operational data assimilation system for the...

303

Journal of Oceanography, Vol. 60, pp. 303 to 312, 2004

Keywords:⋅ Data assimilation,⋅ Kuroshio,⋅ reanalysis,⋅ Ryukyu CurrentSystem.

* Corresponding author. E-mail: [email protected]

Copyright © The Oceanographic Society of Japan.

Operational Data Assimilation System for the KuroshioSouth of Japan: Reanalysis and Validation

MASAFUMI KAMACHI1*, TSURANE KURAGANO2, HIROSHI ICHIKAWA3, HIROHIKO NAKAMURA3,AYAKO NISHINA3, ATSUHIKO ISOBE4, DAISUKE AMBE4, MASAZUMI ARAI5, NORIAKI GOHDA5,SATOSHI SUGIMOTO2, KUMI YOSHITA2, TOSHIYUKI SAKURAI2 and FRANCESCO UBOLDI6

1Meteorological Research Institute, Nagamine, Tsukuba 305-0052, Japan2Office of Marine Prediction, Japan Meteorological Agency, Ohtemachi, Tokyo 100-8122, Japan3Faculty of Fisheries, Kagoshima University, Shimoarata, Kagoshima 890-0056, Japan4Department of Earth System Science and Technology, Interdisciplinary Graduate School of Engineering Science, Kyushu University, Kasuga, Fukuoka 816-8580, Japan5Faculty of Engineering, Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8527, Japan6LEGOS/BRESM, 14, Av. Edouard Belin 31400 Toulouse, France

(Received 11 September 2003; in revised form 2 February 2004; accepted 2 February 2004)

We describe an operational ocean data assimilation system for the Kuroshio and itsvalidation using a nine-year reanalysis (historical run from 1993 to 2001) dataset ofupper-ocean state estimation in the North Pacific. The horizontal structure of volumetransport of the Ryukyu Current System (RCS) is shown from the reanalysis: TheRCS is connected to the flow of the subtropical gyre, and its volume transport gradu-ally increases from south-east of Okinawa (5–10 Sv) to the east of Amami-OhshimaIsland (20 Sv). Comparing the reanalysis with independent observations on the south-east slope of the Amami-Ohshima Island indicates that the root mean square differ-ences (RMSDs) are 0.076 (0.037) m/s in the period of December 1998 to November1999 (November 1999 to November 2000) respectively. The reanalysis field has a bias(3.1 Sv) of the volume transport of the RCS and the RMSD (3.5 Sv) which is largerthan the observed variability (2.81 Sv). Surface velocity and the Kuroshio axis southof Japan are also examined. Comparison of the reanalysis and ADCP data gave maxi-mum RMSD of 0.749 (0.271) m/s in the strong (weak) current regions, respectively.The annual mean value of the axis error is 19 km in 1998. The RMSD of the error is atmost 50 km, in 294 cases in the observation period, which is smaller than the ob-served root mean square variability of the axis (64 km).

Kuroshio, especially the upstream area and south of Ja-pan. It has been reported that ocean states in the upstreamarea of the Kuroshio are important in understanding themechanism of and to predict the variability of theKuroshio south of Japan (e.g., Kawabe, 1980; Saiki,1982). A large difference between the Kuroshio volumetransport in the East China Sea and the transport south ofJapan has also been reported (e.g., Nitani, 1972). Recentobservations show that the Ryukyu Current System (RCS),which flows in the east of Okinawa and Amami-OhshimaIsland, compensates the difference in the volume trans-port (e.g., Zhu et al., 2003; Ichikawa et al., 2004). Thehorizontal structure of volume transport of the RCS isshown by the assimilation. The result of the assimilationalso supplies information on errors in the system. We usethe results of the assimilation (reanalysis) as a referencefor comparing and validating the predicted ocean state in

1. IntroductionIt has recently been recognized that it is crucial to

synthesize the results of sustained observation and ad-vanced models for successful prediction of the ocean andclimate. We have developed an ocean data assimilationsystem (COMPASS-K: Comprehensive Ocean Modeling,Prediction, Analysis and Synthesis System in the Kuroshioregion) for understanding the ocean state in the NorthPacific (see also Kamachi et al., 2001) and for operationaluse in the Japan Meteorological Agency (JMA).

