Ocean Dynamics (2017) 67:713–728
DOI 10.1007/s10236-017-1056-1

Assimilation of high-resolution sea surface temperature data
into an operational nowcast/forecast system around Japan
using a multi-scale three-dimensional variational scheme
Yasumasa Miyazawa 1 & Sergey M. Varlamov 1 & Toru Miyama 1 & Xinyu Guo 1,2 &Tsutomu Hihara 1 & Keiji Kiyomatsu 3 & Misako Kachi 4 & Yukio Kurihara 4 &Hiroshi Murakami 4

Received: 21 November 2016 / Accepted: 4 April 2017 / Published online: 5 May 2017
# The Author(s) 2017. This article is an open access publication

Abstract A multi-scale three-dimensional variational (MS3DVAR) scheme is developed to assimilate high-resolution
Himawari-8 sea surface temperature (SST) data for the firsttime into an operational ocean nowcast/forecast systemtargeting the North Western Pacific, JCOPE2. MS-3DVAR
improves representation of the Kuroshio path south ofJapan, its associated sea level variations, and temperature/salinity profiles south of Japan, the Kuroshio/Oyashio mixed
water region, and the Japan Sea as compared to those of the
products by the traditional single-scale 3DVAR. Validation
results demonstrate that MS-3DVAR well assimilates thesparsely distributed in situ temperature and salinity profiles
data by spreading the information over the large scale andby representing the detailed information near the measurement
points. MS-3DVAR succeeds to assimilate the Himawari-8
SST product without noisy features caused by the cloud effects. We also find that MS-3DVAR is more effective for
estimating oceanic conditions in regions with smaller mesoscale variability including the mixed water region and Japan
Sea than in south of Japan.

Responsible Editor: Emil Vassilev Stanev* Yasumasa Miyazawa
miyazawa@jamstec.go.jp

1

Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan

2

Ehime University, Matsuyama, Ehime 790-8577, Japan

3

Forecast Ocean Plus, Inc., Chiyoda-ku, Tokyo 102-0074, Japan

4

Japan Aerospace Exploration Agency, Ibaraki, Tsukuba 305-8505,
Japan

Keywords Himawari-8 sea surface temperature data .
Multi-scale three-dimensional variational scheme .
Operational ocean nowcast/forecast system

1 Introduction
The operational ocean forecasting based on numerical ocean
modeling and data assimilation is advancing toward operating
the products with higher spatial resolution and temporal frequency (Varlamov et al. 2015). Increasing computational resources based on evolving high-performance computing technology facilitates developments of numerical ocean models
with higher resolution. Skillful nowcast/forecast with highresolution models requires enough amounts of the observation
data well constraining the model state through data
assimilation.
Coverage and density of the current satellite altimeters formonitoring the sea surface height anomaly (SSHA) have alimitation of the minimum observable spatial scale of
100 km (Fu and Ferrari 2008). To capture whole activity ofthe mesoscale and submesoscale eddies ranging from O(100 km) to O (1 km) scales, we need to fully utilize thecurrent available observation data together with developing
new observation facilities including the wide-swath radar interferometry satellites (Fu and Ubelmann 2014; Ichikawa
2014).
The satellite sea surface temperature (SST) is anotherimportant data source for monitoring oceanic condition.
The high-resolution SST products obtained from the infrared radiometers well detect the front variability if
cloud masking contaminates signals included in the data.
A SST product obtained from the infrared sensor of therecent launched Japanese geostationary satellite

714

BHimawari-8^ allows relatively high-frequency measurement of SST in a wide area of Western Pacific every
10 min with horizontal resolution 0.02° (Kurihara et al.
2016). Temporal composite of the high-frequency snapshot measurements removes the missing and contaminated regions caused by the cloud noise more effectively
than the previous infrared SST products including theAdvanced Very High-Resolution Radiometer (AVHRR).
In the present study, we develop an assimilation scheme
suitable for the Himawari-8 SST data on an operational
ocean nowcast/forecast system in North Western Pacificocean: JCOPE2 (the real-time nowcast/forecast information is available from the website http://www.jamstec.go.
jp/jcope/; also see Miyazawa et al. 2009 for a detailed
description), which originally assimilates SSHA, MultiChannel SST resampled with coarse resolution from
AVHRR (May et al. 1998), and in situ temperature andsalinity profile (T/S) data. This is the first application of
the Himawari-8 SST product to operational ocean
nowcast/forecast systems.
The current version of JCOPE2 adopts the threedimensional variational (3DVAR) data assimilation
scheme assuming a horizontal single scale of approximately 100 km in the background error covariance (Miyazawa
et al. 2009), which significantly affects the spatial scale ofthe target phenomena represented by the data assimilation.
We compare the skills of a single-scale (SS-) and multiscale (MS-) 3DVAR implementations on JCOPE2. Li et al.
(2015a) describe an algorithm of the MS-3DVAR scheme,
and they demonstrate that it reasonably assimilates the observation data with both dense and coarse distributions in
the coastal oceans off Alaska (Li et al. 2013) and California(Li et al. 2015b) coasts. Muscarella et al. (2014) briefly
report that both the dense satellite and coarse in situ data
are well assimilated into their ocean model in the Kuroshio
Extension region. Here, we examine in detail the effects of
MS-3DVAR on the skill of JCOPE2 south of Japan, in the
Kuroshio/Oyashio mixed water region (the mixed water
region), and in the Japan Sea.
Given that the high-resolution data are available withuniform distribution without any data missing regions,SS-3DVAR with a horizontal scale comparable to the data
resolution effectively extracts fine-scale information fromthe data. In this case, skills of MS-3DVARs and SS3DVARs are comparable to each other (Li et al. 2015a).
In real situations, however, the distributions of the available data are not uniform; in particular, the high-resolution
infrared SST including the Himawari-8 product involves
the data missing areas due to the cloud noise (e.g.,
Miyazawa et al. 2013), and in situ temperature and salinity
data are obtained with quite coarse resolution (e.g.,
Miyazawa et al. 2012). The MS-3DVAR scheme effectively assimilates the localized and patchy dense-distributed

Ocean Dynamics (2017) 67:713–728

observation data without unrealistic smoothing and wellspreads the information of the sparse-distributed data (Liet al. 2015a). In terms of this viewpoint, we investigate
skills in SS-3DVAR cases with different horizontal scales
and compare their skills with that of MS-3DVAR.
The paper is organized as follows. The model and observation data used in this study are described in Sect. 2.
Model skills for the SST reproducibility, the Kuroshio pathand its related sea level variations, and the temperature/salinity profiles are shown in Sect. 3. We examine differences in the skills between the SS-3DVAR and MS3DVAR schemes by checking the fitting to the assimilated
SST data (Sect. 3.1), by validating the results using independent observation data (Sect. 3.2), and by diagnosing theresults in terms of intercomparison among sensitivity experiments (Sect. 3.3). We also examine sensitivity ofchoices of three SST products: Himawari-8, Merged
Satellite and In situ Data Global Daily SST (MGDSST;
Kurihara et al. 2006), and Moderate Resolution Imaging
Spectroradiometer (MODIS)-SST (Hosoda et al. 2007) in
Sect. 3.4. Final section is devoted for summary anddiscussion.

