Editorial Type:
Article
Article Type:
Research Article

Development of a Four-Dimensional Variational Assimilation System for Coastal Data Assimilation around Japan

Norihisa Usui Oceanography and Geochemistry Research Department, Meteorological Research Institute, Tsukuba, Japan

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Yosuke Fujii Oceanography and Geochemistry Research Department, Meteorological Research Institute, Tsukuba, Japan

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Kei Sakamoto Oceanography and Geochemistry Research Department, Meteorological Research Institute, Tsukuba, Japan

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Masafumi Kamachi Oceanography and Geochemistry Research Department, Meteorological Research Institute, Tsukuba, Japan

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Print Publication:
01 Oct 2015
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Sixth WMO Data Assimilation Symposium
DOI:
https://doi.org/10.1175/MWR-D-14-00326.1
Page(s):
3874–3892
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Abstract

The authors have developed an assimilation system toward coastal data assimilation around Japan, which consists of a four-dimensional variational (4DVAR) assimilation scheme with an eddy-resolving model in the western North Pacific (MOVE-4DVAR-WNP) and a fine-resolution coastal model covering the western part of the Japanese coastal region around the Seto Inland Sea (MOVE-Seto). The 4DVAR scheme is developed as a natural extension of the 3DVAR scheme used in the Meteorological Research Institute Multivariate Ocean Variational Estimation (MOVE) system. An initialization scheme of incremental analysis update (IAU) is incorporated into MOVE-4DVAR-WNP to filter out high-frequency noises. During the backward integration of the adjoint model, it works as an incremental digital filtering. MOVE-Seto, which is nested within MOVE-4DVAR-WNP, also employs IAU to initialize the interior of the coastal model using MOVE-4DVAR-WNP analysis fields. The authors conducted an assimilation experiment using MOVE-4DVAR-WNP, and results were compared with an additional experiment using the 3DVAR scheme. The comparison reveals that MOVE-4DVAR-WNP improves mesoscale variability. In particular, short-term variability such as small-scale Kuroshio fluctuations is much enhanced. Using MOVE-Seto and MOVE-4DVAR-WNP, the authors also performed a case study focused on an unusual tide event that occurred at the south coast of Japan in September 2011. MOVE-Seto succeeds in reproducing a significant sea level rise associated with this event, indicating the effectiveness of the newly developed system for coastal sea level variability.

Corresponding author address: Norihisa Usui, Oceanography and Geochemistry Research Department, Meteorological Research Institute, Nagamine 1-1, Tsukuba 305-0052, Japan. E-mail: nusui@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

The authors have developed an assimilation system toward coastal data assimilation around Japan, which consists of a four-dimensional variational (4DVAR) assimilation scheme with an eddy-resolving model in the western North Pacific (MOVE-4DVAR-WNP) and a fine-resolution coastal model covering the western part of the Japanese coastal region around the Seto Inland Sea (MOVE-Seto). The 4DVAR scheme is developed as a natural extension of the 3DVAR scheme used in the Meteorological Research Institute Multivariate Ocean Variational Estimation (MOVE) system. An initialization scheme of incremental analysis update (IAU) is incorporated into MOVE-4DVAR-WNP to filter out high-frequency noises. During the backward integration of the adjoint model, it works as an incremental digital filtering. MOVE-Seto, which is nested within MOVE-4DVAR-WNP, also employs IAU to initialize the interior of the coastal model using MOVE-4DVAR-WNP analysis fields. The authors conducted an assimilation experiment using MOVE-4DVAR-WNP, and results were compared with an additional experiment using the 3DVAR scheme. The comparison reveals that MOVE-4DVAR-WNP improves mesoscale variability. In particular, short-term variability such as small-scale Kuroshio fluctuations is much enhanced. Using MOVE-Seto and MOVE-4DVAR-WNP, the authors also performed a case study focused on an unusual tide event that occurred at the south coast of Japan in September 2011. MOVE-Seto succeeds in reproducing a significant sea level rise associated with this event, indicating the effectiveness of the newly developed system for coastal sea level variability.

Corresponding author address: Norihisa Usui, Oceanography and Geochemistry Research Department, Meteorological Research Institute, Nagamine 1-1, Tsukuba 305-0052, Japan. E-mail: nusui@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

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  • Bennett, A. F., 1992: Inverse Methods in Physical Oceanography. Cambridge University Press, 346 pp.

  • Bennett, A. F., 2002: Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, 234 pp.

  • Bloom, S. C., L. L. Takacs, A. M. D. Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256–1271, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Broquet, G., C. Edwards, A. Moore, B. Powell, M. Veneziani, and J. Doyle, 2009: Application of 4D-Variational data assimilation to the California current system. Dyn. Atmos. Oceans, 48, 69–92, doi:10.1016/j.dynatmoce.2009.03.001.

