Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-995-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-995-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Feb 2022
Development and technical paper |
| 03 Feb 2022
| 03 Feb 2022
Improvements in the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2)
Xueming Zhu
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai, 519000, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Ziqing Zu
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Miaoyin Zhang
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Yunfei Zhang
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Hui Wang
CORRESPONDING AUTHOR
Institute of Marine Science and Technology, Shandong University,
Qingdao, Shandong, 266237, China
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
Ang Li
National Marine Environmental Forecasting Center, Key Laboratory of
Marine Hazards Forecasting, Ministry of Natural Resources, Beijing, 100081, China
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Cited articles
Bao, X., Wan, X., Gao, G., and Wu, D.: The characteristics of the seasonal
variability of the sea surface temperature field in the Bohai Sea, the
Huanghai Sea and the East China Sea from AVHRR data, Acta Oceanol. Sin., 24,
125–133, 2002.
Barnier, B., Siefridt, L., and Marchesiello, P.: Thermal forcing for a
global ocean circulation model using a three-year climatology of ECMWF
analyses, J. Mar. Syst., 6, 363–380, https://doi.org/10.1016/0924-7963(94)00034-9, 1995.
Barnier, B., Patrick, M., Miranda, A. P. D., Molines, J.-M., and Coulibaly,
M.: A sigma-coordinate primitive equation model for studying the circulation
in the South Atlantic, Part I: Model configuration with error estimates,
Deep-Sea Res. Pt. I, 45, 543–572, 1998.
Beckmann, A. and Haidvogel, D. B.: Numerical simulation of flow around a tall
isolated seamount, Part I: problem formulation and model accuracy, J. Phys.
Oceanogr. 23, 1737–1753, 1993.
Bloom, S. C., Takacs, L. L., Silva, A. M. D., and Ledvina, D.: Data
Assimilation using incremental Analysis Updates, Mon. Weather Rev.,
124, 1256–1271, 1996.
Cai, S., Xie, J., Xu, J., Wang, D., Chen, Z., Deng, X., and Long, X.:
Monthly variation of some parameters about internal solitary waves in the
South China sea, Deep-Sea Res. Pt. I, 84, 73–85, https://doi.org/10.1016/j.dsr.2013.10.008, 2014.
Carnes, M. R.: Description and Evaluation of GDEM-V3.0, Naval Research Laboratory,Stennis Space Center, MS, NRL/MR/7330-09-9165, 2009.
Carton, J. A. and Giese, B. S.: A Reanalysis of Ocean Climate Using Simple
Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999–3017,
https://doi.org/10.1175/2007MWR1978.1, 2008.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A New Ocean Climate Reanalysis, J. Clim., 31, 6967–6983, https://doi.org/10.1175/JCLI-D-18-0149.1, 2018, and data available at: https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.1_mn_download.htm, last access: 3 January 2021, and https://dsrs.atmos.umd.edu/DATA/soda3.3.2/REGRIDED/ocean/, last access: 3 January 2021.
Chu, P. C. and Li, R.: South China Sea isopycnal-surface circulation, J.
Phys. Oceanogr., 30, 2419–2438, https://doi.org/10.1175/1520-0485(2000)030<2419:SCSISC>2.0.CO;2, 2000.
Cummings, J. A.: Operational multivariate ocean data assimilation, Quart. J.
R. Met. Soc., 131, 3583–3604, https://doi.org/10.1256/qj.05.105, 2005.
Ding, R., Xuan, J., Zhang, T., Zhou, L., Zhou, F., Meng, Q., and Kang, I.:
Eddy-Induced Heat Transport in the South China Sea, J. Phys. Oceanogr.,
51, 2329–2349, https://doi.org/10.1175/JPO-D-20-0206.1, 2021.
Divakaran, P., Brassington, G. B., Ryan, A. G., Regnier, C., Spindler, T.,
Mehra, A., Hernandez, F., Smith, G. C., Liu, Y., and Davidson, F.: GODAE
OceanView Inter-comparison for the Australian Region, J. Oper. Oceanogr., 8, s112–s126, https://doi.org/10.1080/1755876X.2015.1022333, 2015.
