• Journal of Korean Society for Atmospheric Environment
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• v.19 no.E2
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• pp.75-81
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• 2003

Variational data assimilation, which is recently introduced to the air quality modeling, is a promising tool for obtaining optimal estimates of initial conditions and other important parameters such as emission and deposition rates. In this paper. two advanced techniques for variational data assimilation, based on the adjoint and quasi-inverse methods, are tested for a simple air quality problem. The four-dimensional variational assimilation (4D-Var) requires to run an adjoint model to provide the gradient information in an iterative minimization process, whereas the inverse 3D-Var (I3D-Var) seeks for optimal initial conditions directly by running a quasi -inverse model. For a process with small dissipation, I3D-Vu outperforms 4D-Var in both computing time and accuracy. Hybrid application which combines I3D-Var and standard 4D-Var is also suggested for efficient data assimilation in air quality problems.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.4
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• pp.264-274
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• 2009

Concentration of Air Quality Models (CMAQ) has a deep connection with emissions and wind fields. In particular the wind field is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. The purpose of this study is to examine the impact of interpolation on Air quality model. This study was designed to evaluate enhancement of MM5 and CMAQ predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station and the MODerate resolution Imaging Spectroradiometer (MODIS). The alternative meteorological fields predicted with and without MODIS data were used to simulate spatial and temporal variations of ozone in combined with CMAQ on June 2006. The result of this study indicated that data assimilation using MODIS data provided an attractive method for generating realistic meteorological fields and dispersion fields of ozone in the Korea peninsular, because MODIS data in 10 km domain are grid horizontally and vertically. In order to ensure the success of Air quality model, it is necessary to FDDA using MODIS data.

• Journal of Environmental Impact Assessment
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• v.11 no.2
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• pp.109-116
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• 2002

The diagnostic and prognostic methods for generating 3 dimensional wind field were comparatively analyzed and 4 dimensional data assimilation (FDDA) technique by incorporating Automatic Weather System (AWS) into the prognostic methods was discussed for the urban scale air quality model. The A WS covered the urban scale grid distance of 10.6 km and 4.3 km in South Korea and Kyong-in region, respectively. This is representing that AWS for FDDA could be fairly well accommodated in prognostic model with the meso${\gamma}$~ microa scale (~5 km), indicating that the 3 dimensional wind field by FDDA technique could be a useful interpretative tool in urban area for the atmospheric environmental impact assessment.

• Atmosphere
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• v.12 no.2
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• pp.10-12
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• 2002

• Journal of Environmental Impact Assessment
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• v.11 no.2
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• pp.79-91
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• 2002

Four dimensional data assimilation (FDDA) technique was considered for 3 dimensional wind field in coastal area and a set of 3 numerical experiments including control experiments has been tested for the case of the synoptic weather pattern of the weak northerly geostrophic wind with the cloud amount of less than 5/10 in autumn. A three dimensional land and sea breeze model with the sea surface temperature (SST) of 290K was performed without nudging the observed wind field and surface temperature of AWS (Automatic Weather System) for the control experiment. The results of the control experiment showed that the horizontal temperature gradient across the coastline was weakly simulated so that the strength of the sea breeze in the model was much weaker than that of observed one. The experiment with only observed horizontal wind field showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated. However, the horizontal wind speed and vertical motion in the convergence zone were weakly simulated. The experiment with nudgings of both the surface temperature and wind speed showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated even though the ending time of the sea breeze was delayed due to oversimulated temperature gradient along the shoreline.

• Journal of Environmental Science International
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• v.15 no.8
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• pp.759-774
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• 2006

We employed two data assimilation techniques including MM5 Four Dimensional Data Asssimilation (FDDA) and Local Analysis and Prediction System (LAPS) to find out the effects of the changed inetial conditions on the wind fields simulation according to the objective analysis methods. We designed 5 different modeling cases. EXP B used no data assimilation system. Both EXP Fl using surface observations and EXP F2 with surface and upper-air observations employed MM5 FDDA. EXP Ll using surface observations and EXP L2 with surface and upper-air observations used LAPS. As results of, simulated wind fields using MM5 FDDA showed locally characterized wind features due to objective analysis techniques in FDDA which is forcefully interpolating simulated results into observations. EXP Fl represented a large difference in comparison of wind speed with EXP B. In case of LAPS, simulated horizontal distribution of wind fields showed a good agreement with the patterns of initial condition and EXP Ll showed comparably lesser effects of data assimilation of surface observations than EXP Fl. When upper-air observations are applied to the simulations, while MM5 FDDA could hardly have important effects on the wind fields simulation and showed little differences with simulations with merely surface observations (EXP Fl), LAPS played a key role in simulating wind fields accurately and it could contribute to alleviate the over-estimated winds in EXP Ll simulations.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.195-196
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• 2004

• Journal of the Korean earth science society
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• v.36 no.5
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• pp.460-475
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• 2015

A number of heavy rainfall events on the Korean Peninsula are indirectly influenced by tropical cyclones (TCs) when they are located in southeastern China. In this study, a heavy rainfall case in the middle Korean region is selected to examine the influence of typhoon simulation performance on predictability of remote rainfall over Korea as well as direct rainfall over Taiwan. Four different numerical experiments are conducted using Weather Research and Forecasting (WRF) model, toggling on and off two different improvements on typhoon in the model initial condition (IC), which are TC bogussing initialization and dropwindsonde observation data assimilation (DA). The Geophysical Fluid Dynamics Laboratory TC initialization algorithm is implemented to generate the bogused vortex instead of the initial typhoon, while the airborne observation obtained from dropwindsonde is applied by WRF Three-dimensional variational data assimilation. Results show that use of both TC initialization and DA improves predictability of TC track as well as rainfall over Korea and Taiwan. Without any of IC improvement usage, the intensity of TC is underestimated during the simulation. Using TC initialization alone improves simulation of direct rainfall but not of indirect rainfall, while using DA alone has a negative impact on the TC track forecast. This study confirms that the well-suited TC simulation over southeastern China improves remote rainfall predictability over Korea as well as TC direct rainfall over Taiwan.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.