    • Search Google Scholar
    • Export Citation

  • Chua, B. S., and A. F. Bennett, 2001: An inverse ocean modeling system. Ocean Modell., 3, 137–165, doi:10.1016/S1463-5003(01)00006-3.

    • Search Google Scholar
    • Export Citation

  • CLS, 2004: SSALTO/DUACS user handbook: (M)SLA and (M)ADT near-real time and delayed time products. Centre National d’Etudes Spatiales Rep. CLS-DOS-NT-04-103, 42 pp.

  • Conkright, M., and Coauthors, 2002: Introduction. Vol. 1, World Ocean Database 2001, NOAA Atlas NESDIS 42, 167 pp.

  • Courtier, P., J.-N. Thepaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367–1387, doi:10.1002/qj.49712051912.

    • Search Google Scholar
    • Export Citation

  • Cummings, J., and Coauthors, 2009: Ocean data assimilation systems for GODAE. Oceanography, 22, 96–109, doi:10.5670/oceanog.2009.69.

    • Search Google Scholar
    • Export Citation

  • Dombrowsky, E., and Coauthors, 2009: GODAE systems in operation. Oceanography, 22, 80–95, doi:10.5670/oceanog.2009.68.

  • Evensen, G., 2007: Data Assimilation: The Ensemble Kalman Filter. Springer, 307 pp.

  • Fujii, Y., 2005: Preconditioned optimizing utility for large-dimensional analyses (popular). J. Oceanogr., 61, 167–181, doi:10.1007/s10872-005-0029-z.

    • Search Google Scholar
    • Export Citation

  • Fujii, Y., and M. Kamachi, 2003a: A reconstruction of observed profiles in the sea east of Japan using vertical coupled temperature-salinity EOF modes. J. Oceanogr., 59, 173–186, doi:10.1023/A:1025539104750.

    • Search Google Scholar
    • Export Citation

  • Fujii, Y., and M. Kamachi, 2003b: Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity EOF modes. J. Geophys. Res., 108, 3297, doi:10.1029/2002JC001745.

    • Search Google Scholar
    • Export Citation

  • Fujii, Y., S. Ishizaki, and M. Kamachi, 2005: Application of nonlinear constraints in a three-dimensional variational ocean analysis. J. Oceanogr., 61, 655–662, doi:10.1007/s10872-005-0073-8.

    • Search Google Scholar
    • Export Citation

  • Fujii, Y., H. Tsujino, N. Usui, H. Nakano, and M. Kamachi, 2008: Application of singular vector analysis to the Kuroshio large meander. J. Geophys. Res., 113, C07026, doi:10.1029/2007JC004476.

    • Search Google Scholar
    • Export Citation

  • Fujii, Y., T. Nakano, N. Usui, S. Matsumoto, H. Tsujino, and M. Kamachi, 2013: Pathways of the North Pacific intermediate water identified through the tangent linear and adjoint models of an ocean general circulation model. J. Geophys. Res., 118, 2035–2051, doi:10.1002/jgrc.20094.

    • Search Google Scholar
    • Export Citation

  • Fukumori, I., 2002: A partitioned Kalman filter and smoother. Mon. Wea. Rev., 130, 1370–1383, doi:10.1175/1520-0493(2002)130<1370:APKFAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ghil, M., and P. Malanotte-Rizzoli, 1991: Data assimilation in meteorology and oceanography. Advances in Geophysics, Vol. 33, Academic Press, 141–266, doi:10.1016/S0065-2687(08)60442-2.

    • Search Google Scholar
    • Export Citation

  • Griffies, S. M., and R. W. Hallberg, 2000: Biharmonic friction with a Smagorinsky-like viscosity for use in large-scale eddy-permitting ocean models. Mon. Wea. Rev., 128, 2935–2946, doi:10.1175/1520-0493(2000)128<2935:BFWASL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hamilton, D., 1994: GTSPP builds an ocean temperature-salinity database. Earth Syst. Monit., 4, 4–5.

  • Hanawa, K., and F. Mitsudera, 1985: On the data processings of daily mean values of oceanographical data. Note on the daily mean sea-level data (in Japanese). Bull. Coast. Oceanogr., 23, 4–5.