Dombrowsky, E., Bertino, L., Brassington, G. B., Chassignet, E. P.,
Davidson, F., Hurlburt, H. E., Kamachi, M., Lee, T., Martin, M. J., Mei, S.,
and Tonani, M.: GODAE systems in operation, Oceanography, 22, 80–95,
https://doi.org/10.5670/oceanog.2009.68, 2009.
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ. [data set], 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012, data available at: https://resources.marine.copernicus.eu/product-detail/SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001/DATA-ACCESS, last access: 28 January 2022.
E.U. Copernicus Marine Service Information: SST of the in situ drifting
BUOY, Copernicus.eu [data set], https://resources.marine.copernicus.eu/product-detail/INSITU_GLO_UV_L2_REP_OBSERVATIONS_013_044/INFORMATION, last access: 28 January 2022a.
E.U. Copernicus Marine Service Information: SLA global ocean along-track L3 sea surface heights reprocessed (1993–ongoing) tailored for data assimilation, Copernicus.eu [data set], https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_L3_MY_008_062/DATA-ACCESS, last access: 28 January 2022b.
Evensen, G.: The Ensemble Kalman Filter: Theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J.
B.: Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for
the COARE Algorithm, J. Clim., 16, 571–591, 2003.
Farris, A. and Wimbush, M.: Wind-induced Kuroshio intrusion into the South
China Sea, J. Oceanogr., 52, 771–784, https://doi.org/10.1007/BF02239465, 1996.
GEBCO: GEBCO_2014 Grid, GEBCO [data set], https://www.gebco.net/data_and_products/historical_data_sets/#gebco_2014, last access: last access: 28 January 2022.
Hellerman, S. and Rosenstein, M.: Normal Monthly Wind Stress Over the World
Ocean with Error Estimates, J. Phys. Oceanogr., 13, 1093–1104, 1983.
Hernandez, F., Bertino, L., Brassington, G. B., Chassignet, E., Cummings,
J., Davidson, F., Drevillon, M., Garric, G., Kamachi, M., Lellouche, J.-M.,
Mahdon, R., Martin, M. J., Ratsimandresy, A., and Regnier, C.: Validation
and intercomparison studies within GODAE, Oceanography, 22, 128–143,
https://doi.org/10.5670/oceanog.2009.71, 2009.
Hernandez, F., Blockley, E., Brassington, G. B., Davidson, F., Divakaran,
P., Drévillon, M., Ishizaki, S., Garcia-Sotillo, M., Hogan, P. J.,
Lagemaa, P., Levier, B., Martin, M., Mehra, A., Mooers, C., Ferry, N., Ryan,
A., Regnier, C., Sellar, A., Smith, G. C., Sofianos, S., Spindler, T.,
Volpe, G., Wilkin, J., Zaron, E. D., and Zhang, A.: Recent progress in
performance evaluations and near real-time assessment of operational ocean
products, J. Operat. Oceanogr., 8, s221–s238, https://doi.org/10.1080/1755876X.2015.1050282, 2015.
Hwang, C. and Chen, S.-A.: Circulations and eddies over the South China Sea
derived from TOPEX/Poseidon altimetry, J. Geophys. Res., 105, 23943–23965,
https://doi.org/10.1029/2000JC900092, 2000.
Ji, Q., Zhu, X., Wang, H., Liu, G., Gao, S., Ji, X., and Xu, Q.:
Assimilating operational SST and sea ice analysis data into an operational
circulation model for the coastal seas of China, Acta Oceanol. Sin., 34,
54–64, https://doi.org/10.1007/s13131-015-0691-y, 2015.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J.,
Fiorino, M., and Potter, G. L.: Ncep-Doe Amip-II reanalysis (R-2), Bull.
Amer. Meteor. Soc., 83, 1631–1643, https://doi.org/10.1175/BAMS-83-11-1631, 2002.
Kourafalou, V. H., Mey, P. D., Hénaff, M. L., Charria, G., Edwards, C.
A., He, R., Herzfeld, M., Pascual, A., Stanev, E. V., Tintoré, J., Usui,
N., van der Westhuysen, A. J., Wilkin, J., and Zhu, X.: Coastal Ocean
Forecasting: system integration and evaluation, J. Oper. Oceanogr., 8,
s127–s146, https://doi.org/10.1080/1755876X.2015.1022336, 2015.
