• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.141-153
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• 2012

In this study, we estimated probable precipitation amounts at the target year (2020, 2030, 2040) of 55 weather stations in Korea using the 24 hour annual maximum precipitation data from 1973 through 2009 which should be useful for management of agricultural reservoirs. Not only trend tests but also non-stationary tests were performed and non-stationary frequency analysis were conducted to all of 55 sites. Gumbel distribution was chosen and probability weighted moment method was used to estimate model parameters. The behavior of the mean of extreme precipitation data, scale parameter, and location parameter were analyzed. The probable precipitation amount at the target year was estimated by a non-stationary frequency analysis using the linear regression analysis for the mean of extreme precipitation data, scale parameter, and location parameter. Overall results demonstrated that the probable precipitation amounts using the non-stationary frequency analysis were overestimated. There were large increase of the probable precipitation amounts of middle part of Korea and decrease at several sites in Southern part. The non-stationary frequency analysis using a linear model should be applicable to relatively short projection periods.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.557-577
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• 2023

This study defines non-precipitation information as areas with weak precipitation or cloud particles that radar cannot detect due to weak returned signals, and suggests methods for its utilization in data assimilation. Previous studies have demonstrated that assimilating radar data from precipitation echoes can produce precipitation in model analysis and improve subsequent precipitation forecast. However, this study also recognizes the non-precipitation information as valuable observation and seeks to assimilate it to suppress spurious precipitation in the model analysis and forecast. To incorporate non-precipitation information into data assimilation, we propose observation operators that convert radar non-precipitation information into hydrometeor mixing ratios and relative humidity for the Weather Research and Forecasting Data Assimilation system (WRFDA). We also suggest a preprocessing method for radar non-precipitation information. A single-observation experiment indicates that assimilating non-precipitation information fosters an environment conducive to inhibiting convection by lowering temperature and humidity. Subsequently, we investigate the impact of assimilating non-precipitation information to a real case on July 23, 2013, by performing a subsequent 9-hour forecast. The experiment that assimilates radar non-precipitation information improves the model's precipitation forecasts by showing an increase in the Fractional Skill Score (FSS) and a decrease in the False Alarm Ratio (FAR) compared to experiments in which do not assimilate non-precipitation information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.29-39
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• 2014

In this study, we performed a non-stationary frequency analysis using a power model and the model was applied for Seoul, Daegu, Daejeon, Mokpo sites in Korea to estimate the probable precipitation amount at the target years (2020, 2050, 2080). We used the annual maximum precipitation of 24 hours duration of precipitation using data from 1973 to 2009. We compared results to that of non-stationary analyses using the linear and logistic regression. The probable precipitation amounts using linear regression showed very large increase in the long term projection, while the logistic regression resulted in similar amounts for different target years because the logistic function converges before 2020. But the probable precipitation amount for the target years using a power model showed reasonable results suggesting that power model be able to reflect the increase of hydrologic extremes reasonably well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Journal of Environmental Health Sciences
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• v.22 no.4
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• pp.62-68
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• 1996

Precipitation samples were collected by the wet-only event sampling method at Seoul from September 1991 to April 1995. Concentrations of samples for the ion components($NO_3^-, NO_2^-, SO_4^{2-}, Cl^-, F^-, Na^+, K^+, Ca^{2+}, Mg^{2+}$ and $NH_4^+$) were measured in addition to pH and electric conductivity. During the sampling period, 182 samples were collected, but only 163 samples were identified as valid. The pH, calculated from the volume-weighted $H^+$ concentration, was found to be 4.7, indicating a relatively intensive acidity compared with data from other regions of the world, where acid deposition was known to be a problem. Above all, the concentration of non-seasalt sulfate was $84 \mu eq/L$, which was the highest compared to that measured in other regions of the world. The major acidifying ions in the precipitation at Seoul were identified as sulfate and nitrate except for chloride, because the Cl/Na ratio in the precipitation was close to the ratio in seawater. If all of the non-seasalt sulfate and nitrate existed in the form of sulfuric and nitric acids, respectively, the average pH in the precipitation was calculated as 3.7, lower than the measured value. Consequently, the difference between the calculated and measured pH suggest that the acidity of precipitation was neutralized by alkaline species, not due to the low contribution of an anthropogenic air pollutants to the precipitation. The equivalent concentration ratio of sulfate to nitrate was 3.5, which indicated that the contributions of sulfuric and nitric acids to the precipitation acidity were 78% and 22%, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1247-1250
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• 2004

The amount of precipitation during El Nino over Korea increases in Summer and Winter. However, it decreases in Fall, and exhibits not much change in Spring. Especially, the amount of precipitation during September of El Nino year is much less than that of the September of non-El Nino year. The amount of precipitation during El Nino year of October and November shows similar amount of precipitation during non-El Nino year of the same period. The reason for decreasing precipitation in September is related to the weakening of the 2nd rainy season during the development of El Nino over East Asia including Korea. Insufficiency of fall precipitation during El Nino year influences drought in Spring for next year.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.242-247
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• 2015

In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.6
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• pp.457-467
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• 2019

Because non-point source pollution is very closely related to hydrological characteristics, its importance is highly emphasized nowadays along with accelerating climate change. Especially for Korea, the non-point source pollution and its control are entirely depending on runoff, precipitation, drainage, land use or development, based on geographical and topographical reasons of Korea. Many studies reported the physical (e.g., apparatus- and natural-type facilities, etc.) and chemical methods (e.g., organic and inorganic coagulants, etc.) of controling non-point pollutant source pollution, however, those are needed to be reconsidered along with climate change causing the unexpected patterns and amounts of precipitation and strengthen complexity of social community. The objectives of this study are to assess recent situations of non-point source pollution in Korea and its control means and to introduce possible effective ways of non-point source pollution against climate change in near future.