In this paper we introduce the system and comparethe result of assimilation with independent observationsto validate the variability of ocean state related to the

304 M. Kamachi et al.

the accompanying paper by Kamachi et al. (2004). Thispaper thus sets out the basis for the validity of the predic-tion experiments reported in Kamachi et al. (2004).

The system is for operational use in JMA, so a sim-ple time-retrospective nudging method (Kamachi et al.,2001) is adopted as an assimilation method. This nudg-ing method controls the values of model variables so thatthey tend gradually to the observational values. Thoughthis method does not guarantee the optimum state, still itis useful for operational system as the first step. Thoughrecent computer and software developments make it pos-sible to adopt advanced assimilation methods, we startedwith a simple system without a heavy computational bur-den (see Wunsch, 1996; Bourtier and Courtier, 1999;Bennett, 2002; Kalnay, 2003 for advanced methods).

This paper is organized as follows: the analysis meth-odology is mentioned in Section 2. Section 3 deals withthe validation of data assimilation products. Section 4summarizes our findings.

2. Analysis Methodology: Ocean Model, Data, andAssimilation SystemThe COMPASS-K system consists of an ocean gen-

eral circulation model (OGCM), analyses of observeddata, and assimilation techniques (see also Kamachi etal., 2001). The model is an eddy permitting version ofthe OGCM of the Meteorological Research Institute(MRI). The area covered by the model calculation is from119°E to 109°W, and 12.5°N to 55.5°N in the North Pa-cific. Primitive equations of momentum and tracers (tem-

perature and salinity) are solved with finite differenceschemes (Bryan, 1969; Kimura and Endoh, 1989).Arakawa’s B-grid system is adopted with variable gridsizes. The spacing gradually changes from 1/4° × 1/4° inthe region from 23°N–45°N and 120°E–180° to 0.5° inlatitude and to 1.5° in longitude outside of the region.The model has 21 vertical levels. Maximum depth is 4500m. There are 5 levels in the upper 200 m. Coastal andbottom topography data of ETOPO5 (“Earth Topography-5 Minute” provided by the U.S. Naval OceanographicOffice) was used to specify the model bottom topogra-phy. The ETOPO5 depth values were rounded to the depthof the nearest vertical level in each node of the modelgrid. A slope-advective bottom topography scheme is alsoadopted for the correct calculation of flows around a steepbottom topography (Ishizaki and Motoi, 1999). The gen-eralized Arakawa scheme is adopted for the momentumadvection terms (Ishizaki and Motoi, 1999). Biharmonicand harmonic types are adopted for viscous and diffusionterms, respectively. An implicit scheme is adopted for thevertical diffusion and dissipation. The values of the coef-ficients of the viscosity and diffusivity are changed ac-cording to the Richardson’s 4/3-power low (Richardsonand Stommel, 1948; Stommel, 1949). The model uses arigid lid approximation. The surface height is not a vari-able in the model, so a vertical projection from surfaceheight (altimetry) to subsurface temperature and salinityfields is adopted. The general frame is the same as theMRI-OGCM reported by Kimura and Endoh (1989).

The model was spun up for 180 years to obtain a

Fig. 1. Geographical distribution of the vertical profile of the linear regression coefficients between T/P altimeter and tempera-ture (solid line)/salinity (dashed line) in the large scale.

Kuroshio Reanalysis and Validation 305

statistical equilibrium state. The initial conditions havehorizontally uniform profiles of climatological tempera-ture (T) and salinity (S) fields in each model depth, andno-motion. Climatological monthly mean wind stress isadopted as momentum forcing (Hellerman andRosenstein, 1983). The time interpolation scheme givenby Killworth (1996) is adopted to calculate wind stressin each time step from climatological monthly values. Atthe northern and southern boundaries and the sea surface,temperature and salinity are restored to the climatologicalmonthly mean values given by Levitus (1982). After 180years of the spin-up, the model was spun up for 7 yearsusing a seasonal wind stress averaged for 1980–1998 of

NCEP daily wind stress (Kalnay et al., 1996). The modelwas then integrated from 1980 to 2001 using the NCEPdaily mean wind stress. The same wind stress is also usedfor the data assimilation experiment explained below.