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air–sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009.
Lee, M.-S. and Barker, D.: Preliminary Tests of First Guess at Appropriate
Time (FGAT) with WRF 3DVAR and WRF Model, Asia-Pac. J. Atmos. Sci., 41,
495–505, 2005.
Lee, M.-S., Barker, D., Huang, W., and Kuo, Y.-H.: First Guess at
Appropriate Time (FGAT) with WRF 3DVAR, Preprints for WRF/MM5 Users'
Workshop, Boulder, CO, 2004.
Lellouche, J.-M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N., Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y., Daudin, A., and De Nicola, C.: Evaluation of global monitoring and forecasting systems at Mercator Océan, Ocean Sci., 9, 57–81, https://doi.org/10.5194/os-9-57-2013, 2013.
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time
Li, A., Zhang, M., Zhu, X., Zu, Z., and Wang, H.: A research on the optimal
approach of CFSR surface flux data correction based on different surface
forcing modes, Haiyang Xuebao,
41, 51–63, https://doi.org/10.3969/j.issn.0253-4193.2019.11.006, 2019. (In Chinese with English abstract)
Li, A., Zhu, X., Zhang, Y., Ren, S., Zhang, M., Zu, Z., and Wang, H.: Recent
improvements to the physical model of the Bohai Sea, the Yellow Sea and the
East China Sea Operational Oceanography Forecasting System, Acta
Oceanol. Sin., 40, 1–17, https://doi.org/10.1007/s13131-021-1840-0, 2021.
Li, B., Cao, A., and Lv, X.: Three-dimensional numerical simulation of M2
internal tides in the Luzon Strait, Acta Oceanol. Sin., 34, 55–62,
https://doi.org/10.1007/s13131-015-0748-y, 2015.
Li, H., Song, D., Chen, X., Qian, H., Mu, L., and Song, J.: Numerical study
of M2 internal tide generation and propagation in the Luzon Strait, Acta
Oceanol. Sin., 30, 23–32, https://doi.org/10.1007/s13131-011-0144-1, 2011.
Liu, Z., Chen, X., Yu, J., Xu, D., and Sun, C.: Kuroshio intrusion into the
South China Sea with an anticyclonic eddy: evidence from underwater glider
observation, J. Oceanol. Limnol., 37, 1469–1480, https://doi.org/10.1007/s00343-019-8290-y, 2019.
Mao, Q., Shi, P., and Qi, Y.: Sea surface dynamic topography and geostrophic
current over the South China Sea from Geosat altimeter observation, Acta
Oceanol. Sin., 21, 11–16, 1999.
Marchesiello, P., Debreu, L., and Couvelard, X.: Spurious diapycnal mixing
in terrain-following coordinate models: The problem and a solution, Ocean
Model., 26, 156–169, https://doi.org/10.1016/j.ocemod.2008.09.004, 2009.
Nan, F., Xue, H., and Yu, F.: Kuroshio intrusion into the South China Sea: A
review, Prog. Oceanogr., 137, 314–333, https://doi.org/10.1016/j.pocean.2014.05.012, 2015.
Naughten, K. A., Galton-Fenzi, B. K., Meissner, K. J., England, M. H.,
Brassington, G. B., Colberg, F., Hattermann, T., and Debernard, J. B.:
Spurious sea ice formation caused by oscillatory ocean tracer advection
schemes, Ocean Model., 116, 108–117, https://doi.org/10.1016/j.ocemod.2017.06.010, 2017.
NOAA/OAR/ESRL PSL: NCEP_Reanalysis 2, NOAA/OAR/ESRL PSL [data set], Boulder, Colorado, USA, https://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html, last access: 3 January 2021.
Oke, P. R., Brassington, G. B., Griffin, D. A., and Schiller, A.: The
Bluelink ocean data assimilation system (BODAS), Ocean Model., 21, 46–70,
https://doi.org/10.1016/j.ocemod.2007.11.002, 2008.
Ourmières, Y., Brankart, J.-M., Berline, L., Brasseur, P., and Verron,
J.: Incremental Analysis Update Implementation into a Sequential Ocean Data
Assimilation System, J. Atmos. Ocean. Technol., 23, 1729–1744, 2006.
