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OS | Articles | Volume 15, issue 2
Ocean Sci., 15, 401–412, 2019
https://doi.org/10.5194/os-15-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Ocean Sci., 15, 401–412, 2019
https://doi.org/10.5194/os-15-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Apr 2019

Research article | 12 Apr 2019

A simple predictive model for the eddy propagation trajectory in the northern South China Sea

Jiaxun Li et al.

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Cited articles

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Ali, M. M., Kishtawal, C. M., and Jain, S.: Predicting cyclone tracks in the north Indian Ocean: An artificial neural network approach, Geophys. Res. Lett., 34, L04603, https://doi.org/10.1029/2006GL028353, 2007. 
Bao, S., Zhang, R. Wang, H., Yan, H., and Yu, Y.: Salinity profile estimation in the Pacific Ocean from satellite Surface salinity observations, J. Atmos. Oceanic. Technol., 36, 53–68, 2019. 
Cai, S., Long, X., Wu, R., and Wang, S.: Geographical and monthly variability of the first baroclinic rossby radius of deformation in the south china sea, J. Mar. Syst., 74, 711–720, 2008. 
Canes, M. R.: Description and evaluation of GDEM-V3.0, Rep. NRL/MR/7330-09-9165, Nav. Res. Lab, Washington, DC, 2009. 
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Short summary
A novel predictive model is built for eddy propagation trajectory using the multiple linear regression method. This model relates various oceanic parameters to eddy propagation position changes in the northern South China Sea (NSCS). Its performance is examined in the NSCS based on five years of satellite altimeter data, and demonstrates its significant forecasting skills over a 4-week forecast window compared to the traditional persistence method.
A novel predictive model is built for eddy propagation trajectory using the multiple linear...
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