• Korean Journal of Plant Taxonomy
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• v.51 no.1
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• pp.49-99
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• 2021

Gwangdeoksan Mountain (699.3 m) is the highest border mountain between the two cities of Chungcheongnamdo Province, Cheonan and Asan, Korea. In this study, we investigated the flora of Gwangdeoksan Mt. from April of 2015 to October of 2017. Through 20 independent field investigations, we identified and tallied a total of 428 species, 9 subspecies, 30 varieties, and a forma in 287 genera and 97 families. Of a total of 468 taxa, 128 taxa in 112 genera and 58 families were found to be Korean endemic species (7 taxa), floristic regional indicator species (45 taxa), rare or endangered species (3 taxa), species subject to the approval of outbound transfer (73 taxa), and alien or ecosystem disturbing species (32 taxa). The flora of Gwangdeoksan Mt. can be divided into four distinct floristic subregions, with higher diversity in the north-facing subregion. The complex flora of Gwangdeoksan Mt., emerging at the edge of two floristic regions of the Korean peninsula, may represent a significant conservation priority and a topic for future ecological and geographical studies.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.4
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• pp.325-337
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• 2020

The Jurassic granitic rocks in the area of Gwangdeoksan located along the boundary between Hwacheon and Cherwon in northern Gyeonggi Massif consist of two-mica granite, garnet-bearing two-mica granite, mica-granite, and porphyritic biotite granite. These granitic rocks are calc-alkaline series and plotted in peraluminious domain in A/CNK vs. A/NK diagram. Petrographical and geochemical data indicate that the porphyritic biotite granite which intruded at the last period originated from distinct parental magma from two-mica granite, garnet-bearing two-mica granite, and mica-granite. On the basis of Rb/Sr vs. Rb/Ba diagram and Al2O3/TiO2 vs. CaO/Na2O, it is inferred the porphyritic biotite granite originated from protolith with less pelitic composition than 3 other granitic rocks. The enriched values of lithophile elements of Cs, Rb, and Ba and negative trough of Nb, P, Ti on spider diagram suggest that the peraluminous Jurassic granitic rocks in Gwangdeoksan area formed in subduction tectonic environment. Whole-rock zircon saturation thermometer indicates that the granitic rocks in the study area were melted at 692-795℃.

• Atmosphere
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• v.21 no.4
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• pp.447-454
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• 2011

As dust storms originated in Neimongu Plateau and Manchuria became more frequent in Korea, there was a growing need for Asian Dust (Hwangsa) monitoring stations in North Korea, which is a pathway of Asian Dust to South Korea. The South Korean and the North Korean Governments agreed to build the Automatic Weather System and the PM10 measurement instruments in the Gaeseong Industrial Zone and the Keumgangsan Tourist Region, North Korea in 2007. PM10 mass concentration data in the Keumgangsan Tourist Region could be collected only during the period from September 2007 to May 2008. In this study, daily, monthly and diurnal variations of PM10 mass concentration of the Keumgangsan are analyzed and compared with those of Sokcho and Gwangdeoksan. Three sites show similar variations in daily and monthly means. Correlation coefficients (r) between Sokcho and Keumgangsan, and between Gwangdeoksan and Keumgangsan are 0.89 and 0.67, respectively. But diurnal variation at Keumgangsan has a distinct feature compared to the other sites. Diurnal PM10 variation shows two peaks around 8 AM and 4-5 PM and very low at night. The difference between the daily maximum and minimum is $20{\sim}60{\mu}g\;m^{-3}$ during September to November 2007. Temperature, relative humidity and wind speed from the Keumgangsan AWS data were compared with those from the Changjon station, and showed good correlation each other except wind speed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.132-132
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• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.142-142
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• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.37-48
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• 2010

The purpose of this paper is to evaluate how the rainfall field effect on a runoff simulation using grid radar rainfall data and ground gauge rainfall. The Gwangdeoksan radar and ground-gauge rainfall data were used to estimate a spatial rainfall field, and a hydrologic model was used to evaluate whether the rainfall fields created by each method reproduced a realistically valid spatial and temporal distribution. Pilot basin in this paper was the Naerin stream located in Inje-gun, Gangwondo, 250m grid scale digital elevation data, land cover maps, and soil maps were used to estimate geological parameters for the hydrologic model. For the rainfall input data, quantitative precipitation estimation(QPE), adjusted radar rainfall, and gauge rainfall was used, and then compared with the observed runoff by inputting it into a $Vflo^{TM}$ model. As a result of the simulation, the quantitative precipitation estimation and the ground rainfall were underestimated when compared to the observed runoff, while the adjusted radar rainfall showed a similar runoff simulation with the actual observed runoff. From these results, we suggested that when weather radars and ground rainfall data are combined, they have a greater hydrological usability as input data for a hydrological model than when just radar rainfall or ground rainfall is used separately.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.