• Nuclear Engineering and Technology
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• 제46권5호
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• 한국대기환경학회지
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• 제20권5호
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• pp.633-645
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• 2004

A critical component of air pollution modeling is the representation of meteorological fields within a model domain, since an accurate air quality simulation requires an accurate portrayal of the three-dimensional wind fields. The present study investigated data assimilation using surface observational data in the complex coastal regions to simulate an accurate meteorological fields. Surface observational data were categorized into three groups(Near coastal region, Far coastal regiln 1, Far costal region 2) by the locations where the data are. Experiments were designed and MM5 was used in each case of regions. Case 1 is an experiment without data assimilation, Case N is executed with data assimilation using observational data by meteorological stations and AWS data located in the near coastal region, within 1 km. Case F1 is also an experiment with data assimilation using observational data by meteorological stations and AWS data located in the far coastal regiln 1, more than 1km and less than 5km from the coastal lines. Case F2 is appled to data assimilation using observational data by meteorological stations and AWS data located in the far coastal region 2, beyond 5km from the coastal lines. The result of this study indicated that data assimilation using data in the far coastal region 1 and 2 provided an attractive method for generating accurate meteorological fields, especially in the complex coastal regions.

• 대기
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• 제28권2호
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• pp.113-121
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• 2018

In order to monitor greenhouse gases including $CO_2$, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface $CO_2$ flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface $CO_2$ flux, additional observations would be required. In this study, a system that assimilates aircraft $CO_2$ measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft $CO_2$ measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft $CO_2$ measurement data. This system could also be used to assimilate other aircraft $CO_2$ measurement data in CarbonTracker.

• 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.

• 대기
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• 제23권2호
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• pp.143-160
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• 2013

The effect of 3DVAR (Three Dimension Variational data Assimilation) was examined by comparing observation and the simulations of CNTL (to which data assimilation was not applied) and ALL (to which data assimilation was applied using ground observation data and radar data) for the case of a heavy snowfall event (case A) of 11-12 February 2011 in the Yeongdong region. In case A, heavy snow intensively came in the Yeongdong coastal region rather than Daegwallyeong, in particular, around the Gangneung and Donghae regions with total precipitation in Bukgangneung at approximately 91 mm according to the AWS observation. It can be seen that compared to CNTL, ALL simulated larger precipitation along the Yeongdong coastline extending from Sokcho to Donghae while simulating smaller precipitation for inland areas including Daegwallyeong. On comparison of the total accumulated precipitations from simulations of CNTL and ALL, and the observed total accumulated precipitation, the positive effect of the assimilation of ground observation data and radar data could be identified in Bukgangneung and Donghae, on the other hand, the negative effect of the assimilation could be identified in the Daegwallyeong and Sokcho regions. In order to examine the average accuracy of precipitation prediction by CNTL and ALL for the entire Gangwon region including the major points mentioned earlier, the three hour accumulated precipitation from simulations of CNTL and ALL were divided into 5, 10, 15, 20, 25 and 30 mm/3hr and threat Scores were calculated by forecasting time. ALL showed relatively higher TSs than CNTL for all threshold values although there were some differences. That is, when considered generally based on the Gangwon region, the accuracy of precipitation prediction from ALL was improved somewhat compared to that from CNTL.

• 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.

• 한국지구과학회지
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• 제33권4호
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• pp.309-319
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• 2012

연안 해상 바람 자원 평가에 적용되는 해상풍 위성자료 동화특성을 평가하기 위하여 수치실험을 실시하였다. 사용된 위성자료는 미항공우주국의 QuikSCAT과 유럽우주국의 ASCAT이다. 해상풍 위성자료 동화과정은 연안지역 바람 자원 평가의 정확성을 향상시키는 주요한 요소의 하나이다. QuikSCAT의 관측 가능한 빔폭이 상대적으로 넓기 때문에 QuikSCAT 해상풍 자료를 동화하여 제시된 연안 바람장이 ASCAT를 사용한 바람장보다 약간 높은 정확도를 제시한다. 그러나 센서의 직하 부근의 바람장은 상대적으로 ASCAT의 예측 정확도가 높게 나타난다. 이러한 해상풍 위성자료의 동화효과는 6시간 정도 지속되기 때문에 정확한 연안지역 바람장을 평가하기 위해서는 센서의 공간해상도뿐 아니라 시간해상도가 높은 해상풍 위성자료 동화 과정이 필요하다.

• 한국대기환경학회지
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• 제23권5호
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• pp.585-595
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• 2007

Representative impacts and effectiveness of surface meteorological observation data assimilation were examined in order to use wind resources estimation around southern coastal area of the Korean Peninsula. The data used in study are observational wind and temperature data at 5 and 41 sites of Regional Meteorological Offices and Automatical Weather Systems, respectively. Observation wind speed data tends to show small effective radius with limited area. Especially assimilation impacts of data observed at peninsula type sites like Yeosu play only around the inside of the peninsula. This limited effective radius for wind speed is caused by the strong correlation between topography and wind speed. And the efficient radius for surface air temperature is larger than that of wind. Data assimilation for observational air temperature is useful to increase the accuracy of wind energy estimation. However assimilation of wind data requires special care in its application due to high sensitivity of topographical complexity.

• 한국환경과학회지
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• 제15권1호
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• pp.9-19
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• 2006

The purpose of this research is development of radar data assimilation observed at Jindo S-band radar The accurate observational data assimilation system is one of the important factors to meteorological numerical prediction of the region scale. Diagnostic analysis system LAPS(Local Analysis and Prediction System) developed by US FSL(Forecast Systems Laboratory) is adopted assimilation system of the Honam district forecasting system. The LAPS system was adjusted in calculation environment in the Honam district. And the improvement in the predictability by the application of the LAPS system was confirmed by the experiment applied to Honam district local severe rain case of generating 22 July 2003. The results are as follows: 1) Precipitation amounts of Gwangju is strong associated with the strong in lower level from analysis of aerological data. This indicated the circulation field especially, 850hPa layer, acts important role to precipitation in Homan area. 2) Wind in coastal area tends to be stronger than inland area and radar data show the strong wind in conversions zone around front. 3) Radar data assimilation make the precipitation area be extended and maximum amount of precipitation be smaller. 4) In respect to contribution rate of different height wind field on precipitation variation, radar data assimilation of upper level is smaller than that of lower level.

• 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.