Monitoring Surface Velocity from Repeated ADCP Observations and Satellite Altimetry

Transcription

1 Journal of Oceanography, Vol. 60, pp. 365 to 374, 2004 Monitoring Surface Velocity from Repeated ADCP Observations and Satellite Altimetry KAORU ICHIKAWA 1 *, NORIAKI GOHDA 2, MASAZUMI ARAI 2 and ARATA KANEKO 2 1 Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka , Japan; also at Frontier Observational Research System for Global Change, Yokosuka, Kanagawa , Japan 2 Division of Global Environment Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima , Japan (Received 20 May 2003; in revised form 18 October 2003; accepted 18 October 2003) A method has been developed to monitor the surface velocity field by combining repeated acoustic Doppler current profiler (ADCP) observations and satellite altimetry data. The geostrophic velocity anomaly is calculated from the sea surface height anomaly field estimated from the altimetry data by an optimal interpolation. It has been confirmed that this accurately observes the smoothed velocity anomaly field when the interpolation scales are set according to the spatio-temporal sampling pattern of the altimeter used. The velocity anomaly obtained from the altimetry data is subtracted from the repeated ADCP observations to estimate temporal mean velocity along the ship tracks. Regularly sampled, nine-year time series of surface velocity can then be obtained by adding the computed mean velocity and the altimetry anomaly components. This clearly illustrates surface velocity fluctuations such as the movement of the Kuroshio axis due to its meandering and an increase of the interannual variability of the Subtropical Countercurrent toward its downstream region. Keywords: ADCP, altimeter, surface velocity, Kuroshio, Subtropical Countercurrent. 1. Introduction Since most large-scale signals in the ocean tend to propagate westward, variations in mid-ocean areas may alter the western boundary currents such as the Kuroshio (e.g. Ichikawa, 2001; Tanaka and Ikeda, 2004). It is therefore crucial to monitor the signals in a wide area to study and/or predict the western boundary currents (e.g. Kamachi et al., 2004). Satellite observations are entirely suitable for monitoring wide areas, although the observations are restricted to surface phenomena. Satellite altimetry is especially useful since it can provide observations of the sea surface dynamic topography (SSDT; deviation of the sea surface height from the equi-geopotential geoid surface) which is directly related to the surface geostrophic velocity field itself. However, since the available geoid models are not sufficiently precise, and will not be, until drastic improvements are made, which are expected on completion of the satellite geodetic missions (National Research Council, 1997), only the temporal variations of the SSDT can be accurately obtained from the altimetry data so far. * Corresponding author. ichikawa@riam.kyushu-u.ac.jp Copyright The Oceanographic Society of Japan. Several researchers have nevertheless tried to recover the missing mean SSDT (e.g. Qiu et al., 1991; Ichikawa and Imawaki, 1994). One of the most promising methods seems to be the combined use of the trajectories of surface drifters, which can support a wide coverage of the surface velocity field (Uchida and Imawaki, 2003). Lagrangian observations such as drifters, however, may be subject to the current field itself; i.e., the density of observations in a given area may depend on the convergence of the flow field. Some repeated Eulerian observations of velocity field would therefore also be important to establish monitoring systems. Relatively wide temporal and/or spatial coverage of in situ velocity observations can be obtained by acoustic Doppler current profilers (ADCP s) mounted on volunteer ships (Hanawa et al., 1996; Kaneko et al., 1998, 1999, 2001). These can provide not only frequent Eulerian surface velocity data, but also vertical current structures. The use of these data in a monitoring system, however, involves some difficulties due to the irregularity of the observations, especially when a ship takes multiple routes; the routes of commercial ships may depend on economic demands as well as weather conditions (Kaneko et al., 1998, 1999). In the present study, a method has therefore been developed to regularly monitor the surface velocity 365

