NOAA Technical Memorandum OAR PMEL-117

Correcting Moored ADCP Data for Fish-Bias Errors

at 0◦, 110◦W and 0◦, 140◦W from 1993 to 1995

Patricia E. Plimpton, H. Paul Freitag, and Michael J. McPhaden

Pacific Marine Environmental Laboratory

7600 Sand Point Way NE

Seattle, WA 98115

April 2000

Contribution 2104 from NOAA/Pacific Marine Environmental Laboratory
NOTICE

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NOAA/OAR. 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. 2104 from NOAA/Pacific Marine Environmental Laboratory

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

Springfield, VA 22161

ii
Contents iii

Contents

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

2. Instrumentation and Data Processing . . . . . . . . . . . . . 2

3. ADCP Velocity Bias . . . . . . . . . . . . . . . . . . . . . . . 5

4. ADCP Data Correction . . . . . . . . . . . . . . . . . . . . . 6

5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 9

7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

List of Figures

1 Map of mooring locations for the Tropical Atmosphere Ocean (TAO)

array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 S chematic of PROTEUS mooring . . . . . . . . . . . . . . . . . . . . 13

3 Depth and time coverage of mechanical current meter data at 0◦,

110◦W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Depth and time coverage of mechanical current meter data at 0◦,

140◦W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Time series of ADCP-MCM speed difference and echo intensity range

at 0◦, 110◦W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

6 Time series of ADCP-MCM speed difference and echo intensity range

at 0◦, 140◦W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7 Profiles of mean speed and direction differences for PR13 . . . . . . 18

8 Profiles of mean speed and direction differences for PR14 . . . . . . 19

9 Profiles of mean speed and direction differences for PR15 . . . . . . 20

10 Profiles of mean speed and direction differences for PR16 . . . . . . 21

11 Profiles of mean speed and direction differences for PR17 . . . . . . 22

12 Profiles of mean speed and direction differences for PR18 . . . . . . 23

13 Profiles of mean speed and direction differences for PR19 . . . . . . 24

14 Profiles of mean speed and direction differences for PR20 . . . . . . 25

15 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR13 . . . . . . . . . . . . . . . . . . . . . . . . 26

16 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR14 . . . . . . . . . . . . . . . . . . . . . . . . 27

17 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR15 . . . . . . . . . . . . . . . . . . . . . . . . 28

18 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR16 . . . . . . . . . . . . . . . . . . . . . . . . 29

19 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR17 . . . . . . . . . . . . . . . . . . . . . . . . 30

20 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR18 . . . . . . . . . . . . . . . . . . . . . . . . 31

21 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR19 . . . . . . . . . . . . . . . . . . . . . . . . 32

22 The first three EOF eigenvectors and time series of ADCP-MCM

speed differences for PR20 . . . . . . . . . . . . . . . . . . . . . . . . 33

23 Zonal and meridional velocity at 0◦, 110◦W from corrected daily

profiles of ADCP data . . . . . . . . . . . . . . . . . . . . . . . . . . 34
iv Contents

24 Zonal and meridional velocity a 0◦, 140◦W from corrected daily profiles of ADCP data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

List of Tables

1 PROTEUSmooring deployments at 0◦, 110◦W and 0◦, 140◦W. . . . 3

2 Comparison statistics between existing speeds and speeds computed

with multiple linear regression formulae . . . . . . . . . . . . . . . . 4

3 Percent variance corrected for each of the first three vertical modes

and their total for each deployment. . . . . . . . . . . . . . . . . . . 7
Correcting Moored ADCP Data for Fish-Bias Errors at 0◦, 110◦W

and 0◦, 140◦W from 1993 to 1995

Patricia E. Plimpton, H. Paul Freitag, and Michael J. McPhaden

Abstract. Moorings in the Tropical Atmosphere Ocean (TAO) array monitor upper ocean velocity,

temperature, and salinity as well as atmospheric parameters. Beginning in 1990, PROTEUS(PROfile TElemetry of Upper ocean currentS) moorings were equipped with a 153.6-kHz RD Instruments

acoustic Doppler current profiler (ADCP). With this instrument, ocean currents to a depth of 250 m

could be measured and data transmitted to shore in real time via Service Argos. However, due to

the reflection of acoustic energy from fish in the vicinity of surface moorings, large bias errors were

at times found in the ADCP velocities. This report describes the correction of the ADCP velocity

errors at 110◦W and 140◦W from 1993 to 1995 using coincident mechanical current meter (MCM)

data. A method is developed that uses empirical orthogonal functions of ADCP-MCM speed differences to correct the ADCP velocities in post processing. Corrected speeds are expected to be

accurate to about 5 cm s−1. Beginning in August 1995, the ADCPs at these locations were deployed

on subsurface moorings that were free of the fish-bias errors encountered on the surface mooring

deployments.

