• 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 Environmental Science International
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• v.30 no.7
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• pp.519-527
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• 2021

Moving average precipitation provides periodic precipitation patterns by solving precipitation irregularities. However, due to uncertain moving average periods, excessive data smoothing occurs, which limit the possibility to analyze groundwater levels in the short term. Nonetheless, groundwater level fluctuation can compensate these limitations as it can calculate appropriately for unit time and verify the effect of precipitation penetrated into groundwater in a short time period. In this study, the characteristics of groundwater level were evaluated using groundwater level fluctuation to compensate for limitations of groundwater level analysis using moving average precipitation. In addition, the groundwater quality was investigated using the electrical conductivity fluctuation. The study site was Hyogyo-ri, Yesan-si, Chungcheongnam-do. Four observation wells and an automated weather system were used. The correlation between groundwater level fluctuation and precipitation (Case 1) and the correlation between groundwater level and moving average precipitation (Case 3) were compared. In the analysis for 1 hour data, the correlation coefficient of Case 1 was higher than that of Case 3, and in the analysis for 1 day data, the correlation coefficient of Case 3 was higher than that of Case 1.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.397-404
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• 2000

A total of 65 rain gauges with Automatic Weather Station(AWS) were used to regional analysis of precipitation. Nine cluster regions were identified using geographical locations, maximum, mean, standard deviation of 1 day maximum rainfalls, mean annual precipitation and rainfall of rainy season in Korea. The mean annual precipitation, geographical locations, and the synoptic generating mechanisms were used to identify th five climatological homogeneous regions in Korea. Number of final regions by mean annual precipitation and cluster methods divided into five regions in Korea.

• 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.

• 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.

• Atmosphere
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• v.16 no.2
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• pp.55-65
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• 2006

The characteristics of Depth-Area-Duration (DAD) for 50 storms over the Geumgang river basin have been analysed in terms of various storm causes using the precipitation data during the period from 1984 to 2003. Results show that the ratio of the precipitation depth to duration, and the ratio of decrease in the precipitation depth to area are the largest in the case of the tropical cyclone. Storm maximization ratios are in the range 1.03 to 2.66 for the 50 selected heavy precipitation cases over Geumgang river basin, with the largest value for the tropical cyclone case, suggesting that the tropical cyclone could cause heavier precipitation than the other storms. In addition, the 24-hour probable maximum precipitation for the Geumgang river basin is estimated to be about 745 mm in the maximum precipitation area.

• Atmosphere
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• v.29 no.5
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• pp.615-626
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• 2019

To find any effects of precipitation climate on the forecast verification methods, we processed the hourly records of precipitation over South Korea. We examined their relationship between the climate and the methods of verification. Precipitation is an intermittent process in South Korea, generally less than an hour or so. Percentile ratio of precipitation period against the entire period of the records is only 14% in the hourly amounts of precipitation. The value of the forecast verification indices heavily depends on the climate of rainfall. The direct comparison of the index values might force us to have a mistaken appraisal on the level of the forecast capability of a weather forecast center. The size of the samples for verification is not crucial as long as it is large enough to satisfy statistical stability. Our conclusion is still temporal rather than conclusive. We may need the amount of precipitation per minute for the confirmation of the present results.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.179-191
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• 1993

This paper aims to know the characteristics of occurrence of the anomaly level and variability of the monthly precipitation in Kyeongnam, Korea. For this study, it was investigated 주e distribution of the annual and cont비y mean precipitation, the precipitation variability and its annual change, and the characteristics of occurrence of the anomaly level in Kyeongnam area the results were summarized as follows : 1) she mean of annual total precipitation averaged over Kyeongnam area is 1433.3mm. I'he spatial distribution of the annual total precipitation shows that in Kyeongnam area, the high rainfall area locates in the southwest area and south coast and the low rainfall area in an inland area. 2) Monthly mean precipitation in llyeongnam area was the highest in July(266.4mm) 각lowed by August(238.0mm), June(210.2mm) in descending order. In summer season, rainfall was concentrated and accounted for 49.9 percent of the annual total precipitation. Because convergence of the warm and humid southwest current which was influenced by Changma and typhoon took place well in this area. 3) The patterns of annual change of precipitaion variability can be divided into two types; One is a coast type and the other an inland type. The variability of precipitation generally appears low in spring and summer season and high in autumn and winter season. This is in accord with the large and small of precipitation. 4) The high frequency of anomaly level was N( Normal)-level and the next was LN( Low Informal) -level and 25(Extremely Subnormal)-level was not appeared in all stations. The occurrence frequency of N level was high in high rainfall area and distinguish성 in spring and summer season but the low rainfall area was not. hey Words : anomaly level, variability, precipitation, coast type, inland type.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.1-6
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• 2008

Precipitation and groundwater level data sets from Kum river watershed were analyzed and compared. The correlation between groundwater level and the moving average of precipitation was analyzed. Moving averaging technique is stochastic method and that was used to consider the effect of precipitation events on groundwater level fluctuation. Groundwater level generally follows seasonal precipitation pattern and low level occurs from early December to late April. Relatively high groundwater level is appeared in wet spell (July and August). The correlation between groundwater level and the moving average of precipitation to consider precedent precipitation events was analyzed with minimum two-year data sets. When the precipitation and groundwater level data set pair was selected the precipitation gauge station is closely located to groundwater level gauge station in the upstream direction to minimize the non-homogeneous precipitation distribution effect. The maximum correlation was occurred when the averaging periods were from 10 days to 150 days with Kum river watershed data. The correlation coefficients are influenced by data quality, missing data periods, or snow melt effect, etc. The maximum coefficient was 0.8886 for Kum river watershed data.

• 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.