2 field by combined use of repeated ADCP observations and altimetry data. Use of the ADCP data provides a method to estimate the missing temporal mean from the anomalies of the altimetry data, and the altimetry data, in turn, assure the regular monitoring of the surface velocity field. The remainder of the present paper is organized as follows. The data and method used are described in Sections 2 and 3, respectively; two independent data sets, ADCP velocity and the surface geostrophic velocity determined from the altimetry data, are carefully compared in Subsection 3.2. The results obtained, including the temporal mean velocity field and the time series of the surface velocity field, are shown in Section 4, which also provides detailed descriptions on variations of the Kuroshio and the Subtropical Countercurrent. Discussions of the results and concluding remarks are presented in the following Section Data A research program to observe North Pacific velocity field by an ADCP mounted on a commercial mineral transport ship First Jupiter started in 1997, led by Hiroshima University with partial support from the Core Research for Evolutional Science and Technology of the Japan Science and Technology Corporation (Kaneko et al., 1998, 1999, 2001). The program has provided surface velocity observations in the upper 200-m layer along 43 ship tracks during ; these data are available from our web site ( Among these ship tracks, the tracks between Kashima and the eastern side of Australia (KE), Kashima and the western side of Australia (KW), and Wakayama and the western side of Australia (WW), have been repeated 11, 14 and 6 times, respectively, although they are irregularly spaced in time (Fig. 1; Table 1). At each point (approximately 9.6 km apart along a track) on these three tracks, the most extreme outlier from the average is omitted in turn from the repeated observations until the standard deviation of the zonal position is smaller than the internal Rossby radius at a given latitude. If fewer than five observations remain at the given point, we discarded all the observations at that point to reduce the effect of highfrequency noise in the ADCP data. We used only the shallowest ADCP velocity data (70-m depth) to compare with the surface geostrophic velocity determined from the altimetry data; no clear spatial or temporal dependency is found in the vertical shear of the observed ADCP velocity, suggesting that surface intensified ageostrophic flow such as the Ekman wind drift is not dominant in the ADCP data. Altimetry data sets of TOPEX/POSEIDON (T/P), European Remote Sensing satellite (ERS)-1 and ERS-2 used in the present study are distributed by AVISO (Le Traon et al., 1995; AVISO, 1996; Le Traon and Ogor, 1998). From all these altimetry data, the temporal anomaly of the SSDT field, which is equivalent to the sea surface height anomaly (SSHA) field, is determined on a 0.25 grid by an optimal interpolation every 9.92 days (a repeat cycle of T/P) during a nine-year period between October 1992 and September The optimal interpolation used in the present study is the same as that described by Ichikawa (2001), with various spatio-temporal smoothing scales (namely, the decorrelation scales of the Gaussian correlation function of the SSHA), as described in Subsection Method WW Kii Pen. KW Fig. 1. Locations of the ADCP tracks. Thin horizontal bars indicate the zonal standard deviation at given latitude. 3.1 Fundamentals The surface velocity observed by the ADCP v ship (r, t i ) at time t i and position r can be regarded as consisting of several components, as ( )= ( )+ ( )+ ( ) () v r, t v r v r, t v r, t, 1 ship i i i where v indicates the temporal mean velocity, v represents the temporal velocity anomaly which is regarded to be in geostrophic balance, and v represents high-frequency ageostrophic fluctuations such as tidal currents, inertial oscillations and Ekman wind drift. The purpose of the present analysis is to monitor surface quasigeostrophic velocity at a given position r by obtaining the term { v (r) + v (r, t)} at any time t. In general, the velocity anomaly v (r, t i ) is expected to be replaced by the geostrophic velocity anomaly v alt (r, KE 366 K. Ichikawa et al.