1. Introduction

The Tropical Atmosphere Ocean (TAO) array consists of nearly 70 moorings (Fig. 1) located in the tropical Pacific (Hayes et al., 1991; McPhaden et

al., 1998). The purpose of the TAO Array is to measure oceanographic and

atmospheric variables in real time in support of El Ni ̃no-Southern Oscillation (ENSO) investigations. In addition to ocean temperature and salinity

measurements, direct ocean current measurements have been made at the

equator where geostrophy does not apply. Early equatorial current measurements were collected with mechanical current meters (MCMs) beginning in

1979 at 110◦W, followed later by long-term MCM measurements at 140◦W

(beginning in 1983) and 165◦E (beginning in 1986).

In 1990, the first PROTEUS(PROfile TElemetry of Upper ocean currentS) mooring (McPhaden et al., 1990) was deployed at 0◦, 140◦W. PROTEUSmoorings were equipped with telemetering RD Instruments (RDI)

acoustic Doppler current profilers (ADCPs) and transmitted real-time ocean

currents within 250 m of the surface (Fig. 2). In 1991 PROTEUSsites were

also established at 110◦W and 165◦E as part of the TAO array. An additional

PROTEUSsite was established at 0◦, 156◦E in 1991 as part of the TOGA

Coupled Ocean Atmosphere Response (COARE) Experiment (Webster and

Lukas, 1992).

However, the accuracy of the surface-mounted ADCP measurements on

the PROTEUSmoorings was degraded due to the presence of pelagic fish,

which were sometimes attracted to the vicinity of the moorings (Freitag

et al., 1992; Plimpton et al., 1997). Reflections of the acoustic signal by

fish resulted in velocity errors in the ADCP data. In an earlier report by

Plimpton et al. (1995), a post-processing technique was developed to correct

the PROTEUSADCP data at 0◦, 110◦W and 0◦, 140◦W from 1990 to 1993.

In a effort to eliminate fish-bias velocity errors during data acquisition,

the authors and RD Instruments (the ADCP manufacturer) developed an
2 Plimpton et al.

algorithm to reject fish-biased velocities on an individual ping basis before

ensemble averaging. All PROTEUSmoorings deployed after March 1992

included this algorithm and a decrease in the percentage of good pings per

ensemble average occurred during periods when fish were present. This indicated that some rejection of fish-biased data was occurring during data

collection, but large velocity errors were still evident. The failure of the

ADCP fish-rejection algorithm to eliminate the fish-bias velocity errors resulted in the conversion of the ADCPs to subsurface moorings without the

ability for real-time data transmission. The authors determined that subsurface moorings are free of fish bias by evaluation of subsurface equatorial

ADCP data at 140◦W from the TIWE experiment (Weisberg et al., 1991).

The 110◦W and 140◦W ADCPs were mounted on surface PROTEUS

moorings until August 1995 and September 1995, respectively. This report

describes the ADCP fish-bias velocity errors and correction at these sites

from 1993 to the end of the surface deployments in 1995. A similar technique

to that presented in Plimpton et al. (1995) is used to correct the ADCP

velocities using the concurrent 1993 to 1995 MCM data. The corrected

ADCP profiles retain the high vertical resolution not available from the

MCM data. No corrections for fish bias have been made to the PROTEUS

ADCP data at 0◦, 156◦E and 0◦, 165◦E. These western Pacific sites were

relatively free of the fish bias errors detected at 0◦, 110◦W and 0◦, 140◦W

(Freitag et al., 1993).

2. Instrumentation and Data Processing

PROTEUSmoorings were generally recovered and redeployed twice a year

(Table 1). The downward-looking 153.6 kHz narrowband ADCPs were set

to collect data at a 1-second sample rate for 6 minutes once per hour. The

bin width and pulse length were set to 8 m, resulting in a velocity measurement that represented a weighted average over 16 m depth, sampled at

8-m-depth intervals. The ADCP velocities were collected assuming a surface

sound speed of 1536 m s−1. Hourly surface sound speeds computed from in

situ temperatures measured at the transducer head and historically averaged surface salinities were used to correct the ADCP velocities (Gordon,