    • Search Google Scholar
    • Export Citation

  • Hunke, E. C., and J. K. Ducowicz, 1997: An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceanogr., 27, 1849–1867, doi:10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hunke, E. C., and J. K. Ducowicz, 2002: The elastic-viscous-plastic sea ice dynamics model in general orthogonal curvilinear coordinates on a sphere: Incorporation of metric terms. Mon. Wea. Rev., 130, 1848–1865, doi:10.1175/1520-0493(2002)130<1848:TEVPSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hurlburt, H. E., and Coauthors, 2009: High-resolution global and basin-scale ocean analyses and forecasts. Oceanography, 22, 110–127, doi:10.5670/oceanog.2009.70.

    • Search Google Scholar
    • Export Citation

  • Ishikawa, Y., T. Awaji, T. Toyoda, T. In, K. Nishina, T. Nakayama, S. Shima, and S. Masuda, 2009: High-resolution synthetic monitoring by a 4-dimensional variational data assimilation system in the northwestern North Pacific. J. Mar. Syst., 78, 237–248, doi:10.1016/j.jmarsys.2009.02.016.

    • Search Google Scholar
    • Export Citation

  • Kawabe, M., 1980: Sea level variations along the south coast of Japan and the large meander in the Kuroshio. J. Oceanogr. Soc. Japan, 36, 97–104, doi:10.1007/BF02312095.

    • Search Google Scholar
    • Export Citation

  • Killworth, P. D., D. J. Webb, D. Stainforth, and S. M. Paterson, 1991: The development of a free-surface Bryan–Cox–Semtner ocean model. J. Phys. Oceanogr., 21, 1333–1348, doi:10.1175/1520-0485(1991)021<1333:TDOAFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kondo, J., 1975: Air–sea bulk transfer coefficients in diabatic conditions. Bound.-Layer Meteor., 9, 91–112, doi:10.1007/BF00232256.

    • Search Google Scholar
    • Export Citation

  • Kourafalou, V., and Coauthors, 2015: Coastal Ocean Forecasting: System integration and evaluation. J. Oper. Oceanogr., 8 (S1) s127–s146, doi:10.1080/1755876X.2015.1022336.

    • Search Google Scholar
    • Export Citation

  • Kuragano, T., and M. Kamachi, 2000: Global statistical space-time scales of oceanic variability estimated from the TOPEX/POSEIDON altimetry data. J. Geophys. Res., 105, 955–974, doi:10.1029/1999JC900247.

    • Search Google Scholar
    • Export Citation

  • Kuragano, T., Y. Fujii, T. Toyoda, N. Usui, K. Ogawa, and M. Kamachi, 2014: Seasonal barotropic sea surface height fluctuation in relation to regional ocean mass variation. J. Oceanogr., 70, 45–62, doi:10.1007/s10872-013-0211-7.

    • Search Google Scholar
    • Export Citation

  • Kurapov, A. L., D. Foley, P. T. Strub, G. D. Egbert, and J. S. Allen, 2011: Variational assimilation of satellite observations in a coastal ocean model off Oregon. J. Geophys. Res., 116, C05006, doi:10.1029/2010JC006909.

    • Search Google Scholar
    • Export Citation

  • Kurihara, Y., T. Sakurai, and T. Kuragano, 2000: Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in-situ observations (in Japanese). Wea. Bull., 73, S1–S18.

    • Search Google Scholar
    • Export Citation

  • Lewis, J. M., S. Lakshmivarahan, and S. Dhall, 2006: Dynamic Data Assimilation: A Least Squares Approach. Cambridge University Press, 654 pp.

  • Madec, G., P. Delecluse, M. Imbard, and C. Levy, 1998: OPA 8.1 Ocean General Circulation Model reference manual. Institut Pierre-Simon Laplace Note du Pole de Modélisation 11, 91 pp. [Available online at http://www.nemo-ocean.eu/content/download/259/1665/file/Doc_OPA8.1.pdf.]

  • Mellor, G. L., and L. Kantha, 1989: An ice-ocean coupled model. J. Geophys. Res., 94, 10 937–10 954, doi:10.1029/JC094iC08p10937.

    • Search Google Scholar
    • Export Citation

  • Moore, A. M., H. G. Arango, G. Broquet, B. S. Powell, A. T. Weaver, and J. Zavala-Garay, 2011: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part I—System overview and formulation. Prog. Oceanogr., 91, 34–49, doi:10.1016/j.pocean.2011.05.004.

    • Search Google Scholar
    • Export Citation

  • Noh, Y., and H. J. Kim, 1999: Simulations of temperature and turbulence structure of the oceanic boundary layer with the improved near-surface processes. J. Geophys. Res., 104, 15 621–15 634, doi:10.1029/1999JC900068.

    • Search Google Scholar
    • Export Citation

  • Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85, 369–432, doi:10.2151/jmsj.85.369.