3 Table 1. Observation dates for each ship track shown in Fig. 1. Tracks Year Dates KE 1999 Jan/12 17, Jul/5 9, Jul/ Jul/24 28, Aug/10 15, Nov/ Jan/17 19, Feb/10 15, Feb/20 24, Jun/1 5, Jun/19 24 KW 1997 Mar/28 Apr/02, Jul/1 5, Aug/ Jun/5 10, Jul/3 8, Jul/13 17, Aug/4 9, Dec/ Sep/11 16, Sep/29 Oct/ Apr/10 15, Aug/19 24, Sep/19 23, Oct/6 11 WW 1997 Feb/27 Mar/03, Jun/ Jun/16 20, Aug/16 18, Nov/ Mar/19 22 t i ) determined from the satellite altimetry SSHA data. Combined use of the repeated ADCP data and the satellite altimetry data thus provides several estimates of the mean velocity v (r) with high-frequency noise v (r, t i ) at each observation time t i ; namely, ( ) ( ) ( )+ ( ) ( ) v r, t v r, t v r v r, t. 2 ship i alt i i Provided that the high-frequency noises are reduced to a negligible magnitude by statistical averaging over many t i, or v 0 where... denotes statistical averaging, the average of Eq. (2) should provide a good estimate of the mean velocity v (r). The fundamental concept to determine the mean velocity v by subtracting the altimetry temporal anomaly component v alt from the in situ velocity observations is same method as that described by Uchida and Imawaki (2003). Once the mean velocity v (r) at the position r is obtained, the absolute geostrophic velocity at any time t can be obtained by combining v (r) with the geostrophic velocity anomaly v alt (r, t) obtained from the altimetry SSHA data at the given position and time. 3.2 Representativeness of v alt for v Altimetry measures the SSHA along the satellite subtrack which is equivalent only to the geostrophic velocity anomaly component normal to the subtrack, so that some sort of interpolation is necessary to obtain the vector velocity field v alt. Since replacement of v and v alt is the most essential step in the present method, the similarity between v and the interpolated vector velocity v alt should be carefully examined. In the present study, the effect of interpolation on v alt is investigated by comparing three altimetry SSHA data sets with different smoothing scales of the optimal interpolation. In two data sets, the SSHA at a point is estimated from data points on surrounding satellite subtracks, all of which are assumed to be correlated to each other. The spatio-temporal smoothing scales of the optimal interpolation are thus set to the sampling configuration of the satellite (Ichikawa and Imawaki, 1996); we used smoothing scales of approximately 340 km for the zonal distance, 223 km for the meridional distance and 5 days for the temporal differences as typical T/P sampling scales in the study area, and also smoothing scales of 83 km, 110 km, 17.5 days, respectively, for typical ERS sampling scales. Meanwhile, in the third data set, the SSHA at a point is interpolated mainly with reference to the closest data point on a single closest satellite subtrack so that the spatial smoothing scales are kept small to reproduce small-scale features (42 km, 42 km, 17.5 days). These three data sets are hereafter called T/Psampling SSHA, ERS-sampling SSHA, and closest-reference SSHA, respectively. The calculated v alt from the SSHA field of these three data sets should be compared with the observed geostrophic velocity anomaly v itself. It is impossible, however, to extract the velocity anomaly v alone from the ADCP data. Therefore, in the present study, the observed ADCP velocity v ship is instead compared with the composite velocity; the composite velocity is the combination of the geostrophic velocity anomaly v alt determined by the altimetry SSHA data and the approximated mean geostrophic velocity ṽ obtained from the climatological mean SSDT data (Ichikawa and Imawaki, 1994; Ichikawa et al., 1995). Note that the comparison between the ADCP velocity v ship = v + v + v and the composite velocity ṽ + v alt includes an additional degrading component due to the high-frequency noise v and the inconsistency between the mean fields v and ṽ, so that the comparison in the present study is worse than the comparison between v and v alt that was originally required. The inter-comparison between the three SSHA data sets, however, would not be affected by this degradation since effects due to v ṽ and v are common in Monitoring Velocity by ADCP and Altimetry 367

