Abstract
The Ensemble Kalman Filter (EnKF) was implemented to an ocean circulation modeling system of the Northwest Pacific Ocean. The study area includes the northwestern part of the Pacific Ocean, the East China Sea, the Yellow Sea and the East/Japan Sea. The numerical model used for the system was the Regional Ocean Model System, which is a 3-dimensional primitive-equation ocean circulation model. The performance of EnKF was evaluated by assimilating satellite-observed Sea Surface Temperature (SST) data into the numerical ocean model every 7 day for year 2003. SST data were obtained from 30 fixed points at a time. The number $N$ of ensemble members used in this study was 16. Without localization of covariance matrix, ensemble spread (EnSP) drastically decreased due to rank deficiency and the large correlation between two distant state variables. To resolve the ensemble collapse, localization of covariance matrix was performed and EnSP did not collapse throughout the experiment. Root -mean-square (RMS) error of SST from the assimilative model (RMS error= 2.2°C) was smaller than that of the non-assimilative model (RMS error= 3.2°C). This work provides promising results that can be further explored in establishing operational ocean prediction systems for the Northwest Pacific including its marginal seas.
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Seo, GH., Kim, S., Choi, BJ., Cho, YK., Kim, YH. (2009). Implementation of the Ensemble Kalman Filter into a Northwest Pacific Ocean Circulation Model. In: Park, S.K., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71056-1_18
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DOI: https://doi.org/10.1007/978-3-540-71056-1_18
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