2 Methods and data
The forward ocean model of JCOPE2 driven by the
NCEP/NCAR reanalysis data (Miyazawa et al. 2009),
based on the Princeton Ocean Model with generalized coordinate of sigma (Mellor et al. 2002), covers the Western
North Pacific (10.5–62° N, 108–180° E; Fig. 1), and has ahorizontal resolution of 1/12° and 46 vertical active levels.
For this study, the model is modified to involve some recent
advances in the ocean modeling as follows: (1) replacementof the baroclinic pressure gradient scheme from the fourthorder (McCalpin 1994) to the rotated schemes (Thiem andBerntsen 2006), (2) replacement of the turbulent closure
model from Mellor and Yamada (1982)’s level 2.5 modelwith surface wave breaking parameterization (Mellor andBlumberg 2004) to the Nakanishi and Niino (2009)’s modelwith a modification for oceanic applications by Furuichi
et al. (2012), (3) input of surface wind relative to ocean
current into the bulk formulae (Griffies et al. 2009), (4)use of MGDSST (Kurihara et al. 2006) for correction of
the surface heat flux, (5) addition of precipitation and evaporation data from the NCEP/NCAR reanalysis into the surface freshwater flux with correction to the monthly climatological salinity, (6) the enhanced precipitation along thecoasts caused by the coastal precipitation over a strip ofland 80 km wide, which represents the freshwater flux fromsmall rivers (Hirose 2011), and (7) the additional horizontal
viscosity (1000 m2 s−1) around the Tsugaru Strait controlling the volume transport of the Tsugaru warm current

Ocean Dynamics (2017) 67:713–728

715

Fig. 1 The JCOPE2 model
region. Shades denote the bottom
depths. Rectangles indicate the
3DVAR analysis regions in themodified JCOPE2. Numbers of
the regions are denoted bynumerals. Note that the data
assimilation is not applied to the
Okhotsk Sea part in the region 1(Uchimoto et al. 2007) and enhancement of the verticalviscosity/diffusivity (a lower bound value of 0.02 m2 s−1)
around the Bussol Strait representing the tidal mixing effect(Fujisaki et al. 2010).
The 3DVAR scheme on JCOPE2 implemented byMiyazawa et al. (2009) is modified to extract the information on shorter temporal and smaller spatial-scale
phenomena from the high-resolution observation data.
The data assimilation cycle period and horizontal gridinterval of the analysis are shortened from 7 to 2 days
and from 0.25° to 0.125°, respectively. SSHA, SST, andT/S data are assimilated into the model with time window parameters: 5, 1, and 5 days before and after the
analysis time, respectively. The temperature-salinity coupling empirical orthogonal mode functions (Fujii and
Kamachi 2003) for each subregion (Fig. 1) have beenrecalculated using the World Ocean Database 2013 andMarine Information Research Center Data Set 2001. To
accelerate the whole assimilation process, each costfunction minimization is separately conducted for each
subregion (Fig. 1) using different processors on multicore workstations.

The most critical change in the data assimilation process is use of multi-scale cost functions inspired by seriesof the recent MS-3DVAR studies (Li et al. 2013, 2015a,
2015b; Muscarella et al. 2014). Here, we introduce largescale and small-scale components of the analysis increments, δXL and δXS, respectively, for estimation of theanalysis temperature/salinity Xa from the first guess ones
Xf,
X a ¼ X f þ δX L þ δX S

ð1ÞThe first guess Xf from the model is also decomposed intolarge-scale and small-scale ones,
X f ¼ X Lf þ X Sf

ð2Þwhich are related to the decomposition of the dense observation data,
yd ¼ ydL þ ydS

ð3Þ

MS-3DVAR separately minimizes two types of cost functions for the large-scale and small-scale components,

716

Ocean Dynamics (2017) 67:713–728

J L ðδX L Þ ¼
þþJ S ðδX S Þ¼
þþ

1 T −1
δX L BL δX L
 2 T  −1   
1  d fRdL
H d X f −X Sf þ δX L −ydL
H X −X Sf þ δX L −ydL2 T 
−1  c  f 
1  c f
H X þ δX L −yc Rc þ H c BS H cT
H X þ δX L −yc
2ð4Þ

1 T −1
δX B δX S
2 S S  



 
T  −1
1
H d X f −X Lf þ δX S −ydS
RdS
H d X f −X Lf þ δX S −ydS2 T 
−1  c  f 
1  c f
H X þ δX S −yc Rc þ H c BL H cT
H X þ δX S −yc2ð5Þ

where BL and BS are the large-scale and small-scale background error covariance matrices, respectively; yd and yc are
the observation data with spatially dense and coarse distributions, respectively; Hd and Hc are operators projecting themodel variables to the dense and coarse observation variables,
respectively. RdL , RdS , and Rc are the observational error covariance matrices associated with ydL , ydS , and yc, respectively.
Additional terms HcBLHcT and HcBSHcT to the conventional
3DVAR cost function are the large-scale and small-scale representativeness error matrices. Practically, HcBLHcT could be
negligible (Li et al. 2015a) compared to Rc due to relatively
weak correlation among variables represented by BL. Note thatthe formulation of MS-3DVAR (2) and (3) is slightly different
from the original one represented by the cost function in an
incremental form (e.g., see equations 34 and 35 in Li et al.
2015a).
The background error covariance matrix B in the 3DVAR
scheme of JCOPE2 is characterized by a horizontal-scale parameter (approximately 100 km) representing the decorrelation
scale in the Gaussian function (Miyazawa et al. 2009).
Considering the typical horizontal scale of the mesoscaleeddies in North Western Pacific O (100 km) (Kuragano and
Kamachi 2000) and the detectable minimum scale on the analysis grid with 0.125° (~14 km), we define the large (for BL) andsmall (for BS) scale parameters as 100 and 30 km, respectively.
The high-resolution SST data from Himawari-8 (Fig. 2a),MGDSST, and Aqua/Terra MODIS are considered as thedense observation data in the present study. Gaussian smoothing filter with a spatial scale of 1.5° (approximately 170 km) isutilized for the decomposition of the model (4) and dense
observation (5) variables as in the previous studies (Li et al.
2015a, 2015b). T/S profile data are categorized as the coarse
observation containing the information on the various phenomena over the broad horizontal-scale range and are included in both the large-scale and small-scale cost functions (2)and (3). SSHA may also contain rich information on the various scale phenomena; however, the maximum spatial intervalof the satellite tracks exceeds 100 km at the present time, andthe observable spatial scale has a minimum limit of 100 km(Fu and Ferrari 2008). We thus include SSHA only in thecoarse observation term of the large-scale cost function. In
the present study, MS-3DVAR is applied to the subregions
2, 3, 4, 10, and 11 (Fig. 1) because of our particular interest
to the ocean current variability there.
The observation error covariance matrix is supposed as the
diagonal form. We assume that the error standard deviation
values of SSHA are 0.05–0.1 m (Table 1). The observation
error standard deviation of temperature T oerr including both insitu and satellite SST and salinity S oerr is specified as follows:
pﬃﬃﬃﬃﬃﬃﬃﬃ o
1−σT std ðn; zÞ; S oerr ðn; zÞpﬃﬃﬃﬃﬃﬃﬃﬃ¼ 1−σS ostd ðn; zÞ ð0 < σ < 1Þ