1996). Nominal ADCP bin depths, which assume a constant sound speed

with depth of 1475.1 cm s−1, were adjusted using an average sound speed

profile computed from historical data at a given location. The bin depth

correction, which increased with distance from the transducer, was on average 6.2 m at 110◦W and 7.3 m at 140◦W at a depth of 250 m. The depth

error, resulting from use of average rather than contemporaneous sound velocities, was estimated from the standard deviation of the historical sound

velocities. At a maximum depth of 250 m, the depth errors were less than

1.7 m at 140◦W and 1.3 m at 110◦W. The depth-adjusted ADCP data were

mapped onto a 5-m depth interval to allow precise comparison of the MCM

and ADCP velocities at the MCM depths. The ADCP data at 10 m depth

was estimated by linear extrapolation based on vertical gradients between

bin 1 at 14 m and bin 2 at 22 m.
Correcting Moored ADCP Data for Fish-Bias Errors 3

Table 1: PROTEUSmooring deployments at 0◦, 110◦W and 0◦,

140◦W.

Latitude Longitude Start date End date

140 ◦W Sites

PR13 0◦ 2.1

N 139◦ 59.1W 14 Oct 93 26 May 94

PR15 0◦ 1.8

N 139◦ 58.2W 29 May 94 10 Oct 94

PR170◦ 0.8

N 139◦ 58.7W 13 Oct 94 20 Mar 95

PR19 0◦ 0.3

N 139◦ 56.7W 23 Mar 95 9 Sep 95

110 ◦W Sites

PR14 0◦ 0.7

S 109◦ 59.0W 11 Sep 93 4 May 94

PR16 0◦ 1.2

S 110◦ 1.2W 8 Jun 94 17 Sep 94

PR18 0◦ 0.8

N 110◦ 1.1W 20 Sep 94 22 Feb 95

PR20 0◦ 0.6

N 109◦ 59.6W 25 Feb 95 10 Aug 95

Due to the high shear of equatorial velocities, the ADCPs were set to

minimize skew error (Pullen et al., 1992). All 1993–1995 PROTEUSmooring

ADCPs used a broad bandwidth filter (600 Hz) in the shallower portion

of the depth profile where the greatest shear occurs, switching to a narrow

bandwidth filter at a depth below the core of the Undercurrent. The ADCPs

were equipped with a KVH compass calibrated to an accuracy of ±2.5◦.

The PROTEUSMCMs were either EG&G Vector Averaging Current

Meters (VACMs) or Vector Measuring Current Meters (VMCMs). They

measured velocities at six or seven specific depths, which could be compared

with the ADCP. Small differences occurred in the comparisons due to the

ADCP velocity representing a weighted average over 16 m rather than a

specific depth. Other small differences were probably caused by MCM measurement errors due to mooring motion. PMEL tow tank tests indicated

that VACMs were accurate to within 1.2 cm s−1 in steady flow. In highly

variable flows, VACMs have been found to overestimate velocity (Beardsley, 1987; Karweit, 1974) and VMCMs have been found to underestimate

velocity by a few percent (Weller and Davis, 1980). Halpern (1987) found

good agreement in comparisons of VACM/VMCM pairs separated by 1 m

on taut-line equatorial moorings. The largest RMSdifferences were at 13–

14 m, equal to 7.4 cm s−1 or about 10% of the mean speeds, with smaller

differences found at deeper depths. The VMCMs used a flux gate compass,

similar to the one used in the ADCP, calibrated to an accuracy of ±2.5◦.

During pre-deployment checkout, the VACM mechanical compass linearity

(compass error relative to a chosen fixed direction) was confirmed to be

±5.6◦ or less.

Occasionally the MCM at a specific depth would fail before recovery.

For the ADCP correction analysis described below, coincident speeds at

all six comparison depths were needed. Therefore, the gaps in the MCM

coverage, shown in Figs. 3 and 4, had to be filled. Data gaps of 4 days or

less were filled by interpolation. In a previous report by Plimpton et al.
4 Plimpton et al.

Table 2: Comparison statistics between existing speeds and speeds

computed with multiple linear regression formulae are shown below. Regression fills for MCM data gaps were only used when the

correlation coefficient was .89 or greater. Thus, this method for

computing missing MCM speeds was not used for 10 m, 120 m, or

200 m at 110◦W nor for 80 m at both locations. Statistics are only

shown for depths where gaps occurred in the MCM time series.

Depth Correlation Coefficient

(m) 110◦W 140◦W

10 .86 .93

25 .96

45 .90 .90

80 .73 .83

120 .65

200 .72

(1995), multiple linear regression (MLR) techniques were used to fill longer

gaps in the MCM data at 140◦W. To evaluate the feasibility of using this

technique to fill missing MCM data at 110◦W and 140◦W from 1993 to 1995,

multiple linear regression formulae were computed based on the time series

of speeds at a given depth along with the time series from adjacent depths.