Qiu, B. and Chen, S.: Interannual-to-Decadal Variability in the Bifurcation
of the North Equatorial Current off the Philippines, J. Phys. Oceanogr., 40,
2525–2538, https://doi.org/10.1175/2010JPO4462.1, 2010.
Ryan, A. G., Regnier, C., Divakaran, P., Spindler, T., Mehra, A., Smith, G.
C., Davidson, F., Hernandez, F., Maksymczuk, J., and Liu, Y.: GODAE
OceanView Class 4 forecast verification framework: global ocean
inter-comparison, J. Oper. Oceanogr. [data set], 8, s98–s111,
https://doi.org/10.1080/1755876X.2015.1022330, 2015, data available at ftp://ftp.ifremer.fr/ifremer/argo/, last access: 28 January 2022.
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y. T., Chuang, H. Y., Juang, H. M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J. K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.
Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G.,
and Goldberg, M.: The NCEP climate forecast system reanalysis, Bull. Amer.
Meteor. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010a.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010b.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products, updated daily, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D61C1TXF, 2011.
Saha, K., Zhao, X., Zhang, H.-m., Casey, K. S., Zhang, D., Baker-Yeboah, S., Kilpatrick, K. A., Evans, R. H., Ryan, T., and Relph, J. M.: AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4 km sea surface temperature for 1981–Present, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/v52j68xx, 2018, data available at: http://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/, last access: 3 January 2021.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y.-T., Cuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R.,
Mendez, M. P., Dool, H. V. D., Zhang, Q., Wang, W., Chen, M., and Becker,
E.: The NCEP Climate Forecast System Version 2, J. Clim., 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Sandery, P.: Data assimilation cycle length and observation impact in mesoscale ocean forecasting, Geosci. Model Dev., 11, 4011–4019, https://doi.org/10.5194/gmd-11-4011-2018, 2018.
Shchepetkin, A. F. and McWilliams, J. C.: A method for computing horizontal
pressure-gradient force in an oceanic model with a nonaligned vertical
coordinate, J. Geophys. Res., 108, 3090, https://doi.org/10.1029/2001JC001047, 2003.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling
system (ROMS): a split-explicit, free-surface,
topography-following-coordinate oceanic model, Ocean Model., 9, 347–404,
https://doi.org/10.1016/j.ocemod.2004.08.002, 2005.
Song, Y. and Haidvogel, D.: A Semi-implicit Ocean Circulation Model Using a
Generalized Topography-Following Coordinate System, J. Comp. Phys., 115,
228–244, https://doi.org/10.1006/jcph.1994.1189, 1994.
Taburet, G.: SL-TAC team, Quality Information Document for Sea Level TAC
DUACS Products, Copernicus Marine Environment Monitoring Service, 2.4, 2018.
Tonani, M., Balmaseda, M., Bertino, L., Blockley, E., Brassington, G. B.,
Davidson, F., Drillet, Y., Hogan, P., Kuragano, T., Lee, T., Mehra, A.,
Paranathara, F., Tanajura, C. A. S., and Wang, H.: Status and future of
global and regional ocean prediction systems, J. Oper. Oceanogr., 8,
s201–s220, https://doi.org/10.1080/1755876X.2015.1049892, 2015.
Tsujino, H., Usui, N., and Nakano, H.: Dynamics of Kuroshio path variations
in a high-resolution general circulation model, J. Geophys. Res., 111,
C11001, https://doi.org/10.1029/2005JC003118, 2006.
Usui, N., Ishizaki, S., Fujii, Y., Tsujino, H., Yasuda, T., and Kamachi, M.:
Meteorological Research Institute multivariate ocean variational estimation
(MOVE) system: Some early results, Adv. Space Res., 37, 806–822,
https://doi.org/10.1016/j.asr.2005.09.022, 2006.
Usui, N., Wakamatsu, T., Tanaka, Y., Hirose, N., Toyoda, T., Nishikawa, S.,
Fujii, Y., Takatsuki, Y., Igarashi, H., Nishikawa, H., Ishikawa, Y.,
Kuragano, T., and Kamachi, M.: Four-dimensional variational ocean
reanalysis: a 30-year high-resolution dataset in the western North Pacific
(FORA-WNP30), J. Oceanogr., 73, 205–233, https://doi.org/10.1007/s10872-016-0398-5, 2017.
