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Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas

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Abstract

A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and 99°E to 135°E in longitude including the Bohai Sea, the Yellow Sea, the East China Sea, the South China Sea and the Indonesian seas. To get precise initial conditions for the coupled forecasting model, the forecasting system conducts a 24-h hindcast simulation with data assimilation before forecasting. The Ensemble Adjustment Kalman Filter (EAKF) data assimilation method was adopted for the wave model MASNUM with assimilating Jason-2 significant wave height (SWH) data. The EAKF data assimilation method was also introduced to the ROMS model with assimilating sea surface temperature (SST), mean absolute dynamic topography (MADT) and Argo profiles data. To improve simulation of the structure of temperature and salinity, the vertical mixing scheme of the ocean model was improved by considering the surface wave induced vertical mixing and internal wave induced vertical mixing. The wave and current models were integrated from January 2014 to October 2015 driven by the ECMWF reanalysis 6 hourly mean dataset with data assimilation. Then the coupled atmosphere-wave-ocean forecasting system was carried out 14 months operational running since November 2015. The forecasting outputs include atmospheric forecast products, wave forecast products and ocean forecast products. A series of observation data are used to evaluate the coupled forecasting results, including the wind, SHW, ocean temperature and velocity. The forecasting results are in good agreement with observation data. The prediction practice for more than one year indicates that the coupled forecasting system performs stably and predict relatively accurate, which can support the shipping safety, the fisheries and the oil exploitation.

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References

  • Aldrian E, Sein D, Jacob D, et al. 2005. Modelling Indonesian rainfall with a coupled regional model. Climate Dynamics, 25(1): 1–17, doi: 10.1007/s00382–004–0483–0

    Article  Google Scholar 

  • Anderson J L. 2001. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129(12): 2884–2903, doi: 10.1175/1520–0493(2001)129<2884:AEAKFF>2.0.CO;2

    Article  Google Scholar 

  • Anderson J L. 2003. A local least squares framework for ensemble filtering. Monthly Weather Review, 131(4): 634–642, doi: 10.1175/1520–0493(2003)131<0634:ALLSFF>2.0.CO;2

    Article  Google Scholar 

  • Booij N, Ris R C, Holthuijsen L H. 1999. A third–generation wavemodel for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4): 7649–7666, doi: 10.1029/98JC02622

    Google Scholar 

  • Boville B A, Gent P R. 1998. The NCAR climate system model, version one. Journal of Climate, 11(6): 1115–1130, doi: 10.1175/1520–0442(1998)011<1115:TNCSMV>2.0.CO;2

    Article  Google Scholar 

  • Bruneau N, Toumi R. 2016. A fully–coupled atmosphere–ocean–wave model of the Caspian Sea. Ocean Modelling, 107: 97–111, doi: 10.1016/j.ocemod.2016.10.006

    Article  Google Scholar 

  • Bryan K, Manabe S, Pacanowski R C. 1975. A global ocean–atmosphere climate model. Part II. The oceanic circulation. Journal of Physical Oceanography, 5(1): 30–46, doi: 10.1175/1520–0485(1975)005<0030:AGOACM>2.0.CO;2

    Article  Google Scholar 

  • Carnes M R. 2009. Description and Evaluation of GDEM–V 3.0. NRL Rep, 2009: NRL/MR/7330–09–9165. Washington DC: Naval Research Lab Stennis Space Center

    Google Scholar 

  • Chassignet E P, Arango H, Dietrich D, et al. 2003. DAMEE–NAB: the base experiments. Dynamics of Atmospheres and Oceans, 32(3–4): 155–183

    Google Scholar 

  • Chen F, Dudhia J. 2001. Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system: Part II. Preliminary model validation. Monthly Weather Review, 129(4): 587–604, doi: 10.1175/1520–0493(2001)129<0587: CAALSH>2.0.CO;2