  • Polavarapu, S., S. Ren, A. Clayton, D. Sankey, and Y. Rochon, 2004: On the relationship between incremental analysis updating and incremental digital filtering. Mon. Wea. Rev., 132, 2495–2502, doi:10.1175/1520-0493(2004)132<2495:OTRBIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Powell, B., H. Arango, A. Moore, E. D. Lorenzo, R. Milliff, and D. Foley, 2008: 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Modell., 23, 130–145, doi:10.1016/j.ocemod.2008.04.008.

    • Search Google Scholar
    • Export Citation

  • Prather, M. J., 1986: Numerical advection by conservation of second-order moments. J. Geophys. Res., 91, 6671–6681, doi:10.1029/JD091iD06p06671.

    • Search Google Scholar
    • Export Citation

  • Senjyu, T., M. Matsuyama, and N. Matsubara, 1999: Interannual and decadal sea-level variations along the Japanese coast. J. Oceanogr., 55, 619–633, doi:10.1023/A:1007844903204.

    • Search Google Scholar
    • Export Citation

  • Shchepetkin, A. F., and J. C. McWilliams, 2005: The Regional Oceanic Modeling System (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modell., 9, 347–404, doi:10.1016/j.ocemod.2004.08.002.

    • Search Google Scholar
    • Export Citation

  • Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75, 191–209.

  • Thompson, R. O. R. Y., 1983: Low-pass filters to supress inertial and tide frequencies. J. Phys. Oceanogr., 13, 1077–1083, doi:10.1175/1520-0485(1983)013<1077:LPFTSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tsujino, H., N. Usui, and H. Nakano, 2006: Dynamics of Kuroshio path variations in a high-resolution GCM. J. Geophys. Res., 111, C11001, doi:10.1029/2005JC003118.

    • Search Google Scholar
    • Export Citation

  • Tsujino, H., T. Motoi, I. Ishikawa, M. Hirabara, H. Nakano, G. Yamanaka, T. Yasuda, and H. Ishizaki, 2010: Reference manual for the Meteorological Research Institute Community Ocean Model (mri.com) version 3. Meteorological Research Institute Tech. Rep. 59, 241 pp. [Available online at http://www.mri-jma.go.jp/Publish/Technical/DATA/VOL_59/59_en.html.]

  • Ueno, H., and I. Yasuda, 2000: Distribution and formation of the mesothermal structure (temperature inversions) in the North Pacific subarctic region. J. Geophys. Res., 105, 16 885–16 897, doi:10.1029/2000JC900020.

    • Search Google Scholar
    • Export Citation

  • Usui, N., S. Ishizaki, Y. Fujii, H. Tsujino, T. Yasuda, and M. Kamachi, 2006: Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results. Adv. Space Res., 37, 806–822, doi:10.1016/j.asr.2005.09.022.

    • Search Google Scholar
    • Export Citation

  • Usui, N., H. Tsujino, H. Nakano, and Y. Fujii, 2011: Decay mechanism of the 2004/05 Kuroshio large meander. J. Geophys. Res., 116, C10010, doi:10.1029/2011JC007009.

    • Search Google Scholar
    • Export Citation

  • Vialard, J., A. T. Weaver, D. L. T. Anderson, and P. Delecluse, 2003: Three- and four-dimensional variational assimilation with a general circulation model of the tropical Pacific Ocean. Part II: Physical validation. Mon. Wea. Rev., 131, 1379–1395, doi:10.1175/1520-0493(2003)131<1379:TAFVAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Weatherly, G. L., 1972: A study of the bottom boundary layer of the Florida current. J. Phys. Oceanogr., 2, 54–72.

  • Weaver, A. T., J. Vialard, and D. L. T. Anderson, 2003: Three- and four-dimensional variational assimilation with a general circulation model of the tropical Pacific Ocean. Part I: Formulation, internal diagnostics, and consistency checks. Mon. Wea. Rev., 131, 1360–1378, doi:10.1175/1520-0493(2003)131<1360:TAFVAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wunsch, C., 1996: The Ocean Circulation Inverse Problem. Cambridge University Press, 442 pp.

  • Zhang, W. G., J. L. Wilkin, and H. G. Arango, 2010: Towards an integrated observation and modeling system in the New York bight using variational methods. Part I: 4DVAR data assimilation. Ocean Modell., 35, 119–133, doi:10.1016/j.ocemod.2010.08.003.

    • Search Google Scholar
    • Export Citation

  • Zhu, J., and M. Kamachi, 2000: An adaptive variational method for data assimilation with imperfect models. Tellus, 52, 265–279, doi:10.1034/j.1600-0870.2000.d01-3.x.

    • Search Google Scholar
    • Export Citation
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