Wang, J.: Global Linear Stability of the Two-Dimensional Shallow-Water
Equations: An Application of the Distributive Theorem of Roots for
Polynomials on the Unit Circle, Mon. Weather Rev., 124, 1301–1310, 1996.
Wang, Q., Zeng, L., Chen, J., He, Y., Zhou, W., and Wang, D.: The Linkage of
Kuroshio Intrusion and Mesoscale Eddy Variability in the Northern South
China Sea: Subsurface Speed Maximum, Geophys. Res. Lett., 47, e2020GL087034,
https://doi.org/10.1029/2020GL087034, 2020.
Wang, W., Wang, D., Zhou, W., Liu, Q., Yu, Y., and Li, C.: Impact of the
South China Sea Throughflow on the Pacific Low-Latitude Western Boundary
Current: A Numerical Study for Seasonal and Interannual Time Scales, Adv.
Atmos. Sci., 28, 1367–1376, 10.1007/s00376-011-0142-4, 2011.
Wei, Z., Li, S., Susanto, R. D., Wang, Y., Fan, B., Xu, T., Sulistiyo, B.,
Adi, T. R., Setiawan, A., Kuswardani, A., and Fang, G.: An overview of
10-year observation of the South China Sea branch of the Pacific to Indian
Ocean throughflow at the Karimata Strait, Acta Oceanol. Sin., 38, 1–11,
https://doi.org/10.1007/s13131-019-1410-x, 2019.
Xie, J. and Zhu, J.: Ensemble optimal interpolation schemes for assimilating
Argo profiles into a hybrid coordinate ocean model, Ocean Model.,
33, 283–298, https://doi.org/10.1016/j.ocemod.2010.03.002.
Xu, D., Zhuang, W., and Yan, Y.: Could the two anticyclonic eddies during winter 2003/2004 be reproduced and predicted in the northern South China Sea?, Ocean Sci., 15, 97–111, https://doi.org/10.5194/os-15-97-2019, 2019.
Zhang, S., Qiu, F., Zhang, J., Shen, J., and Cha, J.: Monthly variation on
the propagation and evolution of internal solitary waves in the northern
South China Sea, Cont. Shelf Res., 171, 21–29, https://doi.org/10.1016/j.csr.2018.10.014, 2018.
Zhang, Z., Tian, J., Qiu, B., Zhao, W., Chang, P., Wu, D., and Wan, X.:
Observed 3D Structure, Generation, and Dissipation of Oceanic Mesoscale
Eddies in the South China Sea, Sci. Rep., 6, 24349, https://doi.org/10.1038/srep24349, 2016.
Zhao, Z. and Alford, M. H.: Source and propagation of internal solitary
waves in the northeastern South China Sea, J. Geophys. Res., 111, 1–14,
https://doi.org/10.1029/2006JC003644, 2006.
Zheng, Q., Xie, L., Zheng, Z., and Hu, J.: Progress in Research of Mesoscale
Eddies in the South China Sea, Adv. Mar. Sci., 35, 131–158,
https://doi.org/10.3969/j.issn.1671-6647.2017.02.001, 2017.
Zhu, X.: Multi-source Ocean data Online Assimilation System (2.0), Zenodo [code], https://doi.org/10.5281/zenodo.5215783, 2021.
Zhu, X., Wang, H., Liu, G., Régnier, C., Kuang, X., Wang, D., Ren, S., Jing, Z., and Drévillon, M.: Comparison and validation of global and regional ocean forecasting systems for the South China Sea, Nat. Hazards Earth Syst. Sci., 16, 1639–1655, https://doi.org/10.5194/nhess-16-1639-2016, 2016.
Zu, Y., Sun, S., Zhao, W., Li, P., Liu, B., and Fang, Y.: Seasonal
characteristics and formation mechanism of the thermohaline structure of
mesoscale eddy in the South China Sea, Acta Oceanol. Sin., 38, 29–38,
https://doi.org/10.1007/s13131-018-1222-4, 2019.
Short summary
SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS's physical model and data assimilation scheme in order to improve its forecasting skill. The three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.
SCSOFS has provided daily updated marine forecasting in the South China Sea for the next 5 d...