When we use a rigid-lid OGCM and an assimilationmethod such as nudging, we need a statistical evaluationof the subsurface temperature and salinity fields. Tem-perature and salinity fields were calculated from theTOPEX and in situ (ship and float) data using a four-di-mensional space-time optimum interpolation and verti-cal projection (Kuragano et al., 2001). Meso- and large-scale sea surface height anomalies (SSHAs) were ex-tracted from the TOPEX altimeter data by an along-track

Fig. 2. Block diagram of the assimilation system. GPV means grid point value, and GTS means Global TelecommunicationsSystem.


filter (Kuragano and Kamachi, 2003). Space and timedecorrelation scales are calculated from each meso- andlarge-scale SSHAs. The decorrelation scales are differ-ent for meso- and large-scale SSHAs. The scales are alsodifferent for different regions. Each meso- and large-scaleSSHAs were optimally interpolated to the horizontalmodel grids with the space and time decorrelation scalesin each regions (Kuragano and Kamachi, 2000). For ex-ample, the typical meso-scale (large-scale) spacedecorrelation scale used in this work is 170–260 (about1000) km, and the time scale is 24–25 (about 60) days inthe Kuroshio south of Japan. The sea surface heightanomalies were then projected to subsurface temperatureand salinity anomalies using regression coefficients thathad been statistically calculated with the scale decom-posed (large- and meso-scale) data of the World OceanAtlas 1994 (Levitus and Boyer, 1994; Levitus et al., 1994).Figure 1 shows an example of the geographical distribu-tion of the vertical distribution of the linear regressioncoefficients of altimetry versus temperature and salinityin the large scale. The coefficients have maximum valuesat about 400 m depth south of Japan. The projected large-scale temperature anomalies were adjusted to in situ tem-perature anomaly using an optimal interpolation. Finally,adding the meso- and large-scale subsurface values to theclimatological temperature and salinity of the WorldOcean Atlas 1994 (Levitus and Boyer, 1994; Levitus etal., 1994), temperature and salinity in each grid are ob-tained from the surface to 1750 m every 5 days for 1993to 2001 (see also Kuragano et al., 2001; Kuragano andKamachi, 2004). The projected temperature and salinityfields have a realistic distribution; nevertheless, theKuroshio front is weaker and its width is broader than

observations report. A more realistic frontal structure maybe obtained if the statistics are calculated in smaller re-gions and the position of the front can be detected fromthe difference of distribution of the decorrelation scales.Calculated 5-day mean values of temperature and salin-ity are adopted for the assimilation experiments in thestudy.

A time-retrospective nudging method is adopted forthe assimilation in order to prevent the delay of modelresponse due to a nudging method. The time scale of thedelay is proportional to the inverse of the coefficient ofthe nudging term: 5 days is adopted as the time scale ofthe inverse of the coefficient. We therefore started to in-sert the observed data into the model from 3 days beforethe observation time in the time-retrospective nudgingmethod used in this work (see Kamachi et al. (2001) forthe method). Figure 2 shows a block diagram of the analy-ses and data flows in the system.

3. Historical State Estimation (Reanalysis) and Vali-dationThe assimilation (reanalysis) experiment is con-

ducted with historical data from 1993 to 2001. Thereanalysis data from 1993 to 2001 are compared with thedata of independent observations for velocity and vol-ume transport of Ryukyu Current System (RCS) off thesoutheast coast of the Amami-Ohshima Island, for veloc-ity south of Japan, and for the position of Kuroshio axis.

