• 대기
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• 제23권1호
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• pp.33-37
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• 2013

In meteorology, exploitation of Kalman filter as a data assimilation system is virtually impossible due to simultaneous requirements of adjoint model and large computer resource. The other substitute of utilizing ensemble Kalman filter is only affordable by compensating an enormous usage of computing resource. Furthermore, the latter employs ensemble integration sets for evolving the background error covariance matrix by compensating the dynamical feature of the temporal evolution of weather conditions. We propose a new implementation method that works without the adjoint model by utilizing the explicit expression of the background error covariance matrix in backward evolution. It will also break a barrier in the evolution of the covariance matrix. The method may be applied with a slight modification to the real time assimilation or the retrospective analysis.

• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• 한국군사과학기술학회지
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• 제21권3호
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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.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.

• 대기
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• 제32권4호
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• pp.263-276
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• 2022

The observation error of satellite radiation data that assimilated into the Korean Integrated Model (KIM) was diagnosed by applying the Hollingsworth and Lönnberg and Desrozier techniques commonly used. The magnitude and correlation of the observation error, and the degree of contribution for the satellite radiance data were calculated. The observation errors of the similar device, such as Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A shows different characteristics. The model resolution accounts for only 1% of the observation error, and seasonal variation is not significant factor, either. The observation error used in the KIM is amplified by 3-8 times compared to the diagnosed value or standard deviation of first-guess departures. The new inflation value was calculated based on the correlation between channels and the ratio of background error and observation error. As a result of performing the model sensitivity evaluation by applying the newly inflated observation error of ATMS, the error of temperature and water vapor analysis field were decreased. And temperature and water vapor forecast field have been significantly improved, so the accuracy of precipitation prediction has also been increased by 1.7% on average in Asia especially.

• 한국해안·해양공학회논문집
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• 제31권6호
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• pp.458-467
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• 2019

본 연구는 자료동화에 필요한 매개변수의 최적화된 값를 산정하기 위해 서남해안을 포함하는 한반도 중심해역에 해양순환수치모델 FVCOM(Finite Volume Community Ocean Model)을 구축 및 검증하고 이에 연속관측된 수층별 유속자료와 OI(Optimal Interpolation)를 자료동화하였다. 자료동화에는 서남해안에 위치한 4정점에서 ADCP(Acoustic Doppler Current Profiler)을 통해 관측된 수층별 유속자료를 사용하였다. 자료동화에 사용된 배경 모델은 복잡하고 불규칙한 지형적 특성을 가진 서남해안 중심의 한반도 해역을 비구조격자체계의 해양순환수치모델인 FVCOM으로 구성하고 이를 조석검증하였다. 최적내삽법의 Correlation length와 Scale factor는 자료동화 과정에서 관측값의 영향 범위를 결정하고 오차를 보정할 수 있는 매개변수다. 자료동화기법 내 매개변수는 연구 지역에 존재하는 해양학적 특성에 따라 능동적으로 변동되기 때문에 이를 토대로 경험적인 산정 연구가 필요하다. 따라서 서남해안에서 요구되는 각 매개변수들을 Taylor diagram을 활용하여 관측정점별로 분석하고 최적값을 산정하였다. 산정된 최적매개변수는 관측정점마다 요구되는 값이 상이하며 연안에서 외해로 갈수록 증가하는 추세를 보인다. 추가로 조석검증 전과 후에 따른 배경 모델이 갖는 정확성이 자료동화 효과에 미치는 영향을 분석하였다. 조석검증을 통해 정확성이 높아진 배경 모델은 배경오차공분산이 상대적으로 감소됨에 따라서 총 비중 함수가 0에 가까워지고 결과적으로 최적매개변수값이 감소하였다. 이러한 최적매개변수는 광역 모델이 갖고 있는 연안역까지 도달하는 개방경계의 한계점을 완화시켜줄 것으로 기대하며 향후 관측정점별로 요구되는 최적매개변수값을 독립적으로 적용하도록 개선한다면 향상된 해양예측 시스템 개발에 도움이 될 것으로 기대한다.

