• 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.

• 대기
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• 제33권4호
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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.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• 한국언론정보학보
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• 제46권
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• pp.323-353
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• 2009

한국 언론의 공정성 논란은 사회정치적 측면에서 커다란 이슈로 자리 잡았다. 기존 연구는 언론의 공정성을 판단하기 위한 선험적 원리와 기준을 제시하고 구성방식과 실천에 관한 매체와 송신자 중심의 연구가 주류를 이루었다. 본 연구는 언론의 공정성을 일반인의 시각에서 접근하기 위한 시도로 언론의 일반 소비자가 자신이 취하는 사회 갈등적 이슈의 기존 태도에 의해서, 그리고 미디어 내용의 편향을 지각하는 방향에 의해서 공정성 판단에 영향을 미칠 것이라는 공정성에 대한 수용자 지각을 실험연구를 통해 고찰하였다. 개인이 가지고 있는 사물이나 사건에 대한 긍정적이거나 부정적인 태도와 정보원에 대한 인식은 대상을 판정하는 데 일종의 편향성을 제공한다는 사회적 판단이론의 이론적 메커니즘을 뉴스의 공정성 판단에 적용하였다. '체벌법제화'와 '전시작전통제권'의 두 가지 이슈를 통하여 일방형, 양방형, 무판단양방형의 세 가지 종류의 메시지를 작성하여 실험한 결과, 개인이 가지고 있는 기존의 태도와 일치하는 메시지를 받았을 경우 일치하지 않는 경우보다 더욱 보도가 공정하다고 느꼈으며, 이러한 결과는 양면적 메시지 조건에서도 입증되었다. 자신의 입장과 동일한 메시지를 더욱 공정하게 느끼는 동화현상과 자신의 태도에 반대되는 메시지 를 더욱 불공하다고 느끼는 대조를 통해 적대적 매체지각이 발생하였다. 균형된 메시지의 경우 중립집단은 공정하다고 느끼는 반면, 강한 태도를 가진 집단은 덜 공정한 것으로 판단하는 대조편향을 발견하였다. 이외에도 기존의 공정성 판단의 제한적 성격과 타당성이 논의되었다.

• 대기
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• 제30권4호
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• 대기
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• 제32권3호
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• pp.191-206
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• 2022

Assimilation trials were performed using the Korea Institute of Atmospheric Prediction Systems (KIAPS) Korea Integrated Model (KIM) semi-operational forecast system to assess the impact of ground-based Global Navigation Satellite System (GNSS) Zenith Total Delay (ZTD) on forecast. To use the optimal observation in data assimilation of KIM forecast system, in this study, the ZTD observation were pre-processed. It involves the bias correction using long term background of KIM, the quality control based on background and the thinning of ZTD data. Also, to give the effect of observation directly to data assimilation, the observation operator which include non-linear model, tangent linear model, adjoint model, and jacobian code was developed and verified. As a result, impact of ZTD observation in both analysis and forecast was neutral or slightly positive on most meteorological variables, but positive on geopotential height. In addition, ZTD observations contributed to the improvement on precipitation of KIM forecast, specially over 5 mm/day precipitation intensity.

• 한국해양학회지:바다
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• 제10권1호
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• pp.69-78
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• 2005

본 연구에서는 전구 해양에서 관측되는 ARGO및 TAO해양 자료를 이용하여 해양의 3차원적인 구조를 분석.동화하고 궁극적으로 해양대순환모형을 위한 초기장을 생산하였다. 초기장의 생산을 위하여 전구 해양대순환 모형인 MOM3.1을 이용하였으며 생산한 배경장에, 계산시간과 계산공간을 절약할 수 있는 공간필터를 사용한 변분법(VAF, variational analysis using filter)을 이용하여 ARGO와 TAO 수온 자료를 동화하였다. 또한 본 연구에서는 자료 동화가 미치는 지속적인 영향을 살펴보고자 실험적분을 수행하였는데, 모형의 초기입력 자료를 자료동화 기법을 적용한 경우와 적용하지 않은 두 가지로 나누어 비교 실험을 수행하였다. 본 연구에서 자료 동화된 분석장은 OISST와의 비교를 통해 적절히 생산되었음을 보여주었다. 관측자료를 동화한 분석장을 초기자료로 한 10개월간의 적분결과를 살펴보면, 자료 동화를 통해 제거된 모형의 계통적 bias가 적분이 진행되는 과정에서 관성 중력파 등의 형태로 소멸되지 않고 지속적으로 관측과 유사하게 유지되었다. 이는 본 연구에서 실행한 자료동화가 모형의 역학적인 균형을 유지하면서 적절히 이루어졌음을 의미하며, 전구 대순환 모형을 이용한 중.장기 대기.해양 예측에 이러한 해양 자료동화가 대단히 유용하다는 것을 의미한다.

• 대기
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• 제29권3호
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• pp.241-255
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• 2019

Bias correction (BC) and quality control (QC) are essential steps for the proper use of satellite observations in data assimilation (DA) system. BC should be calculated over quality controlled observation. And also QC should be performed for bias corrected observation. In the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), we adopted an adaptive BC method that calculates the BC coefficients with background at the analysis time rather than using static BC coefficients. In this study, we have developed an iterative QC-BC method for Advanced Microwave Sounding Unit-A (AMSU-A) to reduce the negative feedback from the interaction between BC and QC. The new iterative QC-BC is evaluated in the KIAPS 3-dimensional variational (3DVAR) DA cycle for January 2016. The iterative QC-BC method for AMSU-A shows globally significant benefits for error reduction of the temperature. The positive impacts for the temperature were predominant at latitudes of $30^{\circ}{\sim}90^{\circ}$ of both hemispheres. Moreover, the background warm bias across the troposphere is decreased. Even though AMSU-A is mainly designed for atmospheric temperature sounding, the improvement of AMSU-A pre-processing module has a positive impact on the wind component over latitudes of $30^{\circ}S$ near upper-troposphere, respectively. Consequently, the 3-day-forecast-accuracy is improved about 1% for temperature and zonal wind in the troposphere.

• 한국환경과학회지
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• 제19권8호
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• pp.983-999
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• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.