• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.2
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• pp.86-94
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• 2023

Climate change is more rapid in the Arctic than elsewhere in the world, and increased precipitation and warming are expected cause changes in biogeochemical processes due to altered microbial communities and activities. It is crucial to investigate microbial responses to climate change to understand changes in carbon and nitrogen dynamics. We investigated the effects of increased temperature and precipitation on microbial biomass and community structure in dry tundra using two depths of soil samples (organic and mineral layers) under four treatments (control, warming, increased precipitation, and warming with increased precipitation) during the growing season (June-September) in Cambridge Bay, Canada (69°N, 105°W). A phospholipid fatty acid (PLFA) analysis method was applied to detect active microorganisms and distinguish major functional groups (e.g., fungi and bacteria) with different roles in organic matter decomposition. The soil layers featured different biomass and community structure; ratios of fungal/bacterial and gram-positive/-negative bacteria were higher in the mineral layer, possibly connected to low substrate quality. Increased temperature and precipitation had no effect in either layer, possibly due to the relatively short treatment period (seven years) or the ecosystem type. Mostly, sampling times did not affect PLFAs in the organic layer, but June mineral soil samples showed higher contents of total PLFAs and PLFA biomarkers for bacteria and fungi than those in other months. Despite the lack of response found in this investigation, long-term monitoring of these communities should be maintained because of the slow response times of vegetation and other parameters in high-Arctic ecosystems.

• Atmosphere
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• v.32 no.1
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.237-248
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• 2008

Precipitation variability in Korea is mainly influenced by climate circulation such as sea surface temperature, not a local convection. Therefore, this study investigates relationship between monthly precipitation of 61 station observed by Korea Meteorological Administration and global sea surface temperatures (SSTs). The main components of monthly precipitation in Korea are extracted by a method which consists of the principal analysis combined with the cluster analysis, to examine the correlation between monthly rainfalls and SSTs. The relationships between main components of monthly precipitation and SSTs exists in Pacific Ocean. At the result of Wavelet Transform analysis, The 2-4 year band have a strong wavelet power spectrum and the low frequency. the correlation coefficient between low frequency components of monthly rainfalls and SSTs calculated bigger then correlation coefficient between main components and SSTs. Hence, these results propose a prediction possibility of monthly precipitations using the varition of SSTs.

• Atmosphere
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• v.28 no.2
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• pp.211-222
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• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• Korean Journal of Materials Research
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• v.14 no.1
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• pp.9-12
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• 2004

Metallic impurities, such as Fe, Cu, and Au, become generation and recombination centers for minority carriers when combined with oxide precipitates or silicon self-interstitial clusters. As these centers may cause leakage and discharge in silicon devices, their prevention through gettering of the metallic impurities is an important issue. In this article, key aspects of intrinsic gettering, such as oxygen control, wafer cleaning, device area denudation, and bulk oxygen precipitation are discussed, and a practical method of implementing intrinsic gettering is outlined.

• Journal of the Korean Ceramic Society
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• v.35 no.7
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• pp.691-698
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• 1998

• The Journal of Engineering Research
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• v.7 no.1
