• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Journal of Astronomy and Space Sciences
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• 제28권4호
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• pp.267-271
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• 2011

I developed an enhanced correction method for Ricean bias which occurs in linear polarization measurement. Two known methods for Ricean bias correction are reviewed. In low signal-to-noise area, the method based on the mode of the equation gives better representation of the fractional polarization. But a caution should be given that the accurate estimation of noise level, i.e. ${\sigma}$ of the polarized flux, is important. The maximum likelihood method is better choice for high signal-to-noise area. I suggest a hybrid method which uses the mode of the equation at the low signal-to-noise area and takes the maximum likelihood method at the high signal-to-noise area. A modified correction coefficient for the mode solution is proposed. The impact on the depolarization measure analysis is discussed.

• 대기
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• 제24권4호
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• 한국수자원학회논문집
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• 제51권7호
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• pp.575-584
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• 2018

최근 이상기후로 인한 집중호우 발생빈도와 이로 인한 국지적인 홍수 피해가 증가하고 있다. 이러한 점에서 홍수피해 예방측면에서 수치예보 정보 활용이 요구되고 있다. 그러나 수치예보모델은 초기 조건 및 지형적 요인으로 인해 시공간적 편의가 존재하며 실시간 예측정보로 활용하기 전에 모형결과에 대한 편의보정이 요구된다. 본 연구에서는 관측지점 기준으로 편의 보정계수를 산정하는 과정에서 모든 관측소간의 상관성을 거리의 함수로 고려하여 미계측지점의 편의 보정계수를 공간적으로 확장할 수 있는 Bayesian Kriging 기반 MFBC 기법을 개발하였다. 본 연구에서 개발한 방법은 미계측 유역에 대해서도 보정계수를 효과적으로 추정하는 것이 확인되었으며, 비교적 고해상도로 72시간(3일) 정도까지 예측강우 정보를 활용하는 것이 가능할 것으로 판단된다.

• 한국정보통신학회논문지
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• 제14권4호
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• pp.948-958
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• 2010

MRI 시스템이 수집하는 인체신호는 매우 미약하기 때문에 영상화 과정을 거치면서 외부 잡음이나 시스템 불안정성에 의한 영향을 쉽게 받을 수 있다. 따라서 본 논문에서는 저 자장 MRI시스템에서 RF 수신코일의 디자인적 요소에 의해 발생되는 불균일성을 분석하여 영상의 균일도 향상 기법을 제안한다. 본 논문에서는 MRI영상의 신호강도 불균일성을 보정하기 위한 방법 중에서 팬텀 데이터를 이용하여 확장된 크기를 갖는 3차원 bias 볼륨 데이터를 획득하기 위한 방법을 제안함으로써 다양한 크기를 갖는 영상의 보정이 가능하도록 하였다. 제안된 bias 데이터의 최적화 기법을 적용하여 실험을 수행한 결과 단일 bias 데이터의 사용으로 다양한 영상법에 의한 영상을 효과적으로 보정할 수 있음을 확인 하였다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1236-1247
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• 2016

Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.

• 대기
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• 제23권4호
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• pp.453-470
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• 2013

As a part of the KIAPS Observation Processing System (KOPS), we have developed the modules of satellite radiance data pre-processing and quality control, which include observation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extracted using the BUFR (Binary Universal Form for the Representation of meteorological data) decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminated by large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for different surface types since the errors from the skin temperature are caused by inaccurate surface emissivity. Correcting the bias caused by errors in the instruments and radiative transfer model is crucial in radiance data pre-processing. We have developed bias correction modules in two steps based on 30-day innovation statistics (observed radiance minus background; O-B). The scan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictors has been employed to correct the airmass bias.

• Asia Marketing Journal
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• 제26권1호
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• pp.23-30
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• 2024

Firms often engage in manipulating online reviews as a promotional activity to influence consumers' evaluation on their products. With the prevalence of the promotional activities, consumers may notice and discount the reviews generated by the promotional activities. Discounting the firm-generating reviews may cause systematic measurement errors in the valence variable and lead to a negative bias when estimating the effect of consumers' organic reviews on demand. To correct the bias, this study proposes including product-specific bias-correction terms representing the proportion of extreme reviews in analysis. For illustration, the proposed method is applied to a demand model for data of movies released in South Korea. The results confirm a negative bias in the estimate of the valence sensitivity of demand. The negative bias potentially leads to an underestimation of the magnitude of the contagion effect through social interactions, a key component of evaluating the value of a satisfied consumer.

• 한국농공학회논문집
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• 제54권6호
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• pp.133-142
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• 2012

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.