4 b) ~ V'alt + V ~ U'alt + U a) Numb=8680 slope=0.24 intercept=0.00 CorrC.=0.49 RMS diff=0.23 Numb=8680 slope=0.48 intercept=0.01 CorrC.=0.60 RMS diff=0.20 Uship Vship Fig. 2. Scatter diagrams for zonal (a) and meridional (b) components between the ADCP velocity vship and the composite geostrophic velocity v + v alt determined from the T/P-sampling SSHA data set. Regression for each component is indicated by broken lines, together with solid oblique lines as a reference of v alt + v = vship; the unit of the axes, intercept, and RMS difference is m/s. Numbers of points used and cross correlation coefficient are also shown in each panel. b) ~ V'alt + V ~ U'alt + U a) Numb=8982 slope=0.49 intercept=0.00 CorrC.=0.54 RMS diff=0.23 Numb=8982 slope=0.59 intercept=0.01 CorrC.=0.65 RMS diff=0.21 Uship Vship Fig. 3. As Fig. 2, but for the ERS-sampling SSHA data set. the inter-comparison. The approximate mean geostrophic velocity v used in the present study was calculated from the climatological mean geopotential anomaly data determined from in situ observations referred to the 1000 decibar surface (Teague et al., 1990). Scatter diagrams of the zonal and meridional components of these three data sets are plotted in Figs. 2 4 together with the number of points used, slope and intercept of the regression line, cross correlation coefficient, and root-mean-square (RMS) difference; each regression line is determined so that the sum of distances between the line and each data point is minimized. All ADCP data shown in Table 1 are used in the figures. For both regression lines for zonal and meridional components, the slope decreases as the spatial smoothing scale becomes larger; especially for v alt calculated from the T/P-sampling SSHA (Fig. 2), the slope for the meridional component is only The decrease of the slope is caused by spatial smoothing of the altimetry velocity v alt induced by the optimal interpolation of the SSHA, which generally re368 K. Ichikawa et al. duces extreme velocities. On the other hand, the correlation coefficient is not a simple function of the smoothing scales. Larger spatial smoothing does decrease the correlation between the T/ P-sampling and ERS-sampling data sets (Figs. 2 and 3), but the worst result is obtained from the closest-reference SSHA data set with the smallest spatial smoothing scales (Fig. 4). This is probably because the slope of the SSHA at a given point has been taken into account during the optimal interpolation for the T/P-sampling and ERS-sampling data sets by simultaneously referring to surrounding data points, meanwhile only the SSHA at the closest point, not the slope, is referred in the closest-reference data set. These results indicate that, in order to give an accurate estimate of geostrophic vector velocity, interpolation of the SSHA should simultaneously refer to data points on surrounding satellite subtracks, although a smaller smoothing is preferable. In the present study, threfore, the geostrophic velocity anomaly from the ERSsampling SSHA data set is hereafter used as v alt.

5 a) b) U'alt + U ~ V'alt + V ~ Numb=8858 slope=0.64 intercept=-0.00 CorrC.=0.52 RMS diff=0.24 Numb=8858 slope=0.55 intercept=0.01 CorrC.=0.37 RMS diff=0.28 Uship Vship Fig. 4. As Fig. 2, but for the closest-reference SSHA data set. a) b) U'alt + U ~ V'alt + V ~ Numb=878 slope=1.14 intercept=0.01 CorrC.=0.69 RMS diff=0.14 Numb=878 slope=0.99 intercept=0.01 CorrC.=0.60 RMS diff=0.17 Uship Vship Fig. 5. As Fig. 3, but with the ADCP velocity smoothed along each ship track with the same scales used in the ERS-sampling SSHA data set. Data points are limited to a separation of 90 km along the ship tracks so that the comparison is independent after smoothing. As seen in Fig. 3, the composite velocity ṽ + v alt is underestimated compared with the ADCP velocity v ship, and this is considered to be due to smoothing of the altimetry data. This idea is confirmed in Fig. 5 in which the ADCP data is smoothed along the ship tracks with the same decorrelation scales used in the ERS-sampling optimal interpolation; the data points used in the figure are limited to a separation of 90 km along the ship tracks so that the comparison is independent after the smoothing. The slopes of the regression lines are found to be significantly improved, becoming almost unity. Taking account of the possible contamination by v, which may contaminate the cross correlation coefficient and the RMS difference but may not affect the regression line by assuming its randomness, this result confirms that altimetry can observe the smoothed surface velocity field very accurately. Therefore, the ADCP data in the present analysis are hereafter smoothed over the ERS-sampling scales to accord with the altimetry data, in order to maintain the assumption of the similarity of the velocity anomalies, v v alt, in Eq. (2). 