• Journal of Korean Society for Atmospheric Environment
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• 제19권E2호
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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.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.363-370
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• 2017

Data assimilation is an initializing method for air quality forecasting such as PM10. It is very important to enhance the forecasting accuracy. Optimal interpolation is one of the data assimilation techniques. It is very effective and widely used in air quality forecasting fields. The technique, however, requires too much memory space and long execution time. It makes the PM10 air quality forecasting difficult in real time. We propose a fast optimal interpolation data assimilation method for PM10 air quality forecasting using a new kernel tridiagonal sparse matrix and CUDA massively parallel processing architecture. Experimental results show the proposed method is 5~56 times faster than conventional ones.

• 한국환경과학회지
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• 제15권8호
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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.

• 한국인터넷방송통신학회논문지
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• 제17권2호
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• pp.271-277
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• 2017

현대에 들어서면서 대기오염 물질이 심각하게 국민의 건강을 위협하는 단계에 이르렀기 때문에 이에 대한 예보의 중요성은 점점 높아지고 있다. 대기질을 예보하는데 있어서 예보 모델에 입력되는 초기장은 예보의 정확성에 영향을 미치는 요소이기 때문에 신뢰도 높은 초기장을 생성하는 것이 매우 중요하며, 이때 필요한 기법 중 하나가 자료동화이다. 자료동화는 대상 지역이 넓어지고, 관측소의 수가 증가될수록 더 많은 연산이 필요하기 때문에 그 수행시간이 길어진다. 때문에 예보 규모가 커질수록 기존의 순차처리 방식으로는 빠른 처리속도를 요구하는 현업에 적용하기 어렵다. 이에 본 논문에서는 자료동화 기법 중의 하나인 크레스만 방법을 CUDA를 이용하여 실시간으로 처리할 수 있는 방법을 제안하였다. 그 결과, 제안한 CUDA를 이용한 병렬처리 방법이 최소 35배 이상 속도가 향상되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.152-152
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• 2018

Soil moisture is a important factor in hydrologic analysis. So, if we have spatially distributed soil moisture data, it can help to study much research in a various field. Recently, there are a lot of satellite derived soil moisture data, and it can be served through web freely. Especially, NASA (National Aeronautics and Space Administration) launched the Soil Moisture Aperture Passive (SMAP) satellite for mapping global soil moisture on 31 January 2015. SMAP data have many advantages for study, for example, SMAP data has higher spatial resolution than other satellited derived data. However, becuase many satellited derived soil moisture data have a limitation to data accuracy, if we have ancillary materials for improving data accuracy, it can be used. So, in this study, after applying the alogorithm, which is data assimilation methods, applicability of satellite derived soil moisture data was analyzed. Among the various data assimilation methods, in this study, Model Output Statistics (MOS) technique was used for improving satellite derived soil moisture data. Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast by relating model outputs to observational or additional model data.

• Ocean and Polar Research
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• 제42권3호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.39-39
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• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.221-226
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• 1999

A data assimilation system for a 1-dimensional mixed layer model has been constructed using the adjoint method. The classical adjoint method does not work well for the mixed layer variabilities due to the occurrence of spikes in the gradient of the cost function. To solve this problem, the two techniques of scaling the cost function and optimization in the frequency space are used. As a result, the heat flux can be reliably estimated with an accuracy of 8Wm$^{-2}$ rms error in the identical twin experiments. We then applied this system to the tropical Pacific TOGA-TAO buoy data. The air-sea heat flux as well as the mixed layer variability were estimated in close approximation to the buoy data, particularly on time scales longer than the seasonal one.

• 대기
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• 제14권4호
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• pp.19-23
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• 2004

Progresses in numerical prediction of weather and climate have been in parallel with those of computing resources, especially the development of supercomputers. Advanced techniques in numerical modeling, computational schemes, and data assimilation cloud not have been practically achieved without the aid of supercomputers. With such techniques and computing powers, the accuracy of numerical forecasts has been tremendously improved. Supercomputers are also indispensible in constructing and executing the synthetic Earth system models. In this study, a brief overview on numerical weather / climate prediction, Earth system modeling, and the values of supercomputing is provided.

• 한국수자원학회논문집
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• 제46권7호
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• pp.755-765
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• 2013

본 연구의 목적은 앙상블 칼만필터링 기법과 연속형 강우-유출모형을 연계한 SURF 모형과Auto ROM을 결합한 실시간 댐 수문량 예측모형(DHVPM)을 개발하고 그 적용성을 평가하는데 있다. 대상유역은 충주댐 상류유역을 선정하였으며 2006~2009년 동안 연최대 유입량이 발생한 4개 사례를 선정하였다. 관측유량 자료동화 적용에 따른 선행시간 1시간 유입량에 대한 첨두유량 상대오차, 평균제곱근오차, 모형효율성계수를 산정한 결과, 2007년 첨두유량 상대오차 결과를 제외한 모든 사례에서 자료동화기법을 적용한 결과가 우수한 것으로 나타났다. 현시점으로 가정한 가상시점에서 예측 선행시간 10시간에 대해 유입량을 예측한 결과에서, 유역평균 강우량의 오차가 큰 경우에 대해 자료동화기법을 적용함으로써 예측 유입량의 오차가 줄어드는 것을 확인하였다. 이상의 결과로부터 실시간 예측유입량의 정확도를 향상시키기 위해서는 관측유입량의 실시간 활용이 가능한 환경에서 자료동화기법을 연계한 유입량 예측모형을 이용하는 것이 바람직할 것으로 판단된다.