• Asian Journal of Atmospheric Environment
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• v.4 no.1
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• pp.20-25
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• 2010

The gaseous $^{222}Rn$ concentration at the level of clouds was estimated by using the wet scavenging model of its decay products with the observed data of environmental radiation at the ground. And the origin of the $^{222}Rn$ was also discussed. The estimation was done for a precipitation event on Dec. 26-27, 2003, when a large increase of the radiation was observed in Tokai-mura in Ibaraki, Japan. From a backward trajectory analysis, the origin of $^{222}Rn$ atoms for that event was traced back to the northeastern part of China, and it was expected that the large amount of $^{222}Rn$ emitted in the northeastern part of China was transported to Tokai-mura by the Eurasian continental air mass.

• Water for future
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• v.13 no.3
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• pp.77-81
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• 1980

Probable Maximum Precipitation values for seven heavy stroms during the period from 1966 to 1976 are derived, using the manual for W.M.O P.M.P analysis of strom precipitation. The hydrometeoroogical and the statistical methods are consisted of the procedure of P.M.P. estimation in this study. It is possible to draw P.M.P curves from the view points of area and strom durations. A comparison has been made between the P.M.P values of Nakding River basin and the results of this paper. For a storm period of 24 hours, the P.M.P value at the maximum station is 762 mm and the moistrue maximization ratio are within the range 1.17 to 1.41 for the seven selected stroms.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Atmosphere
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• v.25 no.2
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• pp.283-294
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• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.112-121
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• 2017

This study proposed the quantitative precipitation estimation method using overlapped area in radar network. For this purpose, the dense rain gauges and radar network are used. 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.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.693-703
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• 2008

The important source of the mercury in water-column is the influx of atmosphere mercury, via dry and wet deposition. In this study, wet deposition of mercury was estimated to be $14.56{\mu}g/m^2$ during 15 months at the Lake Soyang, which is a little higher than those observed in the several rural US Mercury Deposition Network (MDN) sites with similar precipitation depth. The mercury concentration in precipitation did not show a positive correlation with atmospheric RGM (reactive gaseous mercury) concentration, while maintaining good correlation with atmospheric $PM_{2.5}$ at Soyang Dam. This result suggests that the contribution of particulate Hg to the total Hg wet deposition should be more significant than that of RGM. In this study, both precipitation depth and precipitation type affected the amount of wet deposition and the concurrent mercury levels in precipitation. There was generally an inverse relationship between precipitation depth and Hg concentration in precipitation. Precipitation type was another factor that exerted controls on the Hg concentration in precipitation. As a result, the highest concentration of Hg was observed in snow, followed by in mixture (snow+rain) and in rain.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1427-1430
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• 2009

The amount and the continuity of the precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study the eight methods widely used as methods for estimating are compared. The data used in this research is the annual precipitation amount during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station is estimated and the areal average of annual precipitation for 32 years at the Han River basin is calculated.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1053-1064
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• 2022

The Thiessen method, which is the current area average precipitation method, has serious structural limitations in accurately calculating the average precipitation in the watershed. In addition to the observation accuracy of the precipitation meter, errors may occur in the area average precipitation calculation depending on the arrangement of the precipitation meter and the direction of the heavy rain. When the watershed is small and the station density is sparse, in both simulation and observation history, the Thiessen method showed a peculiar tendency that the average precipitation in the watershed continues to increase and decrease rapidly for 10 minutes before and after the peak. And the average precipitation in the Thiessen basin was different from the rainfall radar at the peak time. In the case where the watershed is small but the station density is relatively high, overall, the Thiessen method did not show a trend of sawtooth-shaped over-peak, and the time-dependent fluctuations were similar. However, there was a continuous time lag of about 10 minutes between the rainfall radar observations and the ground precipitation meter observations and the average precipitation in the basin. As a result of examining the ground correction effect of the rainfall radar watershed average precipitation, the correlation between the area average precipitation after correction is rather low compared to the area average precipitation before correction, indicating that the correction effect of the current rainfall radar ground correction algorithm is not high.

• Water for future
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• v.7 no.1
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• pp.65-70
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• 1974

According to the simplified Gringorten's method of extreme values from data samples, daily maximum precipitation and return period in Seoul was estimated. And also, it was known that the distribution of daily maximum precipitation of Seoul area belong th an exponential type of distribution.