T oerr ðn; zÞ ¼

ð6Þ

where T ostd ðn; zÞ and S ostd ðn; zÞ are the standard deviation
values of observed temperature and salinity, respectively,
and depend on the 11 subregions (n) and 24 vertical levels(z) from 0 to 1500 m. σ denotes the normalized first guesserror parameter as represented in
pﬃﬃﬃ
pﬃﬃﬃ
f
f
T err
ðn; zÞ ¼ σT ostd ðn; zÞ; S err
ðn; zÞ ¼ σS ostd ðn; zÞ ð7Þ
ff
and S err
are the error standard deviation values ofwhere T err
the first guess temperature and salinity, respectively. Table 1
indicates σ values applied to the subregions (Fig. 1), which are
generally similar to those used in the previous studies (Usuiet al. 2006; Miyazawa et al. 2009).
After checking a preliminary assimilation result, we havedecided to enhance constraint to the SST observation in the
pﬃﬃﬃﬃﬃﬃﬃﬃ
regions 1, 5, 6, 7, 8, and 9: T oerr z¼0 ¼ SST errn 1−σT ostd
(SSTerrn = 0.5; Table 1). The large-scale representativeness error matrix (HcBLHcT) is simply ignored in the MS-3DVAR
implementation by following the discussion by Li et al.
(2015a). Effects of small-scale one HcBSHcT included in thelarge-scale cost function (2) are simply parameterized byslightly decreasing σ from 0.7 to 0.6 (Table 1).

Ocean Dynamics (2017) 67:713–728

717

Fig. 2 A snapshot of sea surface
temperature (shaded) on 18
January 2016 from a the
Himawari-8 daily composite, b
SS-3DVAR with 100-km scale, cMS-3DVAR, and d SS-3DVARwith 30-km scale. Thin blackcolored contours denotetemperature distributions with
1 °C interval

(a)

(b)

(c)

(d)

The modified JCOPE2 system is tested for both SS3DVAR and MS-3DVAR schemes assimilating the following
observation data: along-track SSHA data of the Ssalto/Duacs
altimeter products (Jason-2, Cryosat-2, Saral, HaiYang-2),
Himawari-8 SST data, and the Global Temperature-Salinity
Profile Program (GTSPP) temperature and salinity profiles(see Fig. 3a for the locations). To validate the products byusing the temperature and salinity data independent from theassimilation, we randomly extract 10% amount of the datafrom the GTSPP archive and do not assimilate them (Fig. 3b).
Several choices of the horizontal error covariance scale for
SS-3DVAR 100, 50, and 30 km are applied in the target regions 2, 3, 4, 10, and 11 for comparison to the result of MS3DVAR (Bmulti^) with a combination of 100 and 30-km
scales (Table 2). In other regions 1, 5, 6, 7, 8, and 9, zonal
and meridional horizontal scales are specified as 150 and
Table 1 The assimilation
parameters used for thedecomposed subregions

100 km, respectively, for SS-3DVAR according to a scaleestimate based on Kuragano and Kamachi (2000). Example
snapshots of SST on 18 January 2016 are shown in Fig. 2b(100 km), c (multi), and d (30 km).
In addition, other SST products MODIS-SST and
MGDSST are used in place of Himawari-8 SST for MS3DVAR (Table 2). The original SST products of Himawari-8
and MODIS have horizontal resolutions of 2 and 1 km, respectively. The assimilation of them is performed after
regridding to a 0.1° grid (e.g., see Fig. 2a for Himawari-8).
It is found that the MODIS data requires relatively rigorous
quality check (Miyazawa et al. 2013) and additional horizontal Gaussian smoothing with a 0.25° scale because they sometimes show serious noisy features. The assimilation of
MGDSST with horizontal resolution of 0.25° is performed
without regridding.

Regions

SSHAerr (m)SSTerrσ

σ, large scaleσ, small scale

Horizontal scales forzonal and meridional
directions (km)1, 5, 6, 7, 8, 9
2, 3, 4, 10, 11

0.1
0.05

0.5
1.0

0.7
0.7

–
0.6

–
0.7

150, 100
100–30, 100–30

718
Fig. 3 Numbers of in situ
temperature profiles within thegrids with 1/4° distance in the
GTSPP archive for the period
from 1 August 2015 to 1
May 2016 for the data used in thedata assimilation experiments (a)and for the data not used in them(b). Squares shown in a indicate
the regions for validation of themodeled temperature/salinity.
Labels shown in a denotelocations of the regions (south ofJapan (SOJ), mixed water region(MWR), the Japan Sea southern
region (JSS)). Labels shown in b
denote locations of the tide gauge
stations (Aburatsu (AB),Tosashimizu (TS), Muroto (MU),
Kurshimoto (KU), Uragami (UR),Miyakejima (MJ), Hachijojima(HJ), Saigo (SA), Sado (SD),
Fukaura (FK))

Ocean Dynamics (2017) 67:713–728

(a)

(b)

All experiments are started without data assimilation fromthe initial condition same as the FRA-JCOPE2 reanalysis data
(Soeyanto et al. 2014) on 1 June 2015, and data assimilation isapplied from 1 August 2015. The daily mean assimilation(analysis) products for a period from 3 August 2015 to 1
May 2016 are analyzed.