4. Results 4.1 Temporal mean velocity v The nine-year mean surface velocity v estimated by averaging Eq. (2) from the smoothed ADCP data and the ERS-sampling SSHA data is shown in Fig. 6(a). In order to illustrate the improvement of the estimated mean velocity field v, a simple statistical average of the smoothed ADCP velocity data v ship is plotted in Fig. 6(b). Both panels show the westward velocity component along all tracks south of 16 N, and both exhibit a similar, strong Kuroshio structure along the WW track south of the Kii Peninsula. Significant discrepancies are also found in some areas, though, especially south of the Kuroshio and around 20 N. For example, a strong eastward velocity core around 31 N along the KW and KE tracks is found only in Fig. 6(b). The westward Kuroshio recirculation around 29 N along the WW track is clearly stronger in Fig. 6(a) than in Fig. 6(b). Eastward velocity components related to the Subtropical Countercurrent (e.g. Hasunuma and Yoshida, Monitoring Velocity by ADCP and Altimetry 369

6 a) b) 0.5 m/s 0.5 m/s Fig. 6. Mean surface velocity v along the ship tracks determined from the ADCP data and the altimetry data (a); as a comparison, the statistical mean ADCP surface velocity v ship is plotted in the same way (b). For convenience, the velocity vectors are plotted for every three points along a ship track (approximately 29 km apart). A thin ellipse at the top of each vector indicates the standard deviation in zonal and meridional components. Eastward velocity with 0.5 m/s speed is plotted at the bottom of each panel as reference. 1978; Qiu, 1999) are found in all three tracks (20 23 N for WW; N for KW, and N for KE) in Fig. 6(a), while no eastward flow is present along the WW track in Fig. 6(b). Moreover, the eastward velocity components in Fig. 6(b) along the KW track (18 20 N) and the KE track ( N) are located to the south of those in Fig. 6(a), with larger standard deviation in Fig. 6(b) than in Fig. 6(a). These discrepancies are clearly confirmed in Fig. 7, in which the velocity difference between the two panels in Fig. 6, v v ship, is plotted over a distribution map of the difference between speed of the climatological mean velocity ṽ and the standard deviation of the altimetry ( ) 2 velocity anomaly v alt. All the areas described above as discrepancies are marked by larger vectors in Fig. 7, and most of them are associated with a mean speed ṽ that is much smaller than the variability of the velocity ( ) 2 anomaly v alt. This suggests that the velocity anomaly v tends to be quite a lot larger than the mean velocity v in such areas, so that the simple statistical mean of the ADCP velocity v ship v + v would differ from the mean velocity v itself since the statistical mean of the anomaly v may not be negligibly averaged out, compared with the weaker mean velocity v. 4.2 Seasonal and interannual variability The time series of the surface velocity v + v alt along each track can be determined at a 10-day interval. In Fig. 8, the velocity anomaly v alt is averaged over 89 days (nine T/P repeat cycles) in order to reduce variations caused by transient mesoscale eddies so that seasonal and interannual variations can be focused on in this subsection. The Kuroshio During the nine-year study period, the Kuroshio is known not to have taken the so-called stationary large meander offshore path, but it often took a large meandering path in 1999 and later. This can clearly be seen in Fig. 8(b) as the Kuroshio axis is shifted southward along the KW track during that period. In Fig. 8(a), too, a significant eastward current between N is occasionally observed to the south of the strong northeastward current at 35 N (this is especially clear in 1999 and 2001), which is because the KE track passed the Kuroshio twice; 370 K. Ichikawa et al.

7 a) KE [m/s] b) KW Fig. 7. Difference between the panels (a) and (b) in Fig. 6. The reference eastward 0.2 m/s vector is plotted at the top of the panel. The vectors are plotted for every 3 points along tracks as in Fig. 6. They are superimposed on the map of speed difference between the climatological mean velocity ṽ and the standard deviation of the velocity anomaly v alt. Positive speed difference (shaded with lines) indicates that the current is so variable that the speed of the velocity anomaly v alt tends to be larger than that of the mean velocity ṽ. Negative values (shaded with dots) are observed only in the areas close to the southwestern coast of Japan and at lower latitudes. c) WW 0.5m/s