Acta Oceanologica Sinica

, Volume 32, Issue 7, pp 57–65 | Cite as

Improvement of short-term forecasting in the northwest Pacific through assimilating Argo data into initial fields

  • Hongli Fu
  • Peter C. Chu
  • Guijun Han
  • Zhongjie He
  • Wei Li
  • Xuefeng Zhang
Article

Abstract

The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temperature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system (POMgcs). This system uses a sequential multigrid three-dimensional variational (3DVAR) analysis scheme to assimilate observation data. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly (SSHa) and sea surface temperature (SST) in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors (H-RMSEs) and the horizontal distributions of the vertically averaged mean errors (MEs) and the temporal variation of spatially averaged root mean square errors (S-RMSEs). Comparison between the two experiments shows that the assimilation of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.

Key words

data assimilation Argo data western North Pacific ocean prediction 

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References

  1. Chu Peter C, Amezaga G R, Gottshall E L, et al. 2007a. Ocean nowcast/forecast systems for improvement of Naval undersea capabilities. Marine Technol Soc J, 41(2): 23–30CrossRefGoogle Scholar
  2. Chu Peter C, Fan Chenwu. 2010. A conserved minimal adjustment scheme for stabilization of hydrographic profiles. J Atmos Oceanic Technol, 27(6): 1072–1083CrossRefGoogle Scholar
  3. Chu Peter C, Mancini S, Gottshall E L, et al. 2007. Sensitivity of satellite altimetry data assimilation on weapon acoustic preset using MODAS. IEEE J Oceanic Eng, 32: 453–468CrossRefGoogle Scholar
  4. Chu Peter C, Wang GuiHua, Fan Chenwu. 2004. Evaluation of the U.S. Navy’smodular ocean data assimilation system (MODAS) using the South China Sea monsoon experiment (SCSMEX) data. J Oceanogr, 60: 1007–1021CrossRefGoogle Scholar
  5. Galanis G, Chu Peter C, Kallos G. 2011. Statistical post processes for the improvement of the results of numerical wave prediction models. A combination of Kolmogorov-Zurbenko and Kalman filters. J Operat Oceanogr, 4(1): 23–31Google Scholar
  6. Griffa A, Molcard A, Raicich F, et al. 2006. Assessment of the impact of TS assimilation from ARGO floats in the Mediterranean Sea. Ocean Sci, 2: 237–248CrossRefGoogle Scholar
  7. Han Guijun, Li Wei, Zhang Xuefeng, et al. 2011. A regional ocean reanalysis system for China coastal waters and adjacent seas. Advances in Atmospheric Sciences, 28(3): 682–690CrossRefGoogle Scholar
  8. He Zhongjie, Han Guijun, Li Wei, et al. 2010. Experiments on assimilating of satellite data in the China seas and adjacent seas (in Chinese). Periodical of Ocean University of China, 40(9): 1–7Google Scholar
  9. Li Wei, Xie Yuanfu, Deng Shiowming, et al. 2010. Application of the multi-grid method to the two-dimensional doppler radar radial velocity data assimilation. J Atmos Oceanic Tech, 27(2): 319–332CrossRefGoogle Scholar
  10. Li Wei, Xie Yuanfu, He Zhongjie, et al. 2008. Application of the multigrid data assimilation scheme to the China seas’ temperature forecast. J Atmos Oceanic Technol, 25(11): 2106–2116CrossRefGoogle Scholar
  11. Liu Yimin, Zhang Renhe, Yin Yonghong, et al. 2004. The application of ARGO data to the global ocean data assimilation operational system of NCC. Acta Meteorologica Sinica, 19: 355–365Google Scholar
  12. Marshall J, Hill C, Perelman L, et al. 1997. Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modelling. J Geophys Res, 102(C3): 5733–5753CrossRefGoogle Scholar
  13. Shu Yeqiang, Wang Dongxiao, Zhu Jiang et al. 2011. 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. Ocean Modeling, 36(3–4):228–241CrossRefGoogle Scholar
  14. Shu Yeqiang, Zhu Jiang, Wang Dongxiao, et al. 2011. Assimilating remote sensing and in situ observations into a coastal model of northern South China Sea using ensemble Kalman filter. Continental Shelf Research, 31(6): S24–S36CrossRefGoogle Scholar
  15. Troccoli A, Balmaseda MA, Segschneider J, et al. 2002. Salinity adjustments in the presence of temperature data assimilation. Mon Wea Rev, 130: 89–102CrossRefGoogle Scholar
  16. Wong A P S, Johnson G C, Owens W B. 2003. Delayed-mode calibration of autonomous CTD profiling float salinity data by S-climatology. J Atmos. Oceanic Tech, 20: 308–318CrossRefGoogle Scholar
  17. Xiao Xianjun, Wang Dongxiao, Xu Jianjun. 2006. The assimilation experiment in the southwestern South China Sea in summer 2000. Chinese Science Bulletin, 51(2):31–37CrossRefGoogle Scholar
  18. Zhu Jiang, Yan Changxiang. 2006. Nonlinear balance constraints in 3DVAR data assimilation. Science in China: D, 49: 331–336CrossRefGoogle Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hongli Fu
    • 1
  • Peter C. Chu
    • 2
  • Guijun Han
    • 1
  • Zhongjie He
    • 1
  • Wei Li
    • 1
  • Xuefeng Zhang
    • 1
  1. 1.Key Laboratory of State Oceanic Adminstration for Marine Environmental Information Technology, National Marine Data and Information ServiceState Oceanic AdministrationTianjinChina
  2. 2.Naval Ocean Analysis and Prediction LaboratoryNaval Postgraduate SchoolMontereyUSA

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