3 Results and discussion
3.1 Fitting to the assimilated sea surface temperature data
Figure 4a, d, g indicates that root mean square difference(RMSD) between the modeled and assimilated Himawari-8

Ocean Dynamics (2017) 67:713–728
Table 2 Cases of the assimilation
products

719

Case

Description

100 kmModified JCOPE2 with assimilation of Himawari-8 SST and a single horizontal scale of 100 km

50 km

As in B100 km^ except for a single horizontal scale of 50 km in the regions 2, 3, 4, 10, and 11

30 km
30 km nosh

As in 100 km except for a single horizontal scale of 30 km in the regions 2, 3, 4, 10, and 11
As in B30 km^ except for no sea surface height anomaly assimilation in the regions 10 and 11

Multi
MGDSST

Modified JCOPE2 with assimilation of Himawari-8 SST and the horizontal multi-scales,
30–100 km for the regions 2, 3, 4, 10, and 11
As in Bmulti^ except for the assimilation of MGDSST

MODIS

As in multi except for the assimilation of MODIS-SST

(a)

(d)

(b)

(e)

(c)

(f)

Fig. 4 Daily sequences showing RMSD between the modeled andHimawari-8 SST data averaged south of Japan (a, b, c), in the mixed
water region (d, e, f), and in the Japan Sea southern region (g, h, i). a,d, g Total RMSD sequences. b, e, f, c, f, i Larger scale and smaller scale

RMSD sequences (colored thick curves), respectively. A scale of 150 km
is specified as a criterion of scale decomposition using Gaussiansmoothing filter. Thin black curves denote coverage rate of Himawari-8
SST data in the three subregions

720

(g)

(h)

(i )

Fig. 4 (continued)

SST data is relatively low in the three target regions for aperiod from August to October 2015. Coverage rate of the
SST data affected by the cloud condition is negatively correlated with RMSD owing to possible deterioration of the SST
data quality through the cloud effects (Kurihara et al. 2016). In
the mixed water and Japan Sea southern regions, the coveragerate frequently drops and RMSD also frequently increases in
winter season. Mean RMSD values of the MS-3DVAR case

Ocean Dynamics (2017) 67:713–728(green-colored curves) are less than 1 °C (0.55, 0.98, and 0.75(°C) averaged for south of Japan, the mixed water region, andthe Japan Sea southern region, respectively), which is comparable to RMSD between Himawari-8 (daily composite in 0.1°
grid) and in situ observation (GTSPP) SST data, 0.83 °C in the
JCOPE2 region (see Fig. 1 for the region), and RMSD of
0.59 K evaluated from the rigorous validation using the buoy
data by Kurihara et al. (2016). Mean absolute difference(BIAS) values of the MS-3DVAR case are +0.16, +0.12, and+0.02 (°C) for south of Japan, the mixed water region, and theJapan Sea southern region, respectively, and they are alsosimilar to the estimate using in situ data obtained by
Kurihara et al. (2016), +0.16 K, and our estimate in the
JCOPE2 region between the gridded Himawari-8 and in situ
data, +0.18 °C.
Sensitivity to horizontal single scales is not evident southof Japan (Fig. 4a). In particular, RMSD for larger scale features is not sensitive to the choices of the horizontal scales
there (Fig. 4b). The SS-3DVAR case with 100-km scale shows
largest deviation for smaller scale features among all the cases(Fig. 4c). RMSD of the MS-3DVAR case is basically similar
to that of the SS-3DVAR case with 30 km for the total(Fig. 4a), larger scale (Fig. 4b), and smaller scale (Fig. 4c)variations.
In the mixed water region, applying smaller horizontal
scales to SS-3DVAR leads to further reduction in total
RMSD (Fig. 4d) and small-scale RMSD (Fig. 4f), thoughlarge-scale RMSD is not much different among all the experiments (Fig. 4e). The MS-3DVAR case shows the skill comparable to that of SS-3DVAR with 30 km (Fig. 4d–f).
Applying single 100 and 50-km scales results in larger amplitude of small-scale RMSD in winter season as compared to the
30-km case and MS-3DVAR (Fig. 4f), suggesting insufficient
capture of the observed features as indicated by comparison ofFig. 2a, b.
Total RMSD values in the Japan Sea southern region of all
the SS-3DVAR cases increase more than that of the MS3DVAR case during a period from October 2015 to January
2016 (Fig. 4g). Both large (Fig. 4h) and small (Fig. 4i) scale
RMSD values of the SS-3DVAR cases also show similar increase, but the increase of the small-scale RMSD values(Fig. 4i) is more evident than that of the large-scale RMSD
values (Fig. 4h).
We find that relatively small-scale patches of anomalous
temperature and salinity sometimes appear under the cloudy
conditions in the cases of 50 and 30 km. Examples of the SST
patches in the Japan Sea in the case of 30 km are shown in
Fig. 2d (around 37° N, 133° E, and 39° N, 139° E) when theclouds contaminate the Himawari-8 SST distribution in theJapan Sea (Fig. 2a). The cloudy conditions in the Japan Sea
frequently appear especially in winter season, which is related
to the winter monsoon. SS-3DVAR with smaller scales of 30–
50 km results in overfitting to the SSHA data (residual ofOcean Dynamics (2017) 67:713–728

approximately 0.02 m under the prescribed error standard deviation estimate of 0.05 m) in the Japan Sea, and the anomalous temperature/salinity patches are sometimes created owing to the overfitting to SSHA and relatively weak constraint
by the other data including Himawari-8 SST, depending onamount of the available data affected by the cloud condition.
A sensitivity experiment of SS-3DVAR with 30-km scale
without SSHA assimilation in the Japan Sea (the regions 10
and 11; see Fig. 1 for the locations) reduces all the RMSD
indices (Fig. 4g–i), demonstrating the overfitting to SSHA in
the SS-3DVAR with 30-km scale.
3.2 Validation of the products using independent
observation data
We examine the Kuroshio path variation south of Japan bycomparing the modeled Kuroshio path positions defined as
the strongest gradient positions of SSH with the Kuroshio path
positions reported by the Japan Coast Guard. Time sequencesof RMSD in the latitudinal positions averaged from 133 to139 E (Fig. 5) indicate that all the cases show similar variations, implying that reproducibility of the Kuroshio path positions is not much sensitive to the choice of the horizontal
scales. This is not surprising because the reference Kuroshio
path positions mainly represent the smoothed large-scale information (e.g., see Fig. 9 in Miyazawa et al. 2013).
Temporally and spatially averaged RMSD values shown in
Table 3 suggest a best skill in the MS-3DVAR case among
all the cases. Figure 5 displays that the MS-3DVAR case results in more stable RMSD variation than the SS-3DVAR
cases as also indicated in standard deviation results (Table 3).
Daily tide gauge data (see Fig. 3b for the locations of thetide gauge stations) are compared with the modeled sea levelvariations (Tables 4 and 5) after the barometric correction

721
Table 3 RMSD and its standard deviation for the observed Kuroshio
path positions
Case

Mean RMSD
during the period (°)

Standard deviationof RMSD during
the period (°)