    Article  Google Scholar 

  • Collins W D, Bitz C M, Blackmon M L, et al. 2006. The community climate system model version 3 (CCSM3). Journal of Climate, 19(11): 2122–2143, doi: 10.1175/JCLI3761.1

    Article  Google Scholar 

  • Diaz H F, Folland C K, Manabe T, et al. 2002. Workshop on advances in the use of historical marine climate data. World Meteorological Organization Bulletin, 51: 377–380

    Google Scholar 

  • Döscher R, Willén U, Jones C, et al. 2002. The development of the regional coupled ocean–atmosphere model RCAO. Boreal Environment Research, 7(3): 183–192

    Google Scholar 

  • Dudhia J. 2004. The weather research and forecasting model (version 2.0). In: Proceedings of the 2nd International Workshop on Next Generation NWP Model. Seoul: Yonsei University, 19–23

    Google Scholar 

  • Evensen G. 1994. Sequential data assimilation with a nonlinear quasi–geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(5): 10143–10162

    Article  Google Scholar 

  • Evensen G. 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53(4): 343–367, doi: 10.1007/s10236–003–0036–9

    Article  Google Scholar 

  • Gustafsson N, Nyberg L, Omstedt A. 1998. Coupling of a high–resolution atmospheric model and an ocean model for the Baltic Sea. Monthly Weather Review, 126(11): 2822–2846, doi: 10.1175/1520–0493(1998)126<2822:COAHRA>2.0.CO;2

    Article  Google Scholar 

  • Hagedorn R, Lehmann A, Jacob D. 2000. A coupled high resolution atmosphere–ocean model for the BALTEX region. Meteorologische Zeitschrift, 9(1): 7–20, doi: 10.1127/metz/9/2000/7

    Article  Google Scholar 

  • Haidvogel D B, Arango H, Budgell W P, et al. 2008. Ocean forecasting in terrain–following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System. Journal of Computational Physics, 227(7): 3595–3624, doi: 10.1016/j.jcp.2007. 06.016

    Article  Google Scholar 

  • Haidvogel D B, Arango H G, Hedstrom K, et al. 2002. Model evaluation experiments in the North Atlantic Basin: simulations in nonlinear terrain–following coordinates. Dynamics of Atmospheres and Oceans, 32(3–4): 239–281

    Google Scholar 

  • Hasselmann K, Barnett T P, Bouws E, et al. 1973. Measurements of wind–wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutsche Hydrographische Zeitschrift, A8(12): 1–95

    Google Scholar 

  • Hodur R M. 1997. The Naval Research Laboratory’s coupled ocean/atmosphere mesoscale prediction system (COAMPS). Monthly Weather Review, 125(7): 1414–1430, doi: 10.1175/1520–0493(1997)125<1414:TNRLSC>2.0.CO;2

    Article  Google Scholar 

  • Hong Songyou, Pan Hualu. 1998. Convective trigger function for a mass–flux cumulus parameterization scheme. Monthly Weather Review, 126(10): 2599–2620, doi: 10.1175/1520–0493(1998) 126<2599:CTFFAM>2.0.CO;2

    Article  Google Scholar 

  • Hong S Y, Kim J H, Lim J O, et al. 2006. The WRF single moment microphysics scheme (WSM). Journal of the Korean Meteorological Society, 42: 129–151

    Google Scholar 

  • Janjic Z I. 1996. The surface layer in the NCEP Eta model. In: Eleventh Conference on Numerical Weather Prediction. Norfolk: American Meteorological Society, 19–23

    Google Scholar 

  • Janjic Z I. 2002. Nonsingular implementation of the Mellor–Yamada level 2. 5 scheme in the NCEP Meso–scale model. NCEP Office Note, 437: 61

    Google Scholar 

  • Kain J S. 2004. The Kain Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43(1): 170–181, doi: 10.1175/1520–0450(2004)043<0170:TKCPAU>2.0.CO;2

