基于ROMS模式的南海SST与SSH四维变分同化研究
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摘要
卫星遥感观测获得了大量高分辨率的海面实时信息,包括海面温度(SST)和海面高度(SSH)等,同化进入数值模式可有效提升模拟精度。本文基于ROMS模式与四维变分同化方法(4DVAR),使用AVHRR SST和AVISO SSH数据,开展了南海区域同化实验。为检验同化的效果,分别利用HYCOM再分析资料和Argo温盐实测数据分析了同化结果的海面高度、流场及温盐剖面的精度。对比结果表明,SST和SSH的同化能够改善ROMS的模拟结果:同化后海面高度场能够更为准确地捕捉海洋的中尺度特征,与HYCOM海面高度再分析资料相比,平均绝对偏差和均方根误差分别为0.054 m和0.066 m;与HYCOM 10 m层流场相比,东向与北向流速平均绝对偏差分别为0.12 m/s和0.11 m/s,相比未同化均提升约0.01 m/s;温盐同化结果与Argo温盐实测具有较高的一致性,温度和盐度平均绝对偏差为0.45℃、0.077,均方根误差为0.91℃、0.11,单个的温盐廓线对比说明,同化结果与HYCOM再分析资料精度相当。
Oceanic surface information with large scale, real-time, high resolution has been collected by satellite remote sensing instruments, including sea surface temperature(SST) and sea surface height(SSH), which could be assimilated in ocean model to enhance the simulation. In this paper, an experiment in South China Sea is conducted based on ROMS and 4DVAR method, by assimilating the AVHRR SST and AVISO sea level anomalies(SLA) data. To confirm the efficiency of the assimilation, the HYCOM reanalysis data and Argo in situ profile are applied to validate the SSH, current and temperature-salinity(T-S) field of the assimilation. The results show that the simulation is enhanced after the SST and SSH assimilation. The capability of mesoscale characteristics detection in SSH outcomes is promoted, and the absolute bias(Abias) and root mean square error(RMSE) are 0.054 m and 0.066 m, compared with the HYCOM surface elevation. Considering the velocity evaluation with HYCOM, the averaged Abias of the eastward and northward velocity at 10 m layer is 0.12 m/s and 0.011 m/s, accompanied with a promotion of 0.01 m/s. Meanwhile, the assimilation results agree with the Argo T-S profiles well, the averaged Abias of T-S is 0.45℃, 0.077, while the RMSE is 0.91℃ and 0.11, respectively. Moreover, the analysis of single T-S profile indicates that the assimilation results achieve a comparable accuracy with HYCOM.
Oceanic surface information with large scale, real-time, high resolution has been collected by satellite remote sensing instruments, including sea surface temperature(SST) and sea surface height(SSH), which could be assimilated in ocean model to enhance the simulation. In this paper, an experiment in South China Sea is conducted based on ROMS and 4DVAR method, by assimilating the AVHRR SST and AVISO sea level anomalies(SLA) data. To confirm the efficiency of the assimilation, the HYCOM reanalysis data and Argo in situ profile are applied to validate the SSH, current and temperature-salinity(T-S) field of the assimilation. The results show that the simulation is enhanced after the SST and SSH assimilation. The capability of mesoscale characteristics detection in SSH outcomes is promoted, and the absolute bias(Abias) and root mean square error(RMSE) are 0.054 m and 0.066 m, compared with the HYCOM surface elevation. Considering the velocity evaluation with HYCOM, the averaged Abias of the eastward and northward velocity at 10 m layer is 0.12 m/s and 0.011 m/s, accompanied with a promotion of 0.01 m/s. Meanwhile, the assimilation results agree with the Argo T-S profiles well, the averaged Abias of T-S is 0.45℃, 0.077, while the RMSE is 0.91℃ and 0.11, respectively. Moreover, the analysis of single T-S profile indicates that the assimilation results achieve a comparable accuracy with HYCOM.
引文
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[16] Atlas R, Hoffman R N, Ardizzone J, et al. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications[J]. Bulletin of the American Meteorological Society, 2011, 92(2): 157-174.
[17] Kalnay E, Kanamitsu M, Kistler R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society, 1996, 77(3): 437-471.
[18] Reynolds R W, Smith T M, Liu Chunying, et al. Daily high-resolution-blended analyses for sea surface temperature[J]. Journal of Climate, 2007, 20(22): 5473-5496.
[19] Powell B S, Arango H G, Moore A M, et al. 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)[J]. Ocean Modelling, 2008, 23(3/4): 130-145.
[20] Cummings J A, Smedstad O M. Variational data assimilation for the global ocean[M]//Park S, Xu L. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. Ⅱ). Berlin, Heidelberg: Springer, 2013: 303-343.