100 km

0.19

0.050

50 km
30 km

0.19
0.18

0.064
0.060

Multi
MGDSST

0.17
0.16

0.046
0.041

MODIS

0.18

0.055

(Kawabe 1989) and the tide killer filtering (Hanawa and
Mitsudera 1985). The corrected sea level variations along
the southern coasts of Japan are closely related with the
Kuroshio path variations with various timescales (Kawabe1989). All the cases show similar good skills (Table 4) except
for the sea level difference (SLD) between Kushimoto and
Uragami (KSUR), which is a useful indicator of theKuroshio path variation around these places (Kawabe 1989).
MS-3DVAR well reproduces two events of passing of theKuroshio small meander off Kushimoto and Uragami inAugust 2015 and February–March 2016 (not shown),
resulting in smaller RMSD (Fig. 5) and better reproducibility
in SLD (not shown) during the two periods than the SS3DVAR cases.
In the Japan Sea, sea level variability after barometric andtidal corrections is affected by the path variations of the
Tsushima Warm Current (Choi et al. 2004). Skills for reproducing the sea level variations at three points in the Japan Sea(see Fig. 3b for the locations) are generally similar among all
the cases (Table 5). Note that RMSD at the Saigo station (SA)increases in the SS-3DVAR cases with 50 and 30-km scales
and exclusion of SSHA assimilation in the 30-km case reduces RMSD, suggesting that the overfitting to SSHA causes
the relatively large deviation from the observed sea level variation there. Around SA, the mesoscale eddy activity is
Table 4 Skills of the SS-3DVAR and MS-3DVAR cases for sea level
data along the south coast of Japan (see Fig. 3b for the locations)Fig. 5 Daily sequences showing RMSD averaged from 133° E to 139° E
in latitudes between the modeled and observed Kuroshio path positions.
The modeled Kuroshio path positions are defined as positions of thestrongest geostrophic current calculated from SSH gradient withminimum kinetic energy amplitude of 0.4 m2 s−2

AB
TS
MU
KS
UR
MJ
HJ
KS-UR

100 km

50 km

30 km

Multi (100/30 km)

0.84, 0.07
0.79, 0.07
0.74, 0.08
0.74, 0.07
0.84, 0.07
0.79, 0.14
0.88, 0.18
0.55, 0.04

0.81, 0.07
0.76, 0.07
0.70, 0.08
0.71, 0.07
0.79, 0.08
0.86, 0.13
0.88, 0.20
0.44, 0.05

0.84, 0.06
0.77, 0.07
0.69, 0.08
0.74, 0.07
0.81, 0.08
0.86, 0.13
0.89, 0.17
0.51, 0.05

0.83, 0.07
0.80, 0.07
0.75, 0.08
0.76, 0.06
0.82, 0.06
0.82, 0.13
0.88, 0.17
0.70, 0.04

Numerals in left (right) hand side denote correlation (RMSD in m)722

Ocean Dynamics (2017) 67:713–728

Table 5 As in Table 4 except for including an additional case B30 km
nosh^ and for the data in Japan Sea (see Fig. 3b for the locations)
100 km

50 km

30 km

30 km noshMulti
(100/30 km)

SA0.86, 0.07

0.82, 0.11

0.85, 0.10

0.89, 0.05

0.87, 0.06

SD

0.65, 0.10

0.62, 0.10

0.60, 0.10

0.66, 0.10

0.63, 0.10

FK

0.76, 0.08

0.72, 0.08

0.76, 0.08

0.77, 0.08

0.76, 0.08

relatively high as compared to around the other two stations,and the overfitting to SSHA tends to be pronounced aroundthere (also see Fig. 9).
Figure 6 compares fitting to in situ temperature and salinity
profiles independent from the assimilation among the MS3DVAR and SS-3DVAR cases. In south of Japan, a temperature RMSD profile in the upper 400-m depth for the MS3DVAR case is smaller than all the other cases (Fig. 6a); eventhe mean absolute difference (BIAS) in temperature and

(a)

(d)

(b)

(e)

(c)

(f)

Fig. 6 Profiles of RMSD (curves) and BIAS (points) in temperature (a, c, e) and salinity (b, d, f) between the assimilation products and independent
observation data (see Fig. 3b for data locations). a, b South of Japan. c, d Mixed water region. e, f The Japan Sea southern region

Ocean Dynamics (2017) 67:713–728

(a)

(b)

723

salinity in the upper 200 m as compared to those in the SS3DVAR cases. Comparison of temperature and salinity snapshots indicates that decreasing horizontal scale in the SS3DVAR cases tends to represent small-scale patches in the
Oyashio water region (not shown) and intensity of the patches
are more enhanced in salinity than that in temperature, as
demonstrated by significant increase in salinity RMSD forthe 30-km case shown in Fig. 6d.
Dependence of the skill on the single horizontal scales usedin the SS-3DVAR cases is not simple in the Japan Sea southern region (Fig. 6e, f). The use of the 100-km scale leads to
RMSD exceeding 3 °C owing to less ability to capture detailed structures of the observed phenomena by use of thehorizontal larger scale as compared to the observable horizontal scale (cf. Fig. 2a, b). Decreasing the horizontal scale to50 km reduces RMSD in temperature but increases that insalinity. Further reduction of the scale to 30 km slightly changes the skill in salinity but considerably deteriorates the skill intemperature in association with the unrealistic patchy phenomena (e.g., Fig. 2d) caused by overfitting to the SSHA
observation, which is demonstrated by the improved skill inthe sensitivity experiment excluding SSHA assimilation. BothRMSD and BIAS of MS-3DVAR are generally smallest
among all the cases.
3.3 Diagnosis of the SS-3DVAR and MS-3DVAR results(c)Fig. 7 Time sequences of RMSD between the analysis and assimilated
observation temperature data at 100-m depth in the optimized costfunctions of the regions 4 (a), 2 (b), and 11 (c). See Fig. 1 for thelocations. Note that Bmulti.large^ and Bmulti.small^ correspond to thethird terms of (4) and (5), respectively

salinity shows no considerable difference among all the cases(Fig. 6a, b), and salinity RMSD of MS-3DVAR is slightly
worse than SS-3DVAR with 100-km scale for a depth range
from 200 to 500 m.
In the mixed water region (Fig. 6c, d), the MS-3DVAR caseresults in reduced RMSD and BIAS of temperature andMS-3DVAR spreads the information contained in the coarse
observation data over the broad area through the optimizationof the large-scale cost function, while it extracts the detailed
information from the observation through the small-scale costfunction optimization (Li et al. 2015a). To confirm the effectsof the multi-scale optimization, we plot in Fig. 7 temperature
RMSD at 100-m depth between the analysis and assimilated
in situ observation data, which are evaluated from the thirdterms of (4) and (5) in the MS-3DVAR cases and from thecorresponding term in the SS-3DVAR cases, in optimization
results for the regions 4 (Fig. 7a), 2 (Fig. 7b), and 11 (Fig. 7c),which roughly correspond to SOJ, MWR, and JSS, respectively. In the region 4 including south of Japan (see Fig. 1 forthe location), the large-scale cost function in MS-3DVAR iswell optimized as done in the 100-km scale SS-3DVAR costfunction, and moreover, the small-scale cost function in MS3DVAR is optimized more effectively than done in the SS3DVAR cost function with 30-km scale (Fig. 7a). In both theregion 2 including the mixed water region (Fig. 7b) and region
11 including the Japan Sea southern region (Fig. 7c), the analysis temperatures in both large and small scales are fitted tothe observation data more than those in the SS-3DVAR caseswith 100 and 30-km scales, respectively. Figure 7 demonstrates that the MS-3DVAR case achieves the estimation of
the temperature simultaneously in both large and small scales.
Although the SS-3DVAR cases with the smaller scales allow