Since no data shallower than 10 m nor deeper than 200 m were collected,

MLR formulae for the 10-m and 200-m time series were computed based

on the 10-m, 25-m, and 45-m time series and the 80-m, 120-m, and 200-m

time series, respectively. These formulae were used to compute a time series

of speeds at each of the depths where gaps occurred and where data from

adjacent depths were available. Statistics derived from a comparison of the

computed speeds at 10 m, 25 m, 45 m, 80 m, 120 m, and 200 m with existing

speeds at these depths are shown in Table 2. In the earlier report mentioned

above (Plimpton et al., 1995), data gaps were filled using MRL formulae

when the correlation coefficient between the computed and existing speeds

was .89 or greater. The same criteria was used for this study and regression

techniques were used to fill 10 m and 25 m data gaps at 140◦W and 45 m

data gaps at both 110◦W and 140◦W.

Data gaps at depths of 80 m, 120 m, and 200 m were not filled using MLR

techniques due to the low correlation coefficients at these depths. Similarly,

the missing 10-m data in the PR16 deployment at 110◦W were not filled

using MLR techniques. The correlation coefficient for 110◦W at 10 m was

only .86, significantly lower than the .93 correlation computed for 140◦W

at 10 m. The lack of correlation at 110◦W may be related to the depth of

the Equatorial Undercurrent (EUC) at each location. At 140◦W, the EUC

depth is generally between 75 and 175 m, whereas at 110◦W, the EUC depth

is about 75 m shallower than at 140◦W and often reaches the surface. This

increased variability in the 110◦W current structure at 10 m, 25 m, and

45 m decreased the correlation of existing speeds with the calculated speeds

derived from regression formulae based on these depths.

The MCM data not filled by the MLR technique or interpolation were
Correcting Moored ADCP Data for Fish-Bias Errors 5

filled by careful evaluation of the ADCP data. Information on the evidence

of fish bias in the ADCP data is available in the ADCP data from the

echo intensity, which is related to the strength of the backscattered acoustic

signal for each of the four beams. An increase in echo intensity, which was

not related to the diurnal movement of zooplankton, indicated the presence

of fish. For deeper depths, 80 m and below, ADCP velocity errors due to the

presence of fish appeared to be diurnally modulated as described in Freitag et

al. (1993). Fish were present during local daylight hours and absent during

the night. This allowed a daily average to be computed by averaging the

ADCP velocities from the local nighttime hours only (2000 to 0200 local

time). These velocities, essentially free of fish bias, were used to fill the

MCM data records needed for the EOF analysis described below.

For the PR16 deployment, 10-m MCM data were missing for the period

19 July to 17 September 1994. Since these data could not be filled with

the MLR technique, the diurnal presence of fish at 10 m was evaluated

for the PR16 deployment. In other deployments, more heavily biased by

fish, the depths shallower than 80 m generally had significant fish bias even

during local nighttime hours. However, the echo intensities from the PR16

deployment indicated that the ADCP data were essentially free of fish bias

for the entire day at all depths through 27 August 1994. For consistency,

a daily average was computed from only the nighttime ADCP values to fill

the 10-m MCM data gap through 27 August 1994. During the last 21 days

of the deployment, the nighttime hours had minimal fish bias at 10 m and

an average speed could be computed from 2000 to 0200 local time for all

but four of the days. The 10-m speeds for these 4 days were computed by

interpolation between the successfully computed nighttime averaged ADCP

speeds.

No ADCP or MCM data were collected at 110◦W from 5 May thru 7

June 1994.

3. ADCP Velocity Bias

The ADCP transmits an acoustic signal from each of its four beams directed

30 degrees from vertical. The instrument measures the Doppler shift of the

return signal for each beam, time gated so as to compute the beam-direction

velocity as a function of range. The beam velocities are converted to Earth

coordinates using beam geometry and direction from the compass. The frequency shift in the return signal is caused by the relative motion of oceanic

scatterers with respect to the ADCP transducers. For the motion of the

scatterers to represent ocean velocities, it is assumed that they move passively with the currents. Comparison with the MCM velocities has shown

that this assumption fails when pelagic fish are present near the surface

moorings. Since the mean movement of these fish is not due to advection

by currents, their presence in the ADCP acoustic beam will tend to reduce

the magnitude of the velocity measurements. For example, if the scattering

intensity of a fish is similar to that of the surrounding water, an individual

fish would be detected in one ADCP beam but not the opposing beam. The
6 Plimpton et al.