    Article  Google Scholar 

  • Laurent L. 2008. Turbulent dissipation on the margins of the South China Sea. Geophysical Research Letters, 35(23): L23615, doi: 10.1029/2008GL035520

    Book  Google Scholar 

  • Manabe S, Bryan K. 1969. Climate calculations with a combined ocean–atmosphere model. Journal of the Atmospheric Sciences, 26(4): 786–789, doi: 10.1175/1520–0469(1969)026<0786: CCWACO>2.0.CO;2

    Article  Google Scholar 

  • Manabe S, Bryan K, Spelman M J. 1975. A global ocean–atmosphere climate model. Part I. The atmospheric circulation. Journal of Physical Oceanography, 5(1): 3–29, doi: 10.1175/1520–0485(1975)005<0003:AGOACM>2.0.CO;2

    Article  Google Scholar 

  • Mlawer E J, Taubman S J, Brown P D, et al. 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14): 16663–16682, doi: 10.1029/97JD00237

    Google Scholar 

  • Neelin J D, Latif M, Allaart M A F, et al. 1992. Tropical air–sea interaction in general circulation models. Climate Dynamics, 7(2): 73–104, doi: 10.1007/BF00209610

    Article  Google Scholar 

  • Qiao Fangli, Yuan Yeli, Ezer T, et al. 2010. A three–dimensional surface wave–ocean circulation coupled model and its initial testing. Ocean Dynamics, 60(5): 1339–1355, doi: 10.1007/s10236–010–0326–y

    Article  Google Scholar 

  • Qiao Fangli, Yuan Yeli, Yang Yongzeng, et al. 2004. Wave–induced mixing in the upper ocean: Distribution and application to a global ocean circulation model. Geophysical Research Letters, 31(11): L11303

    Article  Google Scholar 

  • Sasaki H, Kurihara K, Takayabu I, et al. 2006. Preliminary results from the coupled atmosphere–ocean regional climate model at the meteorological research institute. Journal of the Meteorological Society of Japan, 84(2): 389–403, doi: 10.2151/jmsj.84.389

    Article  Google Scholar 

  • Shchepetkin A F, McWilliams J C. 2005. The regional oceanic modeling system (ROMS): a split–explicit, free–surface, topographyfollowing–coordinate oceanic model. Ocean Modelling, 9(4): 347–404, doi: 10.1016/j.ocemod.2004.08.002

    Article  Google Scholar 

  • Shchepetkin A F, McWilliams J C. 2009. Correction and commentary for “Ocean forecasting in terrain–following coordinates: Formulation and skill assessment of the regional ocean modeling system” by Haidvogel et al., J. Comp. Phys. 227, pp. 3595–3624. Journal of Computational Physics, 228(24): 8985–9000, doi: 10.1016/j.jcp.2009.09.002

    Google Scholar 

  • Schrum C, Hübner U, Jacob D, et al. 2003. A coupled atmosphere/ice/ocean model for the North Sea and the Baltic Sea. Climate Dynamics, 21(2): 131–151, doi: 10.1007/s00382–003–0322–8

    Article  Google Scholar 

  • Seo H, Miller A J, Roads J O. 2007. The Scripps Coupled Ocean–Atmosphere Regional (SCOAR) model, with applications in the eastern Pacific sector. Journal of Climate, 20(3): 381–402, doi: 10.1175/JCLI4016.1

    Article  Google Scholar 

  • Skamarock W C, Klemp J B, Dudhia J, et al. 2005. A Description of the Advanced Research WRF Version 2. Available from NCAR; P O BOX3000. Boulder, CO, Vol 88, 7–25

    Google Scholar 

  • Sun Meng, Yin Xunqiang, Yang Yongzeng. 2014. Construction and application in global wave data assimilation of static sample set. Oceanologia et Limnologia Sinica (in Chinese), 45(5): 918–927

