• Journal of the Korean earth science society
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• v.39 no.5
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• pp.473-482
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• 2018

During the period of 2002 to 2017, the Gravity Recovery And Climate Experiment (GRACE) had observed time-varying gravity changes with unprecedented accuracy. The GRACE science data centers provide the monthly gravity solutions after removing the sub-monthly mass fluctuation using geophysical models. However, model misfit makes the solutions to be contaminated by aliasing errors, which exhibits peculiar north-south stripes. Two conventional filters are used to reduce the errors, but signals with similar spatial patterns to the errors are also removed during the filtering procedure. This would be particularly problematic for estimating the ice mass changes in Western Antarctic Ice Sheet (WAIS) and Antarctic Peninsula (AP) due to their similar spatial pattern to the elongated north-south direction. In this study, we introduce an alternative filter to remove aliasing errors using the Empirical Orthogonal Functions (EOF) analysis. EOF can decompose data into different modes, and thus is useful to separate signals from noise. Therefore, the aliasing errors are effectively suppressed through EOF method. In particular, the month-to-month mass changes in WAIS and AP, which have been significantly contaminated by aliasing errors, can be recovered using EOF method.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1507-1515
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• 2021

If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.244-252
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• 2014

EOF (Empirical Orthogonal Function) analysis is applied to investigate the sand movement in Hujeong Beach. For the analysis, the profile data which were observed five times from June 2009 to May 2010 along the 13 baselines were used. To secure the temporal and physical consistency among the 13 profile data, the 13 profile data were combined into one data and using this data the EOF analysis was performed. According to the analysis, the first EOF is related with the mean topography and the second EOF represents the natural variation of sediment migration and the third EOF is related with the along-shore sediment transport arising from storm. The remaining EOFs show no special relation with wave conditions. In conclusion the main factors which are having great effects on Hujeong Beach's sand movement are analyzed as natural variation and along-shore sediment transport owing the wave conditions.

• Ocean and Polar Research
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• v.30 no.3
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• pp.313-324
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• 2008

In order to understand the variation of ENSO-related oceanic environments in the tropical and North Pacific Ocean, spatio-temporal variations of sea surface temperature anomaly (SSTA) and sea surface height anomaly (SSHA) are analyzed from distributions of complex empirical orthogonal functions (CEOF). Correlations among warm pool variation, southern oscillation index, and ocean surface currents were also examined with respect to interannual variability of the warm pool in western tropical Pacific. Spatio-temporal distributions of the first CEOF modes for SSTA and SSHA indicate that their variabilities are associated with ENSO events, which have a variance over 30% in the North Pacific. The primary reasons for their variabilities are different; SST is predominantly influenced by the change of barrier layer thickness, while SSH fluctuates with the same phase as propagation of an ENSO episode in the zonal direction. Horizontal boundary of warm pool area, which normally centered around $149^{\circ}E$ in the tropics, seemed to be expanded to the middle and eastern tropical regions by strong zonal currents through the mature phase of an ENSO episode.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.282-282
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• 2016

지표수문해석모형이란 전 지구를 대상으로 수문해석 및 예측이 가능한 분포형 수문모형이다. 본 연구에서는 CSEOF(Cyclostationary Empirical Orthogonal Functions) 분석 방법을 이용하여 지표수문해석 모형 중 하나인 VIC(Variable Infiltration Capacity)모형의 토양수분 모의 성능을 평가해보고자 한다. 이를 위하여 먼저 남한에 대한 VIC 모형으로 모의한 토양수분 예측 결과와 관측자료를 수집하였다. 모의 성능 평가 기간은 1976년부터 2006년까지이다. 이후 본 연구에서는 수집된 VIC 모형의 예측 결과와 관측 자료에 대한 CSEOF 분석을 수행하여 각 자료의 월별 주된 변동 특성을 추출하였다. VIC 모형의 예측 결과와 관측자료의 상관관계는 CSEOF 분석 결과에 대한 Pattern Correlation으로 정량화되었다. 이와 더불어 본 연구에서는 모형의 모의 성능 평가에 주로 사용되는 NRMSE(Nomalized Root Mean Square Error)를 산정하여 예측 결과의 오차를 평가하였다. Pattern Correlation과 NRMSE를 모두 고려하여 VIC 모형의 성능을 평가해본 결과, 건기에 해당하는 기간과 우기에 해당하는 기간의 모의 성능이 다르게 나타났다. 본 연구의 결과는 추후에 지표수문해석 모형의 예측 결과를 이용하는 기후변화 관련 연구에 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.867-872
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• 2005

This study introduces a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows its potential for investigating agricultural drought characteristics. The method is applied to Gyeonggi as a case study. The proposed procedure includes Standard Precipitation Index(SPI) time series, which are linearly transformed by the Empirical Orthogonal Functions(EOF) method, These EOFs are extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole Gyeonggi area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• Wind and Structures
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• v.38 no.4
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• pp.277-293
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• 2024

The recorded thunderstorm winds at a point contain tri-directional components. The probabilistic characteristics of such recorded winds in terms of instantaneous mean wind speed and direction, and the probability distribution and the time-frequency dependent crossed and non-crossed power spectral density functions for the high-frequency fluctuating wind components are unclear. In the present study, we analyze the recorded tri-directional thunderstorm wind components by separating the recorded winds in terms of low-frequency time-varying mean wind speed and high-frequency fluctuating wind components in the alongwind direction and two orthogonal crosswind directions. We determine the time-varying mean wind speed and direction defined by azimuth and elevation angles, and analyze the spectra of high-frequency wind components in three orthogonal directions using continuous wavelet transforms. Additionally, we evaluate the coherence between each pair of fluctuating winds. Based on the analysis results, we develop empirical spectral models and lagged coherence models for the tri-directional fluctuating wind components, and we indicate that the fluctuating wind components can be treated as Gaussian. We show how they can be used to generate time histories of the tri-directional thunderstorm winds.

• Earthquakes and Structures
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• v.2 no.4
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• pp.323-336
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• 2011

The problem of reconstruction of complete building response from a limited number of response measurements is considered. The response at the intermediate degrees of freedom is reconstructed by using piecewise cubic Hermite polynomial interpolation in time domain. The piecewise cubic Hermite polynomial interpolation is preferred over the spline interpolation due to its trend preserving character. It has been shown that factorization of response data in variable separable form via singular value decomposition can be used to derive the complete set of normal modes of the structural system. The time domain principal components can be used to derive empirical transfer functions from which the natural frequencies of the structural system can be identified by peak-picking technique. A reduced-rank approximation for the system flexibility matrix can be readily constructed from the identified mass-orthonormal mode shapes and natural frequencies.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.