horizontal velocity computed from the two beams would be equal to the average of the true ocean current measured in one beam and the current velocity

biased by the fish velocity in the second beam. The velocity of fish schooling

around a mooring would, in the mean, be small. Thus, when the acoustic

reflections from fish are averaged with the reflections from passive scatterers,

the ensemble averaged measurement of horizontal velocities would be biased

low. Alternately, in the situation where the scattering intensity from a fish is

much greater than the surrounding water, the same fish could be detected in

the side lobes of the opposing and neighboring beams. The largest side lobe

amplitude is down about 35 db from the beam’s main lobe and the fish echo

intensity in the beam side lobes can be larger than the scattering signal from

the surrounding water. In this situation, the horizontal velocity would tend

toward zero because the fish-dominated signals in opposing beams would

tend to cancel in the computation of horizontal velocity.

The duration and depth range where fish were present around a mooring

was quite variable for different deployments. Figures 5 and 6 show the

daily ADCP minus MCM speed differences for the deployments at 110◦W

and 140◦W, respectively. The negative sign of the differences represents

the decrease in the ADCP speeds due to the presence of fish. At 140◦W,

the speed differences for the PR13 deployment indicates the presence of fish

down to 80 m immediately after the mooring was deployed. In comparison,

significant fish bias velocity errors were not evident for the first 21⁄2 months of

the PR19 deployment. At 110◦W, the fish bias errors appeared less extensive

than at 140◦W and generally were not evident for several months after the

mooring was deployed.

The presence of fish in the acoustic signal was also evaluated using the

ADCP echo intensity (EI). Since it was difficult to distinguish whether increased EI was due to diurnal movements of the scatterers or to the presence

of fish, the difference in beam-to-beam EI was computed. For a given depth,

the highest beam EI was differenced with the lowest beam EI to compute

the echo intensity range (EIR). Use of the EIR to determine the presence of

fish was most effective when fish were detected in only one to three beams

during the averaging period. If fish were detected in all four beams during the averaging period, all beams would have elevated EI’s and the EIR

magnitudes would be reduced. In Figs. 5 and 6, periods of large EIR are

coincident with times of large ADCP speed errors.

4. ADCP Data Correction

The surface moored ADCP time series at 140◦W and 110◦W is an extensive

current velocity data set with high vertical resolution. The velocity errors

that occurred at times, due to the presence of fish, had to be corrected before

the time series could be included in the TAO data base. A method has been

developed to correct the ADCP data in post processing using in situ MCM

data by computing empirical orthogonal functions (EOFs) of ADCP-MCM

speed differences at the depths of the MCMs.

Mean ADCP and MCM speeds (Figs. 7a–14a) and mean ADCP-MCM
Correcting Moored ADCP Data for Fish-Bias Errors 7

Table 3: Percent variance corrected for each of the first three vertical modes and their total for each deployment.

Mode 1 Mode 2 Mode 3 Total

110 ◦W Sites

PR14 71.4 15.0 6.0 92.4

PR16 86.2 5.5 3.5 95.2

PR18 66.3 17.9 11.7 95.9

PR20 71.5 13.8 7.1 92.4

140 ◦W Sites

PR13 56.0 19.5 17.4 92.9

PR15 71.9 17.0 6.1 95.0

PR1756.4 28.0 6.3 90.7

PR19 77.9 10.4 4.9 93.2

speed differences (Figs. 7b–14b) were computed for each deployment. A

spline fit was performed on the speed differences at the MCM depths to

create values at all intervening ADCP depths. For each deployment, the

spline fit was forced to 0 at 200 m. This was because fish bias was generally

negligible from 200 to 250 m, which was evident from the minimal values

for ADCP-MCM speed differences and EIR at these depths (Figs. 5 and

6). The small differences that were evident may have been from instrument

measurement differences or overspeeding by the VACMs (Halpern, 1987).

For each deployment, the mean speed differences were subtracted from

the ADCP-MCM time series before computing the EOFs. A spline fit, forced

to 0 at 200 m, was performed on the eigenvectors for each eigenmode to create

values at the ADCP depths. The first three eigenmodes for each deployment

are shown in Figs. 15 to 22.

Only the first three eigenmodes were used in this correction scheme because the difference between the ADCP and the MCM speeds were not all

due to the presence of fish. Higher EOF modes may have captured more subtle forms of instrument error in the MCMs and/or the ADCPs. Likewise,

some of the ADCP-MCM differences were probably due to the fact that the

ADCP velocity represents a weighted mean over 16 m compared to the MCM

velocity, which applies to a specific depth. Table 3 lists the percent variance

corrected for each of the first three vertical modes for each deployment. For

all deployments, more then 90% of the variance was explained by these three

modes.