it intersected the Kuroshio at 35 N, and it also crossed an area where a large meander of the Kuroshio interacted with a cyclonic ring to the east, which is seen in the composite SSDT (see Ichikawa and Imawaki, 1994, as an example during the Geosat period). Note that these traces of the Kuroshio meander remain in v ship as if the southern branch of the Kuroshio were centered at about 31 N (Fig. 6(b)), since the dates of ADCP observations indicated by triangle marks in Fig. 8 concentrate in the later half of the nine-year period. Meanwhile, along the WW track in Fig. 8(c), the Kuroshio took a relatively stable path, although slight southward shifts of the Kuroshio axis are found during This is consistent with Fig. 7, which indicates that the temporal mean velocity is larger than the anomaly in the Kuroshio region west of Kii Peninsula. The Subtropical Countercurrent As described in Subsection 4.1, the region at N, where the Subtropical Countercurrent (hereafter abbreviated STCC) is present, is marked in Fig. 7 by a larger temporal velocity anomaly than the mean speed, similar to the downstream Kuroshio region east of Kii Peninsula. Actually, even in the 89-day averaged velocity in Fig. 8, the STCC is so variable that its mean structure, shown in Fig. 6, is not significantly resolved along Fig. 8. Time series of the surface velocity v + v alt along the ship tracks KE (a), KW (b) and WW (c). Northward velocity is plotted as an upward vector; reference eastward 0.5 m/s vector is plotted at the top of the panel (c). Each vector is determined by averaging v alt over nine T/P cycles (89 days), and they are plotted for every three points as in Fig. 6. Triangles at the bottom of each panel indicate dates of the ADCP observations shown in Table 1. all tracks. Interannual variability of the STCC, however, is revealed to depend on location by using the long-term time series. Figure 9 shows the time series of the 1-year averaged velocity at N with tilting of vector plots; the tilt angle for each panel is determined so that the mean velocity v in Fig. 6(a) that corresponds to the STCC is approximately plotted as a horizontal vector. The southeastward STCC along the WW track (Fig. 9(c)) is consistently present at about N, although its width Monitoring Velocity by ADCP and Altimetry 371

8 a) KE a) KE b) KW b) KW Feb.-Apr. May-July Aug.-Oct. Nov.-Jan. c) WW c) WW Feb.-Apr. May-July Aug.-Oct. Nov.-Jan. Fig. 9. As Fig. 8, but each profile is determined by averaging over 37 T/P cycles (367 days), and the vectors are plotted between 16 N and 29 N with tilt by 45 (a), 30 (b) and 60 (c). Reference eastward 0.2 m/s vector is plotted at the left-hand corner of each panel. Fig. 10. Seasonally averaged surface velocity v + v alt along the ship track KE (a), KW (b) and WW (c). All nine-year data is sorted in periods between February April, May July, August October and November January. Tilting angles, interval of the vector plots, and reference eastward velocity are as in Fig. 9, and thin ellipses are as in Fig. 6. shows slight variations, such as the broader band in It is quite impressive that even this significant, steady STCC would be easily dismissed in a short-term or single snapshot observation by overlaying the much larger velocity anomaly induced by transient mesoscale eddies. Indeed, the statistical average of the six ADCP observations v ship in Fig. 6(b) cannot represent the existence of the STCC. Note that there is also another stable southeastward current at N, but this is considered not to be the STCC but accompanies the Kuroshio recirculation at N. Furthermore, the STCC along the KW and KE tracks exhibits clear interannual variations. The eastward STCC along the KW track is frequently found in Fig. 9(b) at N, the downstream area of the stable STCC along the WW track, but the eastward current is found to be separated in 1996 to the south (18 19 N) and to the north (23 24 N). Similarly, the northeastward STCC at N along the KE track (Fig. 9(a)) is found, except in 1994, 1998 and 2001, while another northern band of eastward current at N is also found in and in These would be related to the double subsurface density fronts at 24 N and 18 N, recently reported by Aoki et al. (2002). It should also be noted that the interannual variability of the STCC evidently becomes larger along the ship tracks in the downstream of the STCC. Seasonality of the STCC has been discussed in previous studies (e.g. Qiu, 1999; Kobashi and Kawamura, 2002), which generally concluded that the STCC tends 372 K. Ichikawa et al.

