• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.45-52
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• 2018

This study identifies the cause of the accident and presents a new concept for safe urban stream management by numerical simulating the flood event of Cheonggyecheon on August 17, 2017, using rain data measured through a dense weather observation network. In order to simulate water retention in the CSO channel listed as one of the causes of the accident, a reliable urban runoff model(XP-SWMM) was used which can simulate various channel conditions. Rainfall data measured through SK Techx using SK Telecom's cell phone station was used as rain data to simulate the event. The results of numerical simulations show that rainfall measured through AWSs of Korea Meteorological Administration did not cause an accident, but a similar accident occurred under conditions of rainfall measured in SK Techx, which could be estimated more similar to actual phenomena due to high spatial density. This means that the low spatial density rainfall data of AWSs cannot predict the actual phenomenon occurring in Cheonggyecheon and safe river management needs high spatial density weather stations. Also, the results of numerical simulation show that the residual water in the CSO channel directly contributed to the accident.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.17-27
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• 2019

In this study, we conducted a study to predict the photovoltaic power by constructing the sensor based meteorological data observation system and the accurate terrain data obtained by using LiDAR surveying. The average sunshine hours in 2018 is 4.53 hours and the photovoltaic power is 2,305 MWh. In order to analyze the effect of photovoltaic power on the installation angle of solar modules, we installed module installation angle at $10^{\circ}$ intervals. As a result, the generation time was 4.24 hours at the module arrangement angle of $30^{\circ}$, and the daily power generation and the monthly power generation were the highest, 3.37 MWh and 102.47 MWh, respectively. Therefore, when the module arrangement angle is set to $30^{\circ}$, the generation efficiency is increased by about 4.8% compared with the module angle of $50^{\circ}$. As a result of analyzing the influence of the seasonal photovoltaic power by the installation angle of the solar module, it was found that the photovoltaic power was high in the range of $40^{\circ}{\sim}50^{\circ}$, where the module angle was large from November to February when the weather was cold. From March to October, it was found that the photovoltaic power amount is $10^{\circ}{\sim}30^{\circ}$ with small module angle.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.337-347
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• 2019

This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM's and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.27-38
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• 2021

Recently, many studies have been conducted to use the radar rainfall in hydrology. However, in the case of weather radar, the beam is blocked due to the limitation of the observation such as mountain effect, which causes underestimation of the radar rainfall. In this study, the radar rainfall was estimated using the Hybrid Sacn Reflectivity (HSR) technique for hydrological use of weather radar and the runoff analysis was performed using the GRM model which is a distributed rainfall-runoff model. As a result of performing the radar rainfall correction and runoff simulation for 5 rainfall events, the accuracy of the dual-polarization radar rainfall using the HSR technique (Q_H_KDP) was the highest with an error within 15% of the ground rainfall. In addition, the result of runoff simulation using Q_H_KDP also showed an accuracy of R2 of 0.9 or more, NRMSE of 1.5 or less and NSE of 0.5 or more. From this study, we examined the application of the dual-polarization radar and this results can be useful for studies related to the hydrological application of dual-polarization radar rainfall in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.4
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• pp.139-147
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• 2021

In this study, waves were defined using the water surface elevation data observed from the HeMOSU-1 and 2 marine meteorological observation towers installed on the west coast of Korea, and correlation analysis was performed between wave parameters. The wave height and wave period were determined using the wave-train analysis method and the wave spectrum analysis method, and the relationship between the wave parameters was calculated and compared with the previous study. In the relation between representative wave heights, most of the correlation coefficients between waves showed a difference of less than 0.1% in error rate compared to the previous study, and the maximum wave height showed a difference of up to 29%. In addition, as a result of the correlation analysis between the wave periods, the peak period was estimated to be abnormally large at rates of 2.5% and 1.3% in HeMOSU-1&2, respectively, due to the effect of the bimodal spectrum that occurs when the spectral energy density is small.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.217-225
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• 2021

In this study, wave spectrum data were calculated using the water surface elevation data observed at 5Hz intervals from the HeMOSU-2 meteorological tower installed on the west coast of Korea, and wave parameters were estimated using wave spectrum data. For all significant wave height ranges, the peak enhancement parameter (γ_opt) of the JONSWAP spectrum and the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated based on the observed spectrum, and the distribution of each parameter was confirmed. As a result of the analysis, the peak enhancement parameter (γ_opt) of the JONSWAP spectrum was calculated to be 1.27, which is very low compared to the previously proposed 3.3. And in the range of all significant wave heights, the distribution of the peak enhancement parameter (γ_opt) was shown as a combined distribution of probability mass function (PMF) and probability density function (PDF). In addition, the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated to be [0.245, -1.278], which are lower than the existing [0.300, -1.098], and the result of the linear correlation analysis between the two parameters was β = -3.86α.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.424-433
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• 2021

Recent climate change has caused abnormal weather phenomena all over the world and a lot of damage in many fields of society. Particularly, a lot of recent damages were due to extreme precipitation, such as torrential downpour or drought. The objective of this study was to analyze the temporal and spatial changes in the precipitation pattern in South Korea. To achieve this objective, this study selected some of the precipitation indices suggested in previous studies to compare the temporal characteristics of precipitation induced by climate change. This study selected ten ASOS observatories of the Korea Meteorological Administration to understand the change over time for each location with considering regional distribution. This study also collected daily cumulative precipitation from 1951 to 2020 for each point. Additionally, this study generated high-resolution national daily precipitation distribution maps using an orographic precipitation model from 1981 to 2020 and analyzed them. Temporal analysis showed that although annual cumulative precipitation revealed an increasing trend from the past to the present. The number of precipitation days showed a decreasing trend at most observation points, but the number of torrential downpour days revealed an increasing trend. Spatially, the number of precipitation days and the number of torrential downpour days decreased in many areas over time, and this pattern was prominent in the central region. The precipitation pattern of South Korea can be summarized as the fewer precipitation days and larger daily precipitation over time.

• Atmosphere
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• v.31 no.5
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• pp.577-592
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• 2021

This study evaluates the predictability of the number of days of heat and cold damages by growth stages of rice in South Korea using the hindcast data (1986~2020) produced by Pusan National University Coupled General Circulation Model-Weather Research and Forecasting (PNU CGCM-WRF) model chain. The predictability is accessed in terms of Root Mean Square Error (RMSE), Normalized Standardized Deviations (NSD), Hit Rate (HR) and Heidke Skill Score (HSS). For the purpose, the model predictability to produce the daily maximum and minimum temperatures, which are the variables used to define heat and cold damages for rice, are evaluated first. The result shows that most of the predictions starting the initial conditions from January to May (01RUN to 05RUN) have reasonable predictability, although it varies to some extent depending on the month at which integration starts. In particular, the ensemble average of 01RUN to 05RUN with equal weighting (ENS) has more reasonable predictability (RMSE is in the range of 1.2~2.6℃ and NSD is about 1.0) than individual RUNs. Accordingly, the regional patterns and characteristics of the predicted damages for rice due to excessive high- and low-temperatures are well captured by the model chain when compared with observation, particularly in regions where the damages occur frequently, in spite that hindcasted data somewhat overestimate the damages in terms of number of occurrence days. In ENS, the HR and HSS for heat (cold) damages in rice is in the ranges of 0.44~0.84 and 0.05~0.13 (0.58~0.81 and -0.01~0.10) by growth stage. Overall, it is concluded that the PNU CGCM-WRF chain of 01RUN~05RUN and ENS has reasonable capability to predict the heat and cold damages for rice in South Korea.