For each deployment, the means and first three vertical modes were combined to produce a correction file for the daily ADCP speeds at the ADCP

depths. When the corrected ADCP speeds were compared with the MCM

speeds, there was considerable reduction in the standard deviation of the

speed differences for each deployment (Figs. 7c to 14c). Comparison of the

MCM and the ADCP daily directions indicated that errors in the ADCP

directions occurred due to the presence of fish. The direction errors were
8 Plimpton et al.

evident when measured ADCP speeds less than 10 cm s−1 were increased

by more than 50 cm s−1 during correction by the EOF analysis. At these

times, the daily averaged ADCP directions would have included measurements where the fish-biased speeds were often near zero value and the directions were meaningless. At other times, when the daily ADCP speed bias

was smaller, the ADCP directions did not appear affected by the presence

of fish. Thus, daily profiles of zonal and meridional velocity were first computed using all the ADCP directions and the corrected ADCP speeds. Then

the ADCP data were flagged bad when measured speeds of 10 cm s−1 or less

increased by at least 50 cm s−1 after correction and, for the MCM depths,

when corrected ADCP-MCM zonal or meridional velocity differences were

greater than 10 cm s−1. These data were identified on a bin by bin basis for

each daily profile and deleted from the ADCP data base. ADCP directions

for earlier 1990 to 1993 PROTEUSdeployments, which were previously documented in Plimpton et al. (1995) and included in the TAO archive, were

also reexamined and edited using these procedures.

After the fish-biased ADCP directions were eliminated from the data set,

the ADCP and MCM directions were again compared. The mean ADCPMCM direction differences (Figs. 7d–14d) were used to identify compass errors that may not have been detected in the routine evaluation of the current

meter data. The ADCP directions for the PR17 deployment were rotated by

−5◦. The PR17 ADCP compass error was evident in the large mean ADCPMCM direction differences at all the MCM depths and the apparently large

meridional velocity at the depth of the Undercurrent. MCM directions were

corrected when the mean difference between the edited ADCP daily directions and the MCM directions at a given depth exceeded 10◦. The MCM

directions were then adjusted so that the mean ADCP-MCM difference was

equal to the average differences found at the other depths for the deployment.

The final adjusted directions were used in Figs. 7–14.

The corrected and edited zonal and meridional velocities for the eight

deployments described above were included in the TAO ADCP data base.

Contour plots of these velocities from 1993–1995 at 110◦W and 140◦W are

shown in Figs. 23 and 24, respectively.

5. Summary

Beginning in 1990, PROTEUSmooring sites were established as part of the

TAO array to measure and transmit real-time currents in the upper ocean

with an ADCP. However, PROTEUSvelocity measurements were at times

biased toward lower values due to the reflections from pelagic fish in the

acoustic beams of ADCPs mounted on surface moorings. As a result of the

velocity errors, an algorithm was developed to try to remove fish-bias errors

during data acquisition. Although some fish-biased data were rejected by

the algorithm during data collection, the algorithm essentially failed and

large errors were still evident in the ADCP velocities. For these reasons, the

use of PROTEUSmoorings in TAO was stopped in 1995, and all subsequent
Correcting Moored ADCP Data for Fish-Bias Errors 9

equatorial ADCPs were mounted on subsurface moorings. The subsurface

ADCP data were free of the fish bias found in surface deployments.

The extent of the fish-bias errors in the 1990 to 1995 surface moored

ADCP data were evaluated by computing the ADCP echo intensity range

and by comparison with in situ MCM data. The error description and correction for the 1990 to 1993 ADCP data at 110◦W and 140◦W are presented

in Plimpton et al. (1995). The 1993 to 1995 ADCP velocity errors at 110◦W

and 140◦W are described above. A procedure based on an EOF analysis

of ADCP-MCM speed differences, similar to that used for the earlier data,

were used to correct the ADCP velocities for this period. The corrected

speeds are expected to be accurate to about 5 cm s−1. This estimate was

derived from comparison of two coincident ADCP deployments at 140◦ in

1993 (Plimpton et al., 1995; Plimpton et al., 1997). The corrected ADCP

profiles of daily zonal and meridional velocities have been included in the

TAO data base. These data provide velocities with a finer vertical resolution

and fewer data gaps than previously available with the MCM data.

6. Acknowledgments

This work was supported by NOAA’s office of Global Programs and NOAA’s Environmental Research Laboratories.

7. References

Beardsley, R.C. (1987): A comparison of the vector-averaging current meter and

New Edgerton, Germeshausen, and Grier, Inc., vector-measuring current meter

on a surface mooring in Coastal Dynamics Experiment 1. J. Geophys. Res., 92,

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

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Through Technology, 26–29 October 1992, Newport, RI, 712–717.