3.1 East of Amami-Ohshima Island (Ryukyu Current Sys-tem)In this subsection we compare the reanalysis veloc-

ity fields with the observed fields. We first show the struc-

Fig. 3. Climatological mean stream function in summer, August 28, derived from the assimilation experiment. Contour intervalis 5 Sv.


ture of the volume transport of the RCS calculated fromthe reanalysis. Figures 3 and 4 show a climatological meanstream function, which is a mean value of the entire pe-riod (1993 to 2001) calculated for each 5 days from thereanalysis data, (Fig. 3) and the velocity field at 600 mdepth (Fig. 4) in summer (August 28) derived from thereanalysis dataset. East of Okinawa and Amami-OhshimaIsland there is a northeastward flow, viz., the RCS (Fig.4). The RCS is connected to the flow of the subtropicalgyre, partly flowing from the Izu ridge. The volume trans-port of the RCS gradually increases from south-east ofOkinawa (5–10 Sv) to the east of Amami-Ohshima Is-land (20 Sv). The volume transport is about 20 Sv in theEast China Sea. The flows in the East China Sea and RCSjoin to the east of Tokara Strait. The total volume trans-port of the flows in the East China Sea and RCS is about40 Sv east of Tokara Strait, gradually increasing (e.g., 50Sv south-east of Cape Toi) in the downstream direction.The transport of the RCS east of Amami-Ohshima Islandis similar to the mean value (20 Sv) in the recent obser-vations described below, and the value (25 Sv) in the highresolution modeling using the Earth Simulator reportedby Yoon (2003).

We compare the velocity in the reanalysis data withthe velocity data obtained by mooring measurementsalong the AD-line, which is located on the south-east slopeoff Amami-Ohshima Island (Zhu et al., 2003; Ichikawaet al., 2004). The locations of the mooring sites are shownin Fig. 5 (see also figure 1 of Kamachi et al. in this issuefor geographic names). The mean velocity distribution,perpendicular to the AD-line, of the reanalysis in the pe-riod December 1998 to November 2000 in a vertical sec-tion along the AD-line shows that the Kuroshio has amaximum velocity 0.4 m/s in the East China Sea. On the

other hand, a mid-depth northward current has a maxi-mum core (velocity about 0.3 m/s) in 500 to 600 m depthon the south-east slope of Amami-Ohshima Island. Thebarotropic component of the east of the mid-depth north-ward current is about 0.1 m/s. This barotropic currentflows from the eastern area of Taiwan to Ryukyu Islands,but the mid-depth northward current does not flow fromthe south of Okinawa (though the figure is omitted, wecan see the velocity distribution in the climatological fig-ure, Fig. 4, in which the longer current arrows appear fromthe east of Okinawa to the north-east direction).

Reanalysis products are linearly interpolated, in each0.1 degree in latitude and 0.087 degree in longitude, tothe point on the AD-line. We show the distributions ofthe annual mean velocity values of the reanalysis data(Figs. 6(a) and (b)) and of the moored current meter data(Figs. 6(c) and (d)), along the AD-line, on the south-eastslope of the Amami-Ohshima Island for two periods (De-cember 1998 to November 1999 and November 1999 toNovember 2000). The root mean square differences(RMSDs) are 0.076 (0.037) m/s in the period of Decem-ber 1998 to November 1999 (November 1999 to Novem-ber 2000), respectively. Velocity data of the reanalysisshow the similar position, thickness and width of the ve-locity core of the mid-depth northward current (about 300to 600 m depth, 27.8 to 27.9°N in latitude) to the obser-vations, though the maximum velocities and their timevariation from the first period (December 1998 to No-vember 1999) to the second period (November 1999 toNovember 2000) do not coincide with the observation.For example, the observed data contain a maximum ve-locity larger than 0.4 m/s in the period from December1998 to November 1999, but the reanalysis data is below0.3 m/s. In the following period, from November 1999 to

Fig. 4. Climatological mean velocity at 600 m depth in sum-mer, August 28, derived from the assimilation experiment.