    Google Scholar 

  • Sun Meng, Yin Xunqiang, Yang Yongzeng, et al. 2017. An effective method based on dynamic sampling for data assimilation in a global wave model. Ocean Dynamics, 67(3–4): 433–449

    Article  Google Scholar 

  • Wang Guansuo, Qiao Fangli, Xia Changshui. 2010a. Parallelization of a coupled wave–circulation model and its application. Ocean Dynamics, 60(2): 331–339, doi: 10.1007/s10236–010–0274–6

    Article  Google Scholar 

  • Wang Yonggang, Qiao Fangli, Fang Guohong, et al. 2010b. Application of wave–induced vertical mixing to the K profile parameterization scheme. Journal of Geophysical Research: Oceans, 115(C9): C09014

    Book  Google Scholar 

  • Warner J C, Armstrong B, He Ruoying, et al. 2010. Development of a coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling system. Ocean Modelling, 35(3): 230–244, doi: 10.1016/j.ocemod.2010.07.010

    Article  Google Scholar 

  • Warner J C, Sherwood C R, Signell R P, et al. 2008. Development of a three–dimensional, regional, coupled wave, current, and sediment–transport model. Computers & Geosciences, 34(10): 1284–1306

    Article  Google Scholar 

  • Washington W M, Semtner A J Jr, Meehl G A, et al. 2010. A general circulation experiment with a coupled atmosphere, ocean and sea ice model. Journal of Physical Oceanography, 10(12): 1887–1908

    Article  Google Scholar 

  • Yang Yongzeng, Qiao Fangli, Zhao Wei, et al. 2005. MASNUM ocean wave numerical model in spherical coordinates and its application. Haiyang Xuebao (in Chinese), 27(2): 1–7

    Google Scholar 

  • Yang Qingxuan, Zhao Wei, Liang Xinfeng, et al. 2016. Three–dimensional distribution of turbulent mixing in the South China Sea. Journal of Physical Oceanography, 46(3): 769–788, doi: 10.1175/JPO–D–14–0220.1

    Article  Google Scholar 

  • Yin Xunqiang, Qiao Fangli, Yang Yongzeng, et al. 2010. An ensemble adjustment Kalman filter study for Argo data. Chinese Journal of Oceanology and Limnology, 28(3): 626–635, doi: 10.1007/s00343–010–9017–2

    Article  Google Scholar 

  • Yuan Yeli, Hua Feng, Pan Zengdi, et al. 1992. LAGFD–WAM numerical wave model–II. Characteristics inlaid scheme and its application. Acta Oceanologica Sinica, 11(1): 13–23

    Google Scholar 

  • Yuan Yeli, Pan Zengdi, Hua Feng, et al. 1991. LAGFD–WAM numerical wave model–I. basic physical model. Acta Oceanologica Sinica, 10(4): 483–488

    Google Scholar 

  • Yuan Yeli, Tung C C, Huang N E. 1986. Statistical characteristics of breaking waves. In: Phillips O M, Hasselmann K, eds. Wave Dynamics and Radio Probing of the Ocean Surface. Boston, MA: Springer, 265–272

    Google Scholar 

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Correspondence to Yonggang Wang.

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Foundation item: The National Key Research and Development Program of China under contract No. 2017YFC1404201; the NSFCShandong Joint Fund for Marine Science Research Centers under contract No. U1606405; the SOA Program on Global Change and Air- Sea Interactions under contract Nos GASI-IPOVAI-03 and GASI-IPOVAI-02; the National Natural Science Foundation of China under contract Nos 41606040, 41876029, 41776016, 41706035 and 41606036.

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Sun, J., Wei, Z., Xu, T. et al. Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas. Acta Oceanol. Sin. 38, 154–166 (2019). https://doi.org/10.1007/s13131-019-1419-1

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  • DOI: https://doi.org/10.1007/s13131-019-1419-1

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  1. Tengfei Xu