Freitag, H.P., P.E. Plimpton, and M.J. McPhaden (1993): Evaluation of an ADCP

fish-bias rejection algorithm. Proceedings of Oceans ’93, Engineering in Harmony with the Ocean, Vol. II, 18–21 October 1993, Victoria, Canada, 394–397.

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practical primer. R.D. Instruments, 51 pp. (Available from R.D. Instruments,

9855 Businesspark Ave., San Diego, CA 92131.)

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in the equatorial Pacific. J. Atmos. Ocean. Tech., 4, 84–93.

Hayes, S.P., L.J. Mangum, J. Picaut, A. Sumi, and K. Takeuchi (1991): TOGATAO: A moored array for real-time measurements in the tropical Pacific Ocean.

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Karweit, M. (1974): Response of a Savonious rotor to unsteady flow. J. Mar. Res.,

32, 359–364.

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PROTEUS—Profile telemetry of upper ocean currents. Proceedings of the MTS

’90 Conference, Washington, DC, 26–28 September, Marine Technology Society,

Washington, DC, 353–357.

McPhaden, M.J., A.J. Busalacchi, R. Cheney, J.R. Donguy, K.S. Gage, D. Halpern,

M. Ji, P. Julian, G. Meyers, G.T. Mitchum, P.P. Niiler, J. Picaut, R.W.
10 Plimpton et al.

Reynolds, N. Smith, and K. Takeuchi (1998): The Tropical Ocean Global Atmosphere observing system: A decade of progress. J. Geophys. Res., 103(C7),

14,169–14,240.

Plimpton, P.E., H.P. Freitag, and M.J. McPhaden (1995): Correcting moored

ADCP data for fish-bias errors at 0◦, 110◦W and 0◦, 140◦W from 1990 to

1993. NOAA Tech. Memo. ERL PMEL-107 (NTISPB95-219366), 49 pp.

Plimpton, P.E., H.P. Freitag, and M.J. McPhaden (1997): ADCP velocity errors

from pelagic fish schooling around equatorial moorings. J. Atmos. Ocean. Tech.,

14, 1212–1223.

Pullen, P.E., M.J. McPhaden, H.P. Freitag, and J. Gast (1992): Surface wave

induced skew errors in acoustic Doppler current profiler measurements from

high shear regimes. Proceedings of Oceans ’92, Mastering the Oceans Through

Technology, 26–29 October 1992, Newport, RI, 706–711.

Webster, P.J., and R. Lukas (1992): TOGA-COARE: The Coupled OceanAtmosphere Response Experiment. Bull. Amer. Meteorol. Soc., 73, 1377–1416.

Weisberg, R.H., J.C. Donovan, and R.D. Core (1991): The tropical instability wave

experiment (TIWE) equatorial array. University of South Florida Data Report,

St. Petersburg, FL, 84 pp.

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Res., 27, 575–582.
Correcting Moored ADCP Data for Fish-Bias Errors 11

Figures
12 Plimpton et al. Figure 1: Tropical Atmosphere Ocean (TAO) array of ATLASmoorings (diamonds) and current meter moorings (squares). Solid squares indicate sites instrumented with telemetering PROTEUScurrent meter moorings prior to 1995. At 110◦W, 140◦W, and 165◦E, the ADCPs previously on surface moorings are now deployed on subsurface moorings.
Correcting Moored ADCP Data for Fish-Bias Errors 13

PROTEUS

Figure 2: PROTEUSmooring with atmospheric sensors, an ADCP, mechanical current meters (open

squares), and temperature sensors (solid squares).
14 Plimpton et al. Figure 3: Depth and time coverage of mechanical current meter data at 0◦, 110◦W. Areas of thin striping at 25 m and 45 m are data filled using interpolation or linear regression techniques. Thick striping at 10 m, 80 m, 120 m, and 200 m indicate gaps filled using ADCP data from the portion of the day that was minimally affected by fish. Due to problems with the MCM compass, only speed data are available at 200 m for the entire PR14 deployment and from December 2 to February 22 for the PR18 deployment.
Correcting Moored ADCP Data for Fish-Bias Errors 15 Figure 4: Depth and time coverage of mechanical current meter data at 0◦, 140◦W. Areas of thin striping at 10 m, 25 m, and 45 m are data filled using interpolation or linear regression techniques. The thick striping at 80 m indicates a data gap filled using ADCP data from the portion of the day that was minimally affected by fish. Due to MCM compass problems, only speed data are available at 80 m from April 11 to September 9 for the PR19 deployment.
16 Plimpton et al.