Fig. 5. Map showing the locations of moored current-meterobservations (�) along hydrographic observation line (AD-line). The isobaths of 0, 1000 and 2000 m are also shown.


port across the AD-line calculated from the reanalysisvelocity fields with the time series calculated from themoored current meter observations (Fig. 7) which is in-dependent of the reanalysis. The volume transport is cal-culated from the reanalysis velocity fields in the samecross section (27.5–27.9°N, 0–1500 m depth) as the ob-servations. The reanalysis, in the period from December1999 to August 2001, has a mean value of 10.9 Sv and astandard deviation of 2.2 Sv. The observations have asmaller mean value (7.8 Sv) and a larger standard devia-tion (2.81 Sv). Time variation is generally similar in eachcase. Reanalysis data shows a larger transport (mean value20.8) on the slope of the south-east of Amami-OhshimaIsland (28.1–27.1°N, surface-bottom). This means that thehorizontal center of the barotropic northward flow is inthe east of the mooring sites. The root mean square dif-ference of the two datasets is 3.5 Sv. The reanalysis fieldhas greater bias (3.1 Sv) and difference than the observedvariability (2.81 Sv).

3.2 South of Japan (volume transport, velocity especiallyrelated to mesoscale eddy, and Kuroshio axis)In the above discussion we compared mainly the

velocity field in the upstream region (esp., along the AD-

Fig. 6. Velocity distributions after assimilation ((a) and (b)) and of observations ((c) and (d)) in a vertical section south-east ofthe Amami-Ohshima Island along the T/P ground path 214 (AD-line). (a) and (c): mean values in December 1998 to Novem-ber 1999. (b) and (d): mean values in November 1999 to November 2000. Contour interval is 10 cm s–1.

Fig. 7. Time series of the transport calculated from the veloc-ity fields of assimilation (solid line) and of moored currentmeter (broken line) in 27.45–27.90°N, 0–1500 m depthalong the T/P ground path 214 (AD-line) southeast ofAmami-Oshima Island.

November 2000, the observed maximum value decreasesto 0.3 m/s, and the reanalysis values have a similar value.Therefore the interannual variability of the reanalysis datais less than the observation.

We next compare time series of the volume trans-


Fig. 8. Distributions of velocity fields (mean value in 70–110 m depth) of the reanalysis (black arrows). Distributions of velocityfields of the ADCP observations are also shown in green (red) arrows for the strong (weak) current regions. (a): Cruise 2A,(b): Cruise 15B, (c): Cruise 17A, and (d): Cruise 5A.

therefore the baroclinic structure may be weakened. As aresult, the velocity field has weaker vertical shear andhas a larger vertical decorrelation scale in deeper layers.This may cause the above-mentioned smaller upper 1000m transport (see also Isobe et al., 2004).

Next we compare velocity fields of the reanalysiswith independent ADCP data observed by merchant ships(Kaneko et al., 2001). ADCP data are interpolated withthe decorrelation scale used in the assimilation system,and the reanalysis data are interpolated linearly onto theobserved point and time.

For the comparison of the surface velocity fields ofthe reanalysis and ADCP, both data sets are averaged inthe range of 70 to 110 m depth in which main part of theADCP data are obtained. We divided the velocity datasetsinto two parts: a strong current region (larger than 0.5m/s) and a weak current region (smaller than 0.5 m/s).The difference between the reanalysis and the ADCP ob-servations is calculated for each cruise. Here we showfour cases: Cruise 2A (15B) has the minimum (maximum)RMSD of 0.130 (0.749) m/s in the strong current regionfor all cruises. Cruise 17A (5A) has the minimum (maxi-

line). Now we compare the velocity and related fields(volume transport and Kuroshio axis) of the reanalysiswith independent observations. For example, the valuesand latitudinal dependence of the volume transport acrossthe 137°E JMA observation line is captured well in thereanalysis (RMSD is 6.7 Sv and the correlation coeffi-cient of the latitudinal distribution of the transport is 0.92with 20 degrees of freedom for the case of January, 2001(Sugimoto et al., 2003; Kamachi et al., 2003)).