9 to be strengthened in summer and weakened in winter. In Fig. 10, which shows the seasonally averaged vector produced by annually folding the nine-year data, this tendency can be discerned, although its standard deviation is generally very large due to both interannual and intraseasonal variability. For example, all the STCC centered at 20 N along the WW track, at 20.5 N along the KW track, and at 19 N along the KE track, becomes stronger and wider in May July. Note that the northern band of eastward current along the KE track centered at 23 N described above becomes significant not only in May July but also in November January; this eastward current shows a quite pronounced semiannual variability. 5. Discussion and Summary A method to regularly monitor the surface velocity has been developed by combined use of repeated ADCP observations v ship and altimetry data. The mean velocity v along the ship track is first determined from v ship by subtracting surface geostrophic velocity anomaly v alt calculated from the altimetry SSHA field. In order to obtain accurate vector velocity v alt, it is found that the SSHA at a point should be estimated from altimetry data points on surrounding satellite subtracks so that the slope of the SSHA at the point is accounted for in the interpolation. In addition, it is also necessary to smooth the ADCP v ship data over the same scales as the SSHA data in order to give agreement with the interpolated altimetry data v alt. In the present analysis, the best smoothing scales for the altimetry SSHA data and ADCP data are the sampling patterns of the ERS-1/2 altimeters; note that this conclusion may not hold in areas close to the Equator where large-scale phenomena occur in short time scales, for which the T/P sampling would be more suitable. As mentioned in Section 1, another mean velocity field is available, which is determined similarly from altimetry data and surface drifter trajectories (Uchida and Imawaki, 2003). The drifter-based velocity field is not limited to ship tracks as in Fig. 6, so it can cover a wider area. However, when the drifter-based nine-year mean velocity v drift is extracted along the ship tracks used in the present study (Fig. 11), numerous missing areas are revealed, where there are too few drifter observations to determine the mean velocity v drift accurately. The missing field is especially serious in the Kuroshio recirculation area around 30 N along the KW and KE tracks. Moreover, in the STCC region the mean velocity field is so segmented that the presence of the STCC is not clearly recognized, at least along these ship tracks. Meanwhile, in the Kuroshio region where plenty of drifters passed, a current structure similar to Fig. 6(a) is present with significantly larger maximum speed and horizontal velocity shear. It should be noted, however, that since the 0.5 m/s Fig. 11. Mean surface velocity determined from the altimetry data and surface drifters (Uchida and Imawaki, 2003) extracted along the ship tracks. Thin ellipses indicate the zonal and meridional errors. The reference vector and plotting intervals are as in Fig. 6, but no data are plotted if the mean velocity is missing. geostrophic vector velocity v alt from the altimetry data never modifies any small-scale current structure even in Uchida and Imawaki s (2003) method, the small-scale structure related to the large horizontal velocity shear of the Kuroshio is subject to the reliability of the statistical average of intermittent observations of the drifter velocity v drift. Note too that the presence of a large ageostrophic component is indicated in Fig. 11 by convergence of the Kuroshio along the WW track and divergence along the KW and KE tracks. The ADCP velocity at 70-m depth used in the present study would represent upper layer density structures more directly than the surface velocity determined from drifters with 15-m depth drogues, which would be more affected by ageostrophic velocity such as the Ekman wind drift. These advantages ensure that the present method produces a better monitoring system along the given ship tracks, though it is spatially limited. Although the standard deviation of the estimated mean field v (Fig. 6(a)) is generally smaller than that of the simple statistical average of the ADCP data v ship (Fig. 6(b)), it still has an order of magnitude 0.1 m/s. One possible reason for this is the introduction of errors by replacement of v alt and v in Eq. (2), which would be significant at lower latitudes below 13 N where the standard deviation of the estimated mean v is larger than that Monitoring Velocity by ADCP and Altimetry 373

10 for the simple average v ship. Generally, an error in geostrophic velocity v alt calculated from the SSHA is expected to be larger at lower latitude since the magnitude of the inverse Coriolis parameter 1/f increases closer to the Equator. Another possible reason for the relatively larger standard deviation of the mean velocity v is that the high-frequency noise v would be considerable, although the Ekman wind drift is expected to be not so strong at 70-m depth, as mentioned above. This would further suggest that a single ADCP observation may be contaminated by high-frequency ageostrophic current, v, so that it may induce unrealistic results unless treated carefully. Using the estimated mean v and the anomaly v alt from the altimetry data, time series of the velocity along the ship tracks can be obtained at a 10-day regular interval. Since the downstream Kuroshio east of Kii Peninsula and the STCC are very variable, as indicated in Fig. 7, frequent, regularly-sampled data is quite useful to discuss long-term variations which are never revealed by a single snapshot observation. In the time series obtained, interannual variations of the Kuroshio and the STCC are well described, as expected. In particular, the interannual variation of the STCC is shown to become larger downstream of the STCC. Meanwhile, even the relatively stable STCC along the WW track is often dismissed in a single snapshot observation by overlaying much stronger mesoscale eddies, which suggests the difficulty of studying STCC variations based on a few snapshot observations. Acknowledgements Dr. Hiroshi Uchida kindly provided data of the mean velocity field. The authors also appreciate helpful suggestions from two anonymous reviewers. 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