Figure 5: Time series of ADCP-MCM speed difference and echo intensity range for four deployments at

0◦, 110◦W. Deployment and recovery times of the moorings are shown by black dots at the abscissas. At

10 m depth, the ADCP speed data have been linearly extrapolated from the bin 1 depth of 14 m for the

comparison.
Correcting Moored ADCP Data for Fish-Bias Errors 17

Figure 6: Time series of ADCP-MCM speed difference and echo intensity range for four deployments at

0◦, 140◦W. Deployment and recovery times of the moorings are shown by black dots at the abscissas. At

10 m depth, the ADCP speed data have been linearly extrapolated from the bin 1 depth of 14 m for the

comparison.
18 Plimpton et al.

Figure 7: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean ADCPMCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of speed

difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCP-MCM

direction difference, (e) standard deviation of direction difference, and (f) number of days of data collected

for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).
Correcting Moored ADCP Data for Fish-Bias Errors 19

Figure 8: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean ADCPMCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of speed

difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCP-MCM

direction difference, (e) standard deviation of direction difference, and (f) number of days of data collected

for MCM (circles) and number of days of ADCP data after correction and editing (solid curve). Due to

MCM compass problems, only MCM speeds were available at 200 m for the entire deployment period.
20 Plimpton et al.

Figure 9: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean ADCPMCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of speed

difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCP-MCM

direction difference, (e) standard deviation of direction difference, and (f) number of days of data collected

for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).
Correcting Moored ADCP Data for Fish-Bias Errors 21

Figure 10: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean

ADCP-MCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of

speed difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCPMCM direction difference, (e) standard deviation of direction difference, and (f) number of days of data

collected for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).
22 Plimpton et al.

Figure 11: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean

ADCP-MCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of

speed difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCPMCM direction difference, (e) standard deviation of direction difference, and (f) number of days of data

collected for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).
Correcting Moored ADCP Data for Fish-Bias Errors 23

Figure 12: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean

ADCP-MCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of

speed difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCPMCM direction difference, (e) standard deviation of direction difference, and (f) number of days of data

collected for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).

Due to MCM compass problems, only MCM speeds were available at 200 m from December 2–February 22.
24 Plimpton et al.

Figure 13: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean

ADCP-MCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of

speed difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCPMCM direction difference, (e) standard deviation of direction difference, and (f) number of days of data

collected for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).

Due to MCM compass problems, only MCM speeds were available at 80 m from April 11 to September 9.
Correcting Moored ADCP Data for Fish-Bias Errors 25

Figure 14: Profiles of (a) mean speed for MCM data (circles) and ADCP data (solid curve), (b) mean

ADCP-MCM speed difference (circles) and spline fit to the difference (solid curve), (c) standard deviation of

speed difference before EOF correction (solid curve) and after correction (dashed curve), (d) mean ADCPMCM direction difference, (e) standard deviation of direction difference, and (f) number of days of data

collected for MCM (circles) and number of days of ADCP data after correction and editing (solid curve).
26 Plimpton et al.

Figure 15: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR13.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
Correcting Moored ADCP Data for Fish-Bias Errors 27

Figure 16: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR14.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
28 Plimpton et al.

Figure 17: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR15.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
Correcting Moored ADCP Data for Fish-Bias Errors 29

Figure 18: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR16.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
30 Plimpton et al.

Figure 19: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR17.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
Correcting Moored ADCP Data for Fish-Bias Errors 31

Figure 20: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR18.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
32 Plimpton et al.

Figure 21: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR19.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
Correcting Moored ADCP Data for Fish-Bias Errors 33

Figure 22: The first three EOF eigenvectors and time series of ADCP-MCM speed differences for PR20.

Percent variance explained at each depth is shown in the center panel. Percent of total variance explained

by each EOF is shown in the upper right hand corner of the time series panel. The symbols (×) indicate

values at individual MCM depths; the solid curve through the individual eigenvector values is a spline fit

which has been forced to zero at 200 m.
34 Plimpton et al.

Figure 23: Zonal and meridional velocity at 0◦, 110◦W from corrected daily profiles of ADCP data. Contours are 20 cm s−1 with light shading for westward and southward velocities. Dark shading represents

eastward or northward velocities greater than 60 cm s−1.
Correcting Moored ADCP Data for Fish-Bias Errors 35

Figure 24: Zonal and meridional velocity a 0◦, 140◦W from corrected daily profiles of ADCP data. Contours

are 20 cm s−1 with light shading for westward and southward velocities. Dark shading represents eastward

or northward velocities greater than 60 cm s−1.