We next examine the seasonal variation of the vol-ume transport along the ASUKA line (Imawaki et al.,2001). We calculated the volume transport from the sur-face to 1000 m across the ASUKA line (southern end26°N) from the reanalysis and observation datasets. Theobserved transport is calculated every 10 days, but thereanalysis every 5 days. Reanalysis gives a smaller meanvalue of the transport (30 Sv) than the observation (42Sv), because the horizontal structure of the density gra-dient is smoothed in the decorrelation scale after the op-timum interpolation, so the velocity field is weaker (seealso Kamachi et al., 2003). The strong horizontal densitygradient cannot be maintained on the western boundary,


mum) RMSD of 0.133 (0.271) m/s in the weak currentregion for all cruises. We show the velocity vectors inFig. 8 and the latitudinal dependences of the east-westand north-south velocity components which are averagedfrom 70 to 110 m depth in Fig. 9 for each cruise. Figure 9also shows the maximum and minimum root mean squaredifferences. The maximum root mean square differenceis 0.749 (0.271) m/s in the strong (weak) current regions.In relation to the strong current region, the velocity fielditself as well as the Kuroshio axis in Cruise 2A is repre-sented well in the reanalysis. On the other hand, thereanalysis did not represent the flow pattern in which the

Kuroshio has a meander east of the Izu ridge (Cruises015B and 017A). The difference then is large. In the weakcurrent region, these figures show that the disturbed ve-locity values are difficult to represent exactly in the as-similation system. This may be solved by combiningTOPEX/POSEIDON and ERS satellite altimetry data withthe different space-time resolution, and of an advancedassimilation method that effectively propagates observa-tion information to a different area and time. These arematters for future study.

All the values of the velocity components in thereanalysis and observations are shown in Fig. 10, whichshows a scatter diagram of the velocity fields of the ADCPobservation (vertical axis) and of the reanalysis product(horizontal axis). Correlation coefficients are 0.74 (0.64)in the east-west (north-south) component. The east-westvelocity component in the reanalysis is larger than theobservation, and vice versa for the north-south compo-nent. Comparing each velocity data point, the reanalysisdata give a smaller speed (i.e., absolute value).

In the above discussion, we examined the differenceof the reanalysis and the observations about velocity andvolume transport. The latitudinal position of the Kuroshioaxis derived from the surface velocity field of thereanalysis is also be compared with observations, becausewe will discuss prediction experiments about the Kuroshioaxis in the accompanying paper (Kamachi et al., 2004).The Kuroshio axis is calculated, every 10 days, with ab-solute surface velocity field which is derived from T/Pand surface drifter (see Ambe et al., 2004 for detaileddescription of the analysis methodology) for observations.For this comparison, the Kuroshio axis is also calculatedfrom the maximum surface velocity field of the reanalysis.The axis of the reanalysis is similar to the observationsbut not in the eddy and smaller scale phenomena. This is

Fig. 10. Scatter diagram of the mean velocity fields between70 and 100 m depth of the ADCP observation (vertical axis)and of the assimilation product (horizontal axis). (a): east-west component, (b): north-south component. Correlationcoefficients are 0.74 (0.64) in the east-west (north-south)component. Data in all cruises are plotted.

Fig. 9. Latitudinal distribution of the velocity fields of theADCP (dotted line) and of the reanalysis product (solid line).Left column shows the east-west component, and right col-umn the north-south component in each cruise (Cruises 2A,15B, 17A, and 5A from top to bottom figures). Each panelshows the root mean square differences of the ADCP andassimilation data in the strong current region (∆uh) and theweak current region (∆ul).


due to the data filtering in the process of assimilation.The axis error of the reanalysis is obtained using the ob-servation and reanalysis data which are interpolated ineach 10 km. The axis error is calculated, in each 10 km,from the area between the Kuroshio axes in the reanalysisand observation divided by the length of the axis in theobservation from 130°E to 143°E (see Kamachi et al.,2004). The axis error is different in each year. For exam-ple, in 1998, the annual and regional (from 130°E to143°E) mean axis error is 19 km. The RMSD of the erroris at most 50 km which is calculated from all 294 cases inthe observation and reanalysis periods. The value issmaller than the observed root mean square variability ofthe axis (64 km).

4. SummaryWe have described an operational ocean data assimi-

lation system, and compared the dataset of reanalysis withindependent observations of velocity and related fieldsin the Kuroshio (esp. Ryukyu Current System and sur-face velocity field south of Japan).