724

Ocean Dynamics (2017) 67:713–728

the fitting to the data confined near the measurement location,
it does not confirm the fitting over larger scales. The deterioration of the skill in the fitting to the independent data (Fig. 6)
could occur as a result of the enhanced fitting with the single
smaller scales.
Temperature (Fig. 8a) and salinity (Fig. 8b) RMSDs at 100m depth between the MS-3DVAR and SS-3DVAR with100 km clearly indicate that the modification by MS3DVAR appears confined around the oceanic fronts including
the Kuroshio, Kuroshio Extension, the Japan Sea Subpolar
Fronts, and Subarctic Frontal Zone (Kida et al. 2015), whichis quite consistent with the expected effect of MS-3DVAR
allowing representation of the fine-scale structures associatedwith the front variability by use of a smaller horizontal scale,
30 km. SS-3DVAR with 30-km scale also results in the changes around the oceanic fronts as compared to SS-3DVAR with
100-km scale (Fig. 8c). The magnitude of RMSD in the mixed
water region and the Japan Sea is larger than that of the MS3DVAR case (Fig. 8a), and it is related with the patchy features shown in SS-3DVAR with 30-km scale (e.g., Fig. 2b)and the enhanced deviation from the independent data
(Fig. 6c, e). RMSD between MS-3DVAR and SS-3DVARwith 30-km scale (Fig. 8d) also suggests the overmodification
around the fronts caused by the use of 30-km single scale.
Fig. 8 a RMSD in temperature at
100-m depth between MS3DVAR and SS-3DVAR with
100-km scale. b As in a except forsalinity at 100-m depth. c As in a
except for between SS-3DVAR
cases with 30 and 100 km. d As ina except for between MS-3DVAR
and SS-3DVAR with 30 km

Since the typical horizontal scale of mesoscale eddy in theJapan Sea is 100–150 km (Morimoto et al. 2000) and observable minimum scale of the present altimeter satellites is 100 km(Fu and Ferrari 2008), the horizontal scales of 30 and 50 km are
not suitable for the assimilation of SSHA for capturing themesoscale eddies in the Japan Sea. The overfitting often arises
owing to the weak constraint to the SST data with large part ofmissing when the clouds associated with the winter monsoon
cover the large area of the Japan Sea.
Figure 9 compares RMS variability of temperature at 100m depth among SS-3DVAR with 100-km scale (Fig. 9a), with
50-km scale (Fig. 9b), with 30-km scale (Fig. 9c), with 30-km
scale but without SSHA data assimilation (Fig. 9d), and MS3DVAR (Fig. 9e). The use of smaller horizontal scales (50 and
30 km) results in wrongly enhanced variability especiallyaround the SA station (see Fig. 3b for the location), leading
to worse skills in the sea level variation (Table 5). Excluding
SSHA assimilation in SS-3DVAR with 30-km scale (Fig. 9d)
effectively removes the overfitting effect but it causes the underestimation of the variability as compared to those in theother cases with SSHA assimilation. MS-3DVAR (Fig. 9e)seems to represent a well-organized feature of the variability
mainly related to the mesoscale Tsushima Warm Current variations (Morimoto and Yanagi 2001).

(a)

(b)

(c)

(d)

Ocean Dynamics (2017) 67:713–728
Fig. 9 RMS variability (shades
and contours) of temperature at
100-m depth in the Japan Sea.
Interval of contours is 1 °C. a SS3DVAR with 100-km scale. b SS3DVAR with 50-km scale. c SS3DVAR with 30-km scale. d SS3DVAR with 30-km scale without
SSHA data assimilation. e MS3DVAR

725

(a)

(b)

(c)

(d)

(e)

MS-3DVAR is able to avoid the mismatch between theobservable minimum sampling scale and dominant horizontal
scale of the target phenomena, like aliasing. Note that the useof multi-scales is more effective in the mixed water region andJapan Sea than south of Japan (Figs. 8 and 9). This is reasonably consistent with smaller horizontal scales of oceanic

726

phenomena characterized by the first mode internal Rossby
radius in the former two regions than in the latter one region(Oh et al. 2000).

3.4 SST sensitivity experiments
Other high-resolution SST products of MGDSST and
MODIS-SST are assimilated into the model using MS3DVAR. Even with coarser resolution (0.25°) of MGDSST
than Himawari-8 (0.1° in this implementation), RMSD and
BIAS of the MGDSST case in comparison with the T/S data
are almost similar to those in the case assimilating Himawari-8
SST (Fig. 10 for south of Japan and also in the other regions
including the mixed water region and the Japan Sea; notFig. 10 a As in Fig. 6a except forvalidation of the assimilation
products with three products ofsea surface temperature data:
Himawari-8 (multi), MGDSST,
and MODIS-SST. b As in a
except for the salinity profiles

Ocean Dynamics (2017) 67:713–728

shown). The skill reproducing the Kuroshio path positions is
also similar to the Himawari-8 case (Table 3).
The assimilation of MODIS-SST causes increase of both
RMSD and BIAS in subsurface layer (Fig. 10, also in the otherregions; not shown), suggesting that the noise in MODIS-SST
remaining after the quality check and horizontal smoothing
still affects the representation of temperature/salinity profilesof the assimilation products. The recent degradation of the
MODIS sensor (Polachenski et al. 2015) possibly enhances
the noise. Reproducibility of the Kuroshio path positions in
the MODIS-SST case is worse and more unstable than in theother cases (Table 3), which could be also affected by thenoise.
The sea level variations south of Japan are well reproduced
in both MGDSST and MODIS cases (not shown) as in theOcean Dynamics (2017) 67:713–728

Himawari-8 case (multi in Fig. 5b) except for SLD between
Kushimoto and Uragami. For this SLD, correlation coefficients are 0.61 and 0.50 for the MGDSST and MODIS-SST
cases, respectively, while it is 0.70 for the Himawari-8 case,suggesting a better skill for representing the Kuroshio pathvariation east of Kushimoto and Uragami by assimilating theHimawari-8 SST as compared to the assimilation of the other
SST products.

