• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.403-412
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• 2018

In this study, a three-dimensional variational(3DVAR) data assimilation system was developed for the operational numerical weather prediction(NWP) system at the Republic of Korea Air Force Weather Group. The Air Force NWP system utilizes the Weather Research and Forecasting(WRF) meso-scale regional model to provide weather information for the military service. Thus, the data assimilation system was developed based on the WRF model. Experiments were conducted to identify the nested model domain to assimilate observations and the period appropriate in estimating the background error covariance(BEC) in 3DVAR. The assimilation of observations in domain 2 is beneficial to improve 24-h forecasts in domain 3. The 24-h forecast performance does not change much depending on the estimation period of the BEC in 3DVAR. The results of this study provide a basis to establish the operational data assimilation system for the Republic of Korea Air Force Weather Group.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.458-467
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• 2019

The purpose of this study is the suggestion of optimized parameters in OI (Optimal Interpolation) by experimental study. The observation of applying optimal interpolation is ADCP (Acoustic Doppler Current Profiler) data at the southwestern sea of Korea. FVCOM (Finite Volume Coastal Ocean Model) is used for the barotropic model. OI is to the estimation of the gain matrix by a minimum value between the background error covariance and the observation error covariance using the least square method. The scaling factor and correlation radius are very important parameters for OI. It is used to calculate the weight between observation data and model data in the model domain. The optimized parameters from the experiments were found by the Taylor diagram. Constantly each observation point requires optimizing each parameter for the best assimilation. Also, a high accuracy of numerical model means background error covariance is low and then it can decrease all of the parameters in OI. In conclusion, it is expected to have prepared the foundation for research for the selection of ocean observation points and the construction of ocean prediction systems in the future.