The reanalysis dataset shows the horizontal struc-ture of the volume transport of the Ryukyu Current Sys-tem. The RCS is connected to the flow of the subtropicalgyre, partly flowing from the Izu ridge. Volume transportof the RCS gradually increases from south-east ofOkinawa (5–10 Sv) to the east of Amami-Ohshima Is-land (20 Sv). The volume transport is about 20 Sv in theEast China Sea. The flows in the East China Sea and RCSjoin east of Tokara Strait. The total volume transport isabout 40 Sv east of Tokara Strait, gradually increasing(e.g., 50 Sv in the south-east of Cape Toi) in this system.We compared the reanalysis data with independent ob-servation data from mooring systems on the south-eastslope of Amami-Ohshima Island. The RMSDs are 0.076(0.037) m/s in the period of December 1998 to Novem-ber 1999 (November 1999 to November 2000). TheRMSD of the time series of the volume transport of theRCS is 3.5 Sv. The reanalysis field has greater bias (3.1Sv) and difference than the observed variability (2.81 Sv).This may be due to the variability of meso-scale eddies.

We then compared the reanalysis with independentobservations about the surface velocity field south of Ja-pan. The values and latitudinal dependence of the vol-ume transport across the 137°E JMA observation line arecaptured well in the reanalysis: RMSD is 6.7 Sv and thecorrelation coefficient of the latitudinal distribution ofthe transport is 0.92 with 20 degrees of freedom for thecase of January, 2001. The reanalysis dataset has also beencompared with independent ADCP data south of Japan.Maximum root mean square difference is 0.749 (0.271)m/s in the strong (weak) current regions for all cruises.In relation to the strong current region, velocity field aswell as the Kuroshio axis is represented well in the

reanalysis, though the reanalysis did not represent the flowpattern in which the Kuroshio has a meander east of Izuridge. The velocity values in the weak current region aredifficult to represent exactly in the assimilation system.

The axis error of the Kuroshio is examined, and ithas different values in each year. For example, the an-nual and regional (from 130°E to 143°E) mean axis erroris 19 km in 1998. The RMSD of the error is at most 50km which is calculated from all 294 cases in the observa-tion period. The value is smaller than the observed rootmean square variability of the axis (64 km).

Comparison of velocity and related fields shows goodagreement in terms of mean and low frequency variabil-ity, though meso-scale eddy very much affects the short-range variability. Additional numbers of observations oradvanced assimilation methods, such as the adjointmethod and the adaptive variational method (Zhu andKamachi, 2000), additional satellite altimetry (e.g., ERS)with the different space-time resolution (Kuragano andKamachi, 2003), and the Eddy Synthetic Modeling Ap-proach (ESMA), which assimilates a kind of bogus datainto each eddy (Uboldi, 1998), may be able to solve theproblem.

Recent developments of data assimilation are adoptedin operational centers. Data assimilation is not only aninterpolation/extrapolation of observed data but a totalsystem for now-/fore-casting and understanding of oce-anic and climate phenomena. We need to continue to de-velop each component of the system to give us informa-tion on the heat content of the ocean, heat transport, anda grasp of its variability for climate research. Our systemis also a prototype for the GODAE (Global Ocean DataAssimilation Experiment) project.

AcknowledgementsThe main part of this work is supported by CREST

(Core Research for Evolutional Science and Technology)of Japan Science and Technology Corporation (JST). Weappreciate the members of the project for their helpfuldiscussion. We also appreciate three anonymous review-ers, guest editor Prof. M. Ikeda, and guest editor in chiefProf. S. Imawaki in this special section of the Journal ofOceanography for their fruitful comments. The TOPEX/POSEIDON altimeter data were provided by the NASAPhysical Oceanography Distributed Active Archive Centerat the Jet Propulsion Laboratory, California Institute ofTechnology. We used the free software “GrADS (GridAnalysis and Display System) Version 1.7 Beta 6” to pro-duce the figures. One of the authors (MK) of part of thiswork is also supported by The Category 7 of MEXTRR2002 Project for Sustainable Coexistence of Human,Nature and the Earth, and by the Regular Research Fundin the Meteorological Research Institute.


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