• 한국지구과학회지
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• 제31권5호
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1430-1437
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• 2019

In this paper, we proposed a methodology for estimating rainfall intensity using a W-band FMCW automotive radar signal which is the core technology of autonomous driving car. By comparing and analyzing the results of rainfall and non-rainfall observation, we found that the reflection intensity of the automotive radar is changed with rainfall intensity. We could confirm the possibility of deriving the quantitative precipitation estimation using the methodology derived from this result. In addition it can be possible to develop a new paradigm of precipitation observation technique by observing various events together with the weather radar and the ground rainfall observation equipment.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.318-322
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• 2004

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. For the estimation of the regional design rain(all, classification of the climatologically and geographically homogeneous regions should be preceded preferentially The optimal regionalization of the precipitation data were classified by the above mentioned conditions for all over the regions except Jeju and Ulleung islands in Korea. Relative root mean square error(RRMSE), relative bias(RBIAS) and relative reduction(RR) in RRMSE for the design rainfall were computed and compared between the regional and at-site frequency analysis. Consequently, optimal design rainfalls following the classified regions and consecutive durations were derived by the regional frequency analysis using GEV distribution which was identified to be more optimal one than the other applied distributions.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

• Applied Biological Chemistry
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• 제1권
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• pp.26-33
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• 1960

Organic mercurial fungicides, for seed treatments and dust formulations, has been increasingly used by farmers. Evaluation of the purity of organic mercurial fungicides has been performed by precipitation method at this laboratory. There are several methods for the an alyses of organic mercuric formulation, among which are (1) Precipitation met hod, (2) Volatilization method, (3) Volumetric method, and (4) Dithizon method. These methods, however, show some deffects in specificity (differentiation of organic form) and quantitativity. Polarography applied for the estimation of phenyl mercuric acetate was found to be simple, rapid and accurate. Tile fundamental method of polarography arid accuracy of analysis are discussed statistically and a satisfactory results was obtained.

• 한국수자원학회논문집
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• 제55권6호
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• pp.447-459
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• 2022

본 연구에서는 위성 기반 재분석 강수 자료인 PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record)을 이용하여 한반도에 대한 격자형 확률강수량을 산정하였다. 고려된 기간은 1983년부터 2020년까지 총 38개년이다. 사용된 자료의 공간해상도는 0.04°이며, 시간해상도는 3시간이다. 확률분포로는 빈도해석을 위해 일반적으로 사용되고 있는 Gumbel 분포를 사용하였으며, 매개변수 추정을 위해 확률가중모멘트법을 적용하였다. 지속기간은 3시간부터 144시간 까지, 재현기간은 2년부터 500년까지가 고려되었다. 이러한 방식으로 산정된 결과를 지상우량계인 ASOS (Automated Synoptic Observing System) 기상관측소의 강수 자료를 활용하여 산정된 확률강수량과 비교·검토하였다. 그 결과, PERSIANN-CCS-CDR 자료로부터 산정된 Gumbel 분포의 매개변수들은 지속기간이 증가함에 따라 ASOS의 결과들과 유사한 양상을 보였으며 이를 토대로 얻어진 확률강수량은 지속기간이 짧은 경우 다소 큰 차이를 보였으나, 지속기간이 18 h 이상인 경우 그 차이는 약 20% 이내로 감소함을 확인하였다. 추가적으로, 남북한 차이를 살펴보았으며 Gumbel 분포 매개변수들 중 위치 매개변수의 차이가 두드러지게 나타남을 확인하였다. 지속기간의 증가에 따른 북한의 확률강수량이 상대적으로 작게 나타났으며, 지속기간 3 h 기준 남한의 84%, 지속기간 144 h 기준 70~75% 수준인 것으로 확인되었다.

• 한국토양비료학회지
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• 제35권2호
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• pp.87-92
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• 2002

북한 지역은 산이 많고, 경사지 밭이 많아 토양 유실이 심하므로, 토지를 보호하는 것이 토양의 지력 유지에 중요하다. 토양 보전 대책 마련을 위해서는 각 지역의 토양 유실 가능량을 예측하는 것이 필요하다. 토양 유실 예측에 USLE가 광범위하게 적용된다. 북한 지역의 토양 유실 예측에 필요한 강수 인자8의 산출이 시도되었다. 북한 지역의 수집 가능한 75개소의 월 강수량 자료를 이용하였다. 월 강수량의 7, 8월 강수 집중도(X)로부터 지역 보정 인자를 산출하였다. 지역 보정 인자($U_{adj}$) 산출을 위한 기본식은 남한의 중북부 지역 20개소의 $EI_{30}$와 월강수량 자료로부터 얻어 낸 식 $$U_{adj}=4.095{\cdot}X-0.878(r=0.689^{**})$$ 을 적용하였다. 북한 지역의 강수량은 606-1,520mm이었으며, 7, 8월 집중도는 34.4%~53.8%이었다. 이에 따른 지역 보정 계수 $U_{adj}$는 0.53~1.33이었으며, 동해안과 산간 지방의 $U_{adj}$ 값이 서해안과 내륙 지방의 $U_{adj}$ 값보다 낮았다. 지역 보정 계수를 고려한 USLE의 강수 인자 $R_{adj}$는 107 ~ 493으로 평가되었으며, 이는 평균 259.6으로 남한의 평균 R 값 434.5보다 낮았다.

• 한국농공학회논문집
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• 제61권2호
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• pp.1-11
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• 2019

Remote sensing products have long been used to monitor and forecast natural disasters. Satellite-derived rainfall products are becoming more accurate as space and time resolution improve, and are widely used in areas where measurement is difficult because of the periodic accumulation of images in large areas. In the case of North Korea, there is a limit to the estimation of precipitation for unmeasured areas due to the limited accessibility and quality of statistical data. CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) is global satellite-derived rainfall data of 0.05 degree grid resolution. It has been available since 1981 from USAID (U.S. Agency for International Development), NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration). This study evaluates the applicability of CHIRPS rainfall products for South Korea and North Korea by comparing CHIRPS data with ground observation data, and analyzing temporal and spatial drought trends using the Standardized Precipitation Index (SPI), a meteorological drought index available through CHIRPS. The results indicate that the data set performed well in assessing drought years (1994, 2000, 2015 and 2017). Overall, this study concludes that CHIRPS is a valuable tool for using data to estimate precipitation and drought monitoring in Korea.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 1999년도 학술발표회 논문집
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• pp.131-134
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• 1999

• 전기학회논문지
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• 제64권1호
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.