[21] 苏纪兰. 南海环流动力机制研究综述[J]. 海洋学报, 2005, 27(6): 1-8. Su Jilan. Overview of the South China Sea circulation and its dynamics[J]. Haiyang Xuebao, 2005, 27(6): 1-8.
[22] Argo. Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE[EB/OL]. (2000-09-12)[2015-9-20]. http://doi.org/10.17882/42182.
[2] Ezer T, Mellor G L. Continuous assimilation of Geosat altimeter data into a three-dimensional primitive equation gulf stream model[J]. Journal of Physical Oceanography, 1994, 24(4): 832-847.
[3] Liu Danian, Zhu Jiang, Shu Yeqiang, et al. Model-based assessment of a Northwestern Tropical Pacific moored array to monitor intraseasonal variability[J]. Ocean Modelling, 2018, 126: 1-12.
[4] Liu Danian, Shi Ping, Shu Yeqiang, et al. Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint[J]. Acta Oceanologica Sinica, 2016, 35(1): 30-37.
[5] Shu Yeqiang, Wang Dongxiao, Zhu Jiang, et al. The 4-D structure of upwelling and Pearl River plume in the northern South China Sea during summer 2008 revealed by a data assimilation model[J]. Ocean Modelling, 2011, 36(3/4): 228-241.
[6] Cooper M, Haines K. Altimetric assimilation with water property conservation[J]. Journal of Geophysical Research, 1996, 101(C1): 1059-1078.
[7] White W B, Tai C K, Holland W R. Continuous assimilation of Geosat Altimetric sea level observations into a numerical synoptic ocean model of the California current[J]. Journal of Geophysical Research, 1990, 95(C3): 3127-3148.
[8] Yan Changxiang, Zhu Jiang, Li Rongfeng, et al. Roles of vertical correlations of background error and T-S relations in estimation of temperature and salinity profiles from sea surface dynamic height[J]. Journal of Geophysical Research, 2004, 109(C8): C08010.
[9] Ratheesh S, Sharma R, Basu S. Projection-based assimilation of satellite-derived surface data in an Indian Ocean circulation model[J]. Marine Geodesy, 2012, 35(2): 175-187.
[10] Shu Yeqiang, Zhu Jiang, Wang Dongxiao, et al. Assimilating remote sensing and in situ observations into a coastal model of northern South China Sea using ensemble Kalman filter[J]. Continental Shelf Research, 2011, 31(6): S24-S36.
[11] Zeng Xuezhi, Peng Shiqiu, Li Zhijin, et al. A reanalysis dataset of the South China Sea[J]. Scientific Data, 2014, 1: 140052.
[12] Li Zhijin, Chao Yi, McWilliams J C, et al. A three-dimensional variational data assimilation scheme for the regional ocean modeling system[J]. Journal of Atmospheric and Oceanic Technology, 2008, 25(11): 2074-2090.
[13] Moore A M, Arango H G, Broquet G, et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part Ⅰ-System overview and formulation[J]. Progress in Oceanography, 2011, 91(1): 34-49.
[14] Moore A M, Arango H G, Broquet G, et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part Ⅱ-Performance and application to the California Current System[J]. Progress in Oceanography, 2011, 91(1): 50-73.
[15] Moore A M, Arango H G, Broquet G, et al. The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems: Part Ⅲ-Observation impact and observation sensitivity in the California Current System[J]. Progress in Oceanography, 2011, 91(1): 74-94.
[16] Atlas R, Hoffman R N, Ardizzone J, et al. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications[J]. Bulletin of the American Meteorological Society, 2011, 92(2): 157-174.
[17] Kalnay E, Kanamitsu M, Kistler R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society, 1996, 77(3): 437-471.
[18] Reynolds R W, Smith T M, Liu Chunying, et al. Daily high-resolution-blended analyses for sea surface temperature[J]. Journal of Climate, 2007, 20(22): 5473-5496.
[19] Powell B S, Arango H G, Moore A M, et al. 4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)[J]. Ocean Modelling, 2008, 23(3/4): 130-145.
[20] Cummings J A, Smedstad O M. Variational data assimilation for the global ocean[M]//Park S, Xu L. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. Ⅱ). Berlin, Heidelberg: Springer, 2013: 303-343.
[21] 苏纪兰. 南海环流动力机制研究综述[J]. 海洋学报, 2005, 27(6): 1-8. Su Jilan. Overview of the South China Sea circulation and its dynamics[J]. Haiyang Xuebao, 2005, 27(6): 1-8.
[22] Argo. Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE[EB/OL]. (2000-09-12)[2015-9-20]. http://doi.org/10.17882/42182.
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