 April to 24 September 1993 and from 25

September 1993 to 20 March 1994. (a) Mean ADCP speeds before screening (solid line) and after screening

(dashed line); (b) time averaged difference between original daily speeds and screened daily speeds; (c) standard

deviation of the original minus screened daily speeds; (d) number of ADCP daily averaged speeds (requiring

at least three hourly values) before screening (solid line) and after screening (dashed line). 
14

equal to 13 cm s–1 for the second half of the deployment. The maximum standard deviation of the

difference between the original and unscreened speeds was 8.5 cm s–1, also occurring at 22 m for the

second half of the deployment. In contrast, the standard deviation of the speed difference for the first

half of the deployment did not exceed 1.5 cm s–1 for any depth. 

Figure 8 shows the zonal and meridional velocities for the RTU1 deployment after

postprocessing rejection of fish-biased data. The daily differences between the original and the

screened zonal and meridional velocities are shown in Fig. 9. For zonal velocity, the westward

currents in the upper 75 m were biased low in the unscreened data, with corrections up to 46 cm s–1

after rejecting the fish-biased hourly ensembles. Corrections up to 16 cm s–1 are evident between 100

and 150 m, the depth of the eastward flowing Equatorial Undercurrent. For meridional velocity,

Fig. 9 shows northward velocity corrections up to 17 cm s–1 and southward velocity corrections up

to 14 cm s–1. Data are missing where a daily average, with at least three good hourly ensembles,

could not be computed. The areas where a screened daily average could not be computed would

likely have had even larger fish-bias errors if compared with the actual currents. 

5. Conclusions

Velocity data from ADCPs mounted on equatorial surface moorings were biased by acoustic

reflections from fish schooling around the moorings. The ADCP fish-rejection algorithm, which

rejected fish-affected data based on the difference between the echo intensities of the four beams,

was effective in rejecting significant percentages of biased data during data acquisition. However,

large errors in the ADCP velocities still remained. The failure of the algorithm to reject all the biased

data was partly due to the difficulty in setting the rejection threshold for the algorithm, but more

significantly, due to the cases where the echo intensity was elevated in more than two beams. Data

from mechanical current meters, set at six or seven depths, have been used in postprocessing to

correct the ADCP fish-biased errors in the equatorial data at 110(W and 140(W (Plimpton et al.,

1995). Comparisons with the MCM velocities were also used to develop and evaluate a procedure

to reject fish-biased velocities in postprocessing, when MCM data are unavailable, using only the

ADCP echo-intensity data. For this procedure, a mean diurnal echo intensity for each depth bin was

computed from a time period in the deployment that had only minimal evidence of fish. Hourly

ensembles with significant fish bias were then identified and rejected if the echo intensity was 4 dB

greater than the computed mean diurnal echo intensity. The procedure was tested on PROTEUS

ADCP velocities at 140(W for two different deployments where it significantly reduced ADCP

velocity errors, decreasing ADCP-MCM daily speed differences an average of 65% to 82% at depths

most affected by fish bias.

The echo intensity was also used to reject fish-biased velocities in postprocessing at 170(W,

where no MCM data were available. At times, however, so many of the hourly ensembles were

rejected that a daily average could not be computed at some depths in the upper 75 m. For the

available daily averaged data, comparison of the speeds from before and after the echo-intensity
Fig. 8. Contours of zonal and meridional daily averaged velocities at 0(, 170(W after the hourly fish-biased velocities were rejected by th

technique. During certain periods, less than three good hourly values remained and a daily velocity value could not be compu

15
Fig. 9. Contours of the difference between the original and screened ADCP zonal and meridional velocities at 0(, 170(W. The westward 

current in the upper 75 meters was biased toward zero in the original data. In the screened data the westward velocities werresulting in large (up to 46 cm s–1) original minus screened zonal velocity differences. 

16
17

screening showed significant improvement in the ADCP velocities. For example, for the second half

of the RTU1 deployment at 170(W, the time averaged screened speeds were 13 cm s–1 greater than

the unscreened speeds at 22 m depth, indicating that fish-biased ensembles were rejected by the

screening technique. The screening resulted in even larger differences at 140(W, where the screened

speeds were greater than the unscreened speeds at 22 m by 22 cm s–1 for the second half of PR11 and

by 26 cm s–1 for the last third of PR07. The smaller speed difference coupled with smaller echo

intensity range at 170(W supports the idea, proposed by Freitag et al. (1993), that fish bias is

greatest in the eastern Pacific, at 110(W and 140(W, and decreases to the west such that it is nearly

negligible for most deployments in the western Pacific, at 156(E and 165(E. This suggests a

correlation between fish bias and overall levels of biological productivity which, in the equatorial

Pacific, are related to the depth of the thermocline and nutriciline.

6. References

Chereskin, T.K., and A.J. Harding (1993): Modeling the performance of an acoustic Doppler current

profiler. J. Atmos. Ocean. Tech., 10, 41–63.

Freitag, H.P., M.J. McPhaden, and P.E. Pullen (1992): Fish-induced bias in acoustic Doppler current

profiler data. Proceedings, Oceans ‘92, Mastering the Oceans Through Technology, 26–20

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