• 대한원격탐사학회지
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• 제10권1호
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• pp.1-14
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• 1994

The purpose of this study is to find the relationship between GMS image data and hourly observed rainfalls data. Heavy rainfall cases over South Korea on 10th September 1990 and on 29th July 1993 were selected for studying of the relationship between the image data and reinfalls. First, image data were converted to TBB(Temperature of Black Body) and albedo and then these values were extracted for the pixels closest to the surface observation station to correlate with the rainfall data. Horizontal distribution of TBB and albedo tells roughly rainfall regions. The correlation between rainfall and TBB is found to be very low in quantitative analysis. The weak relationship between the brighter albedo and the higher rainfall probability is observed. This study suggests that the TBB values are useful in classifying rain areas and for heavy rainfalls the albedo values are more useful than the TBB. Low linear correlation between the fields may be attributed to the neglect of cloud types in this study.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Journal of Multimedia Information System
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• 제5권4호
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• pp.245-256
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• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.1874-1889
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• 2017

The paper aims to analyze structure of I/Q data observed from radar and reliably estimate rainfall through quality control of I/Q data that can quantify uncertainty of I/Q data occurring due to resultant errors. Radar rainfall data have strong uncertainty due to various factors influencing quality. In order to reduce this uncertainty, previously enumerated errors in quality need to be eliminated. However, errors cannot be completely eliminated in some cases as seen in random errors, so uncertainty is necessarily involved in radar rainfall data. Multi-Lag Method, one of I/Q data quality control methods, was applied to estimate precipitation with regard to I/Q data of rainfall radar in Mt. Sobaek.

• 한국수자원학회논문집
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• 제45권7호
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• pp.729-741
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• 2012

수문학적 해석에 필요한 기본 자료인 강우 자료의 취득 방법 중 지상관측소는 강우량을 직접 관측하기 때문에 실측 강우량 정보를 얻는 장점이 있으나, 현업에서 필요한 면적강우량을 얻기 위해서는 다수의 관측소를 설치해야 하는 단점이 있다. 한편, 강우레이더는 넓은 범위의 강우량을 실시간으로 취득할 수 있으며, 특히 공간적으로 한정되어 발생하는 국지성 돌발홍수의 원인이 되는 단기 집중 호우를 추적하기 용이한 장점이 있다. 따라서 본 연구에서는 기상청에서 편차 보정과 품질 관리가 이루어지고 있어 상당한 신뢰 수준을 확보하고 있고, 현장에서 쉽게 제공 받을 수 있는 강우레이더 영상인 CAPPI (Constant Altitude Plan Position Indicator) 합성영상으로부터 유역평균 강우를 추출하는 방법으로서 기존의 연산방법인 레스터 방식에서 벗어나 소유역에도 적용될 수 있는 벡터 방식의 영상 추출 기법 CIVCOM을 제시하고 추출된 자료의 타당성을 비교 검토하였다.

• 한국수자원학회논문집
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• 제41권11호
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• pp.1153-1162
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• 2008

수문학적 해석에 있어 레이더 강우의 활용은 원시자료를 획득하기가 어려울 뿐만 아니라 이를 처리하여 적용하는 과정이 간단하지 않기 때문에 대부분의 연구와 실무적용에 있어 많은 어려움이 있다. 본 연구에서는 레이더 영상자료를 실용적으로 활용하기 위한 방안으로 기상청에서 제공하는 레이더 합성 CAPPI(Constant Altitude Plan Position Indicator) 이미지 자료를 디지털 강우자료로 변환할 수 있는 '레이더 영상 디지털 변환법(RAIDOM, RAdar Image DigitalizatiOn Method)'을 연구 개발하였다. 2006년 7월에 발생한 국지성 집중호우와 한강유역 중상류 지역에 걸쳐 큰 홍수량을 발생시킨 2개의 호우사상에 대하여 레이더 강우자료를 분포형 모형에 적용하여 활용성을 검토하였다. 모의된 홍수수문곡선은 실측치와 잘 일치하였고 RAIDOM과 이를 적용한 분포형 모형이 홍수예보를 위하여 활용될 수 있음을 보여주었다. 이러한 연구를 통하여 수문해석에 있어 레이더 강우에 대한 활용성을 넓히는데 기여할 것으로 기대된다.

• 한국대기환경학회지
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• 제34권5호
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• 한국수자원학회논문집
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• 제36권6호
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• pp.999-1012
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• 2003

본 논문의 목적은 위성영상 피복분류항목에 대해 통계적 접근법으로부터 산정된 유출곡선지수(CN)를 이용하여 계산 유효우량과 관측 유효우량을 비교함으로써 그 적용성을 검토하는데 있다. 검정을 위한 적용대상지역은 경안천 수위지점 상류유역, 백옥포 수위지점 상류유역, 괴산댐 수위지점 상류유역으로 선정하였으며 각 지역별로 4개의 홍수사상을 선정하였다. CN 값 산정을 위해 2000년에 획득된 Landsat-7 ETM 영상을 이용하여 토지이용도를 구축하였으며 개략토양도로부터 수문학적 토양군을 구축하였다. 유역평균 CN 값은 대상지역별로 71, 63, 66으로 나타났으며 계산 유효우량과 관측 유효우량의 비교결과 약30% 이내의 일정수준의 오차를 가지는 것으로 나타났다. 따라서 위성영상을 이용하여 미계측 유역의 CN 값 산정시 본 논문 I에서 개발한 CN 값을 적용함으로서 기존의 방법에 비해 보다 객관적인 값을 제시해줄 것으로 판단된다.

• 센서학회지
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• 제23권4호
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• pp.259-265
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• 2014

When raining or snowing, windshield wiper system is very important for safety of driver. However, manual wiper system frequently needed to be controlled for sufficient visibility and it was very uncomfortable. So, rain sensor which controls automatically was developed. This rain sensor technology uses optical sensing technique sensed the rainfall by receiving reflected light of rain dropped on the windshield. The technology used optical sensor was simple and easy to implement as a rain sensing system in the car. However, it is sometime shown low accuracy to measure rainfall on the windshield when affected by ambient lights from surroundings. It is also given inconvenience to the driver to control the car. To solving these problems, we propose a rain sensing system using image sensor and the fuzzy wiper control algorithm.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.352-362
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• 2017

This paper proposes an algorithm for automatically converting and displaying rainfall radar data on a 3D GIS platform. The weather information displayed like rainfall radar data is updated frequently and large-scale. Thus, in order to efficiently display the data, an algorithm to convert and output the data automatically, rather than manually, is required. In addition, since rainfall data is extracted from the space, the use of the display image fused with the 3D GIS data representing the space enhances the visibility of the user. To meet these requirements, this study developed the Auto Data Converter application that analyzes the raw data of the rainfall radar and convert them into a universal format. In addition, Unity 3D, which has good development accessibility, was used for dynamic 3D implementation of the converted rainfall radar data. The software applications developed in this study could automatically convert a large volume of rainfall data into a universal format in a short time and perform 3D modeling effectively according to the data conversion on the 3D platform. Furthermore, the rainfall radar data could be merged with other GIS data for effective visualization.