• 한국통신학회논문지
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• 제34권5C호
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• pp.527-539
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• 2009

본 논문에서는 3GPP LTE (3rd Generation Partnership Project Long Term Evolution) MIMO-OFDMA(multiple-input multiple-output-orthogonal frequency division multiple access) 시스템의 하향 링크 수신기를 위한 asynchronous ICI (Inter-Cell Interference) 완화 기법을 제안한다. Multi-cell 환경을 고려한 celluar OFDMA 시스템에서는 기본적으로 frequency reuse factor가 1로 설정되기 때문에 셀 경계에 위치한 UE (User Equipment)의 경우 ICI 영향을 받게 되며, 특히 각기 다른 셀 반경 및 nodeB 간의 거리 차이 등 현실적인 celluar 환경을 고려 할 경우에는 UE 간 타이밍 오류가 가중되어 수신 신호의 주파수 영역의 직교성이 파괴될 가능성이 있다. 따라서 이러한 인접 셀 간섭을 제거 및 완화하기 위하여 수신 OFDM 심볼에 대한 SCM (Spatial Covariance Matrix) 추정이 필요하다. 일반적으로 SCM 추정은 training symbol을 이용함을 가정하지만, 긴 시간 동안 간섭의 통계적 특성을 측정하는 것은 어려울 뿐만 아니라 training symbol이 고려되지 않는 LTE와 같은 MIMO-OFDMA 시스템에는 적합하지 않다. 또한 추정의 정확성을 높이기 위하여 noise reduction 방식이 적용된 추정 기법이 제시되고 있으나, 기존 time-domain low-pass type weighting 방식은 spectral leakage에 의한 추정 에러를 유발하는 단점이 있다. 따라서, 본 논문에서는 noise reduction 효과를 얻으면서 spectral leakage에 의한 SCM 추정 오류를 최소화할 수 있으며, 주파수 영역에의 moving average filter로 구현 가능한 time-domain sinc-type weighting 방식의 SCM 추정 기법을 제안하였으며, 다양한 환경에서의 컴퓨터 모의 실험을 통하여 제안된 방식이 기존의 방식보다 약 3dB 의 SIR (Signal to Interference Ratio) 이득을 보임을 입증하였다.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.23-28
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• 2010

According to increasing projection order, the AP algorithm bas noise amplification problem in large background noise. This phenomenon degrades the performances of the AP algorithm. In this paper, we analyze convergence characteristic of the AP algorithm and then suggest a noise robust modified AP algorithm for reducing this problem. The proposed algorithm normalizes the update equation to reduce noise amplification of AP algorithm, by adding the multiplication of error power and projection order to auto-covariance matrix of input signal. By computer simulation, we show the improved performance than conventional AP algorithm.

• 대기
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• 제17권3호
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• pp.217-230
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• 2007

Using the Lorenz-95 simple model, which can simulate many atmospheric characteristics, we compare the performance of ensemble strategies such as bred vectors, the bred vectors rotated (to be orthogonal to each bred member), and the Ensemble Transform Kalman Filter (ETKF). The performance metrics used are the RMSE of ensemble means, the ratio of RMS error of ensemble mean to the spread of ensemble, rank histograms to see if the ensemble member can well represent the true probability density function (pdf), and the distribution of eigen-values of the forecast ensemble, which can provide useful information on the independence of each member. In the meantime, the orthogonal bred vectors can achieve the considerable progress comparing the bred vectors in all aspects of RMSE, spread, and independence of members. When we rotate the bred vectors for orthogonalization, the improvement rate for the spread of ensemble is almost as double as that for RMS error of ensemble mean compared to the non-rotated bred vectors on a simple model. It appears that the result is consistent with the tentative test on the operational model in KMA. In conclusion, ETKF is superior to the other two methods in all terms of the assesment ways we used when it comes to ensemble prediction. But we cannot decide which perturbation strategy is better in aspect of the structure of the background error covariance. It appears that further studies on the best perturbation way for hybrid variational data assimilation to consider an error-of-the-day(EOTD) should be needed.