• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.345-357
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• 2001

This review provides a summary of data assimilation applied to the seas around Korea. Currently the worldwide efforts are devoted to applying advanced assimilation to realistic cases, thanks to improvements in mathematical foundations of assimilation methods and the computing capabilities, and also to the availability of extensive observational data such as from satellites. Over the seas around Korea, however, the latest developments in the advanced assimilation methods have yet to be applied. Thus it would be timely to review the progress in data assimilation over the seas. Firstly, the definition and necessity of data assimilation are described, continued by a brief summary of major assimilation methods. Then a review of past research on the ocean data assimilation in the regional seas around Korea is given and future trends are considered. Special consideration is given to the assimilation of remotely-sensed data.

• Ocean and Polar Research
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• v.42 no.3
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• pp.179-194
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• 2020

The present study aims to evaluate the effects of satellite-based SST (OSTIA) assimilation on a regional ocean circulation model for the Yellow and East China Seas (YECS), using three different assimilation methods: the Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), and 4-Dimensional Variational (4DVAR) techniques, which are widely used in the ocean modeling communities. The model experiments show that an improved initial condition by assimilating the SST affects the seasonal water temperature and water mass distributions of the YECS. In particular, the SST data assimilation influences the temperature structures horizontally and vertically in winter, thereby improving the behavior of the YS warm current water. This is due to the fact that during wintertime the water column is well mixed, which is directly updated by the SST assimilation. The model comparisons indicate that the SST assimilation can improve the model performance in resolving the subsurface structures in wintertime, but has a relatively small impact in summertime due to the strong stratification. The differences among the different assimilation experiments are obvious when the SST was sharply changed due to a typhoon passage. Overall, the EnKF and 4DVAR show better agreement with the observations than the EnOI. The relatively low performance of EnOI under storm conditions may be related with a limitation of EnOI method whereby an analysis is obtained from a number of climatological fields, and thus the typhoon-induced SST changes in short-time scales may not be adequately reflected in the data assimilation.

• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• Atmosphere
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• v.25 no.2
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• pp.353-366
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• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

• Ocean and Polar Research
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• v.23 no.2
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• pp.181-188
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• 2001

We describe here the Korea ocean prediction system that closely resembles operational numerical weather prediction systems. This prediction system will be served for real-time forecasts. The core of the system is a three-dimensional primitive equation numerical circulation model, based on ${\sigma}$-coordinate. Remotely sensed multi-channel sea surface temperature (MCSST) is imposed at the surface. Residual subsurface temperature is assimilated through the relationship between vertical temperature structure function and residual of sea surface height (RSSH) using an optimal interpolation scheme. A unified grid system, named as [K-E-Y], that covers the entire seas around Korea is used. We present and compare hindcasting results during 1990-1999 from a model forced by MCSST without incorporating RSSH data assimilation and the one with both MCSST and RSSH assimilated. The data assimilation is applied only in the East Sea, hence the comparison focuses principally on the mesoscale features prevalent in the East Sea. It is shown that the model with the data assimilation exhibits considerable skill in simulating both the permanent and transient mesoscale features in the East Sea.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.1
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• pp.1-15
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• 2015

Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.

• Journal of the korean society of oceanography
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• v.38 no.2
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• pp.52-59
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• 2003

In this paper, a brief overview on data assimilation is provided in the context of oceanographic application. The ocean data assimilation needs to ingest various types of data such as satellites and floats, thus essentially requires dynamically-consistent assimilation methods. For such purpose, sequential and variational approaches are discussed and compared. The major advantage of the Kalman filter (KF) is that it can forecast error covariances at each time step. However, for models with very large dimension of state vector, the KF Is exceedingly expensive and computationally less efficient than four-dimensional variational assimilation (4D-Var). For operational application, simplified 4D-Var schemes as well as ensemble KF may be considered.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 1995.06a
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• pp.375-376
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• 1995

• Atmosphere
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• v.28 no.1
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• pp.99-112
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• 2018

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m ($0.49^{\circ}C$) was much lower than that of HYCOM ($0.73^{\circ}C$). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, $0.61^{\circ}C$; HYCOM, $0.72^{\circ}C$), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.