4 Summary
We implement the MS-3DVAR (Li et al. 2015a) with a combination of 100 and 30-km scales on an operational North
Western Pacific ocean nowcast/forecast system JCOPE2,
which originally uses a SS-3DVAR scheme with approximately 100-km scale (Miyazawa et al. 2009). The present
study is the first application of MS-3DVAR and Himawari-8
SST assimilation to operational ocean nowcast/forecast systems. MS-3DVAR effectively assimilates the sparsedistributed T/S profile data by spreading the information overthe large scale and representing the detailed information near
the measurement points, as noticed by the original theoretical
study (Li et al. 2015a). In addition, we find that JCOPE2 with
MS-3DVAR reasonably assimilates the Himawari-8 SST
product without noisy features caused by the cloud effects thatare involved in the infrared-type SST data. We also find that
MS-3DVAR is more effective for estimating oceanic conditions in regions with smaller mesoscale variability including
the mixed water region and Japan Sea as compared to in southof Japan because it safely avoids aliasing-like phenomena thatare induced by coarse density sampling of SSHA.
The Himawari-8 SST product is assimilated by MS3DVAR without any additional quality check and smoothing,while the assimilation of the MODIS-SST product results inthe worse skill of the water mass distribution even with therelatively rigorous quality check and additional smoothing,indicating requirement of more enhanced calibration for thecurrent MODIS-SST product. The skills of the MGDSST assimilation product for the indices used in the present study arealmost comparable to those of the Himawari-8 SST assimilation. The assimilation of MGDSST with 0.25° resolution
could be useful for the current target scale range: 100–
30 km. Although the Himawari-8 SST data are available from
August 2015, MGDSST and AVHRR SST data are available
from 1980s. We are planning to update the JCOPE2 reanalysis
data (Miyazawa et al. 2009) by the use of the modified
JCOPE2 with MS-3DVAR utilizing MGDSST and/or
AVHRR SST.
Note that temperature/salinity RMSD between the
MGDSST and Himawari-8 SST assimilation cases (notshown) shows considerable deviation from each other similar
to those shown in Fig. 8a, b but with smaller magnitude727

around the oceanic fronts than indicated in Fig. 8a, b, suggesting possible impacts of utilizing the higher resolution
Himawari-8 SST data under the current model setting (cf.
Kawai et al. 2015). Full utilization of the high-resolution
Himawari-8 SST may require applying it to higher resolution
ocean models with smaller target scales and/or more advanced
data assimilation methods (e.g., the ensemble Kalman filter
with 1/36° resolution by Hihara et al. 2017, in preparation).
MS-3DVAR provides an intermediate option for further
improvement of the 3DVAR prior to switching over to theadvanced but expensive methods including the ensemble
Kalman filter (e.g., Miyazawa et al. 2013) and 4DVAR (e.g.,
Usui et al. 2015). It is interesting to carefully compare cost andbenefit depending on possible choices of the forward models
with higher resolution and the data assimilation methods withvarious levels of the implementation and computational costs.

Acknowledgements Comments from two anonymous reviewers arequite useful for improving the earlier versions of the manuscript. This
work is part of the Japan Coastal Ocean Predictability Experiment(JCOPE) promoted by the Japan Agency for Marine-Earth Science andTechnology (JAMSTEC). A source code of the rotated baroclinic pressure gradient scheme was provided by Takashi Kagimoto. The multi-core
workstations based on the Intel Xeon processors were prepared byHidehiro Fujio. The research product of sea surface temperature produced
from Himawari-8, Version 1.1, was supplied by the P-Tree System, Japan
Aerospace Exploration Agency (JAXA). The Moderate Resolution
Imaging Spectroradiometer (MODIS) SST produced from Aqua/Terra
was also provided from JAXA. Merged Satellite and In situ Data
Global Daily SST (MGDSST) was obtained from the NEAR-GOOS
regional real-time database. The Ssalto/Duacs altimeter products were
produced and distributed by the Copernicus Marine and Environment
Monitoring Service (CMEMS) (http://www.marine.copernicus.eu). The
Naval Oceanographic Office (NAVOCEANO)-Multi-Channel SST(MCSST) were downloaded from Physical Oceanography Distributed
Active Archive Center (PODAAC) ftp site: ftp://podaac.jpl.nasa.gov. In
situ temperature and salinity profiles were obtained from the Global
Temperature-Salinity Profile Program (GTSPP) website: http://www.
nodc.noaa.gov/GTSPP/. Tide gauge data were provided from the Japan
Meteorological Agency (JMA) and the Japan Coast Guard. The atmospheric pressure data for the barometric correction of the tide gauge data
were provided from JMA.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made.

References
Choi B-J, Haidvogel DB, Cho Y-K (2004) Nonseasonal sea level variations in the Japan/East Sea from satellite altimeter data. J Geophys
Res 109(C12). doi:10.1029/2004JC002387
Fu LL, Ferrari R (2008) Observing oceanic submesoscale processes fromspace. EOS Transactions American Geophysical Union 89:488

728
Fu LL, Ubelmann C (2014) On the transition from profile altimeter toswath altimeter for observing global ocean surface topography. J
Atmos Ocean Tech 31:560–568
Fujii Y, Kamachi M (2003) A reconstruction of observed profiles in thesea east of Japan using vertical coupled temperature-salinity EOF
modes. J Oceanogr 59:173–186
Fujisaki A, Yamaguchi H, Mitsudera H (2010) Numerical experiments of
air–ice drag coefficient and its impact on ice–ocean coupled systemin the Sea of Okhotsk. Ocean Dyn 60:377–394
Furuichi N, Hibiya T, Niwa Y (2012) Assessment of turbulence closure
models for resonant inertial response in the oceanic mixed layerusing a large eddy simulation model. J Oceanogr 68:285–294
Griffies SM, Biastoch A, Boning C, Bryan F, Danabasoglu G, Chassignet
EP, England MH, Gerdes R, Haak H, Hallberg RW, Hazeleger W,
Jungclaus J, Large WG, Madec G, Pirani A, Samuels BL, Scheinert
M, Sen Gupta A, Severijns CA, Simmons HL, Treguier AM,
Winton M, Yeager S, Yin J (2009) Coordinated ocean-ice reference
experiments (COREs). Ocean Model 26:1–46
Hanawa K, Mitsudera H (1985) About the daily averaging method of
oceanic data (in Japanese). Bull on Coastal Oceanogr 23:79–87
Hirose N (2011) Inverse estimation of empirical parameters used in aregional ocean circulation model. J Oceanogr 67:323–336
Hosoda K, Murakami H, Sakaida F, Kawamura H (2007) Algorithm andvalidation of sea surace temperature observation using MODIS sensors aboard Terra and Aqua in the western north Pacific. J Oceanogr
63:267–280
Ichikawa K (2014) Satellite altimeters in the early 21th century (inJapanese with English abstract and figure captions). Oceanog in
Jpn 23:13–27
Kawabe M (1989) Sea level changes south of Japan associated with the nonlarge-meander path of the Kuroshio. J Oceanogr Sco Jpn 45:181–189
Kawai Y, Miyama T, Iizuka S, Manda A, Yoshioka MK, Katagiri S,
Tachibana Y, Nakamura H (2015) Marine atmospheric boundary
layer and low-level cloud responses to the Kuroshio Extension front
in the early summer of 2012: three-vessel simultaneous observations
and numerical simulations. J Oceanogr 71:511–526
Kida S, Mitsudera H, Aoki S, Guo X, Ito S, Kobashi F, Komori N,
Kubokawa A, Miyama T, Morie R, Nakamura H, Nakamura T,
Nakano H, Nishigaki H, Nonaka M, Sasaki H, Sasaki YN, Suga T,
Sugimoto S, Taguchi B, Takaya K, Tozuka T, Tsujino H, Usui N(2015) Oceanic fronts and jets around Japan: a review. J Oceanogr
71:469–497
Kuragano T, Kamachi M (2000) Global statistical space-time scales ofoceanic variability estimated from the TOPEX/POSEIDON altimeter data. J Geophys Res 105:955–974
Kurihara Y, Sakurai T, Kuragano T (2006) Global daily sea surface temperature analysis using data from satellite microwave radiometer,satellite infrared radiometer and in-situ observations (in Japanese).
Weather Bull 73:s1–s18
Kurihara Y, Murakami H, Kachi M (2016) Sea surface temperature fromthe new Japanese geostationary meteorological Himawari-8 satellite. Geophys Res Let 43:1234–1240
Li Z, Chao Y, Farara JD, McWilliams JC (2013) Impacts of distinct
observations during the 2009 Prince William sound field experiment: a data assimilation study. Cont Shelf Res 63:S209–S222
Li Z, McWilliams JC, Ide K, Farrara JD (2015a) A multi-scale variational
data assimilation scheme: formulation and illustration. Mon Wea
Rev 143:3804–3822
Li Z, McWilliams JC, Ide K, Farrara JD (2015b) Coastal ocean data
assimilation using a multi-scale three-dimensional variational
scheme. Ocean Dyn 65:1001–1015

Ocean Dynamics (2017) 67:713–728
May DA, Parmeter MM, Olszewski DS, McKenzie BD (1998) Operational
processing of satellite sea surface temperature retrievals at the Naval
Oceanographic Office. Bull Amer Meteor Soc 79:397–407
McCalpin JD (1994) A comparison of second-order and fourth-orderpressure gradient algorithms in a sigma coordinate ocean model.
Int Num Methods Fluids 18:361–383
Mellor GL, Blumberg A (2004) Wave breaking and ocean surface layer
thermal response. J Phys Oceanogr 34:693–698
Mellor GL, Yamada T (1982) Development of a turbulence closure modelfor geophysical fluid problems. Rev Geophys Space Phys 20:851–
875
Mellor GL, Hakkinen S, Ezer T, Patchen R (2002) A generalization of asigma coordinate ocean model and an inter comparison of model
vertical grids. In: Pinardi N, Woods JD (eds) Ocean Forecasting:Conceptual Basis and Applications. Springer, New York, pp 55–72
Miyazawa Y, Zhang RC, Guo X, Tamura H, Ambe D, Lee JS, Okuno A,
Yoshinari H, Setou T, Komatsu K (2009) Water mass variability in
the Western North Pacific detected in a 15-year eddy resolving
ocean reanalysis. J Oceanogr 65:737–756
Miyazawa Y, Miyama T, Varlamov SM, Guo X, Waseda T (2012) Openand coastal seas interactions south of Japan represented by an ensemble Kalman Filter. Ocean Dyn 62:645–659
Miyazawa Y, Murakami H, Miyama T, Varlamov SM, Guo X, Waseda T,
Sil S (2013) Data assimilation of the high-resolution sea surface
temperature obtained from the Aqua-Terra satellites (MODIS-SST)
using an ensemble Kalman filter. Remote Sens 5:3123–3139
Morimoto A, Yanagi T (2001) Variability of sea surface circulation in theJapan Sea. J Oceanogr 57:1–13
Morimoto A, Yanagi T, Kaneko A (2000) Eddy field in the the Japan Sea
derived from satellite altimetric data. J Oceanogr 56:449–462
Muscarella PA, Carrier MJ, Ngodock HE (2014) An example of a multiscale three-dimensional variational data assimilation scheme in the
Kuroshio Extension using the naval coastal ocean model. Cont Shel
Res 73:41–48
Nakanishi M, Niino H (2009) Development of an improved turbulence
closure model for the atmospheric boundary layer. J Metorol Soc
Jpn 87:895–912
Oh IS, Zhurbas V, Park W, (2000) Estimating horizontal diffusivity in the
East Sea (Sea of Japan) and the northwest Pacific from satellitetracked drifter data. J Geophys Res: Oceans 105(C3):6483–6492
Polachenski CM, Dibb JE, Flanner MG, Chen JY, Courville ZR, Lai AM,
Schauer JJ, Shafer MM, Bergin M (2015) Neither dust nor black
carbon causing apparent albedo decline in Greenland's dry snow
zone: implications for MODIS C5 surface reflectance. Geophys
Res Let 42:9319–9327
Soeyanto E, Guo X, Ono J, Miyazawa Y (2014) Interannual variations ofKuroshio transport in the East China Sea and its relation to the
Pacific Decadal Oscillation and mesoscale eddies. J Geophys
Oceans 119:3595–3616
Thiem O, Berntsen J (2006) Internal pressure errors in sigma coordinate
ocean models due to anisotropy. Ocean Model 12:140–156
Uchimoto K, Mitsudera H, Ebuchi N, Miyazawa Y (2007) Anticycloniceddy caused by the Soya Warm Current in an Okhotsk OGCM. J
Oceanogr 63:379–391
Usui N, Tsujino H, Fujii Y, Kamachi M (2006) Short-range prediction
experiments of the Kuroshio path variabilities south of Japan. Ocean
Dyn 56:607–623
Usui N, Fujii Y, Sakamoto K, Kamachi M (2015) Development of a fourdimensional variational assimilation system for coastal data assimilation around Japan. Mon Wea Rev 143:3874–3892
Varlamov SM, Guo X, Miyama T, Ichikawa K, Waseda T, Miyazawa Y(2015) M2 baroclinic tide variability modulated by the ocean circulation south of Japan. J Geophys Res Oceans 120:3681–3710