• Journal of Climate Change Research
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• v.9 no.1
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• pp.47-58
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• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.423-433
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• 2018

Particulate matter air pollution is a serious problem affecting human health and visibility. The variations in $PM_{10}$ concentrations are influenced by not only local emission sources, but also atmospheric circulation conditions. In this study, we investigate the temporal features of $PM_{10}$ concentrations in South Korea and the atmospheric circulation patterns associated with high concentration episodes of $PM_{10}$ during winter (December-January-February) 2001-2016. Based on those analyses, a Korea Particulate matter Index (KPI) is developed to represent the large-scale atmospheric pattern associated with high concentration episodes of $PM_{10}$. The atmospheric patterns are characterized by persistent high-pressure anomalies, weakened lower-level north-westerly anomalies, and northward shift of the upper-level meridional wind anomalies near the Korean Peninsula. To evaluate the change in occurrence of high concentration episodes of $PM_{10}$ under a possible future warmer climate, we apply KPI analysis to CMIP5 climate simulations. Here, historical and two representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) are used. It is found that the occurrence of atmospheric conditions favorable for high $PM_{10}$ concentration episodes tends to increase over South Korea in response to climate change. This suggests that large-scale atmospheric circulation changes under future warmer climate can contribute to increasing high $PM_{10}$ concentration episodes in South Korea.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.240-240
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• 2019

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.

• Journal of Climate Change Research
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• v.34 no.20
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• pp.8257-8271
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• 2021

This study presents the ability of seasonal forecast models to represent the observed midlatitude teleconnection associated with El Niño-Southern Oscillation (ENSO) events over the North American region for the winter months of December, January, and February. Further, the impacts of the associated errors on regional forecast performance for winter temperatures are evaluated, with a focus on 1-month-lead-time forecasts. In most models, there exists a strong linear relationship of temperature anomalies with ENSO, and, thus, a clear anomaly sign separation between both ENSO phases persists throughout the winter, whereas linear relationships are weak in observations. This leads to a difference in the temperature forecast performance between the two ENSO phases. Forecast verification scores show that the winter-season warming events during El Niño in northern North America are more correctly forecast in the models than the cooling events during La Niña and that the winter-season cooling events during El Niño in southern North America are also more correctly forecast in the models than warming events during La Niña. One possible reason for this result is that the remote atmospheric teleconnection pattern in the models is almost linear or symmetric between the El Niño and La Niña phases. The strong linear atmospheric teleconnection appears to be associated with the models' failure in simulating the westward shift of the tropical Pacific Ocean rainfall response for the La Niña phase as compared with that for the El Niño phase, which is attributed to the warmer central tropical Pacific in the models. This study highlights that understanding how the predictive performance of climate models varies according to El Niño or La Niña phases is very important when utilizing predictive information from seasonal forecast models.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.997-1007
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• 2018

Drought monitoring is the important system for disasters by climate change. To perform this, it is necessary to measure the precipitation based on satellite rainfall estimation. The data developed in this study provides two kinds of satellite data (raw satellite data and bias-corrected satellite data). The spatial resolution of satellite data is 10 km and the temporal resolution is 1 day. South Korea was selected as the target area, and the original satellite data was constructed, and the bias-correction method was validated. The raw satellite data was constructed using TRMM TMPA and GPM IMERG products. The GRA-IDW was selected for bias-correction method. The correlation coefficient of 0.775 between 1998 and 2017 is relatively high, and TRMM TMPA and GPM IMERG 10 km daily rainfall correlation coefficients are 0.776 and 0.753, respectively. The BIAS values were found to overestimate the raw satellite data over observed data. By using the technique developed in this study, it is possible to provide reliable drought monitoring to Korean peninsula watershed. It is also a basic data for overseas projects including the un-gaged regions. It is expected that reliable gridded data for end users of drought management.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.285-291
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• 2020

The Noah 3.3 Land Surface Model (LSM) was used to estimate the global soil moisture in this study and these soil moisture datasets were assessed against satellite-based and reanalysis soil moisture products. The Noah 3.3 LSM simulated soil moistures in four soil layers and root-zone soil moistures defined as a depth-weighted average in the first three soil layers (i.e., up to 1.0 m deep). The Noah LSM soil moisture products were then compared with a satellite-based soil moisture dataset (European Space Agency Climate Change Initiatives (ESA CCI) SM v04.4) and reanalysis soil moisture datasets (ERA-interim). In addition, the five major basins (Yangtze, Mekong, Mississippi, Murray-Darling, Amazon) were selected for the assesment with the Gravity Recovery and Climate Experiment (GRACE)-based Total Water Storage Anomaly (TWSA) and TWS Change (TWSC). The results revealed that high anomaly correlations were found in most of the Asia-Pacific regions including East Asia, South Asia, Australia, and Noth and South America. While the anomaly correlations in the Murray-Darling basin were somewhat low, relatively higher anomaly correlations in the other basins were found. It is concluded that this study can be useful for the development of soil moisture based drought indices and subsequently can be helpful to reduce damages from drought by timely providing an efficacious strategy.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.69-79
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• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.

• Atmosphere
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• v.34 no.4
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• pp.463-480
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• 2024

This study aims to assess the performance of climate prediction systems around the world, and understand objective seasonal prediction skill of KMA's GloSea6. Using the 2023 hindcast verification values provided by the WMO Lead Centre for Long-Range Forecast (LC-LRF), we analyzed the skill in the global, East Asia, and European regions. The differences in prediction skill and RMSE between GPC (Global Producing Centers) were very small in this challenging area. Overall, GloSea6 showed the best ACC across variables and periods. Operating this outstanding climate prediction system not only ensures the provision of the best forecasting services but also offers excellent research and development tools. This result also suggests that seasonal forecasting requires different strategies against short- to medium-range forecast to account for climate prediction sources and reduce uncertainties. The skill differences between GloSea6-Seoul and GloSea6-Exeter, especially in high latitude, could be due to differences in snow and soil temperature initialization. Understanding these differences is important for future prediction system development. GPCs that use atmospheric only models instead of coupled, showed the limitations for seasonal predictions. Systems developed a relatively long time ago tended to perform low, suggesting that continuous improvements and upgrades are important. Among variables, SST showed the best prediction skill with the lowest RMSE. Temperature and pressure variables showed practical skill levels, around 0.5. We aimed to quantitatively assess the skills of climate prediction systems, and this assessment can guide the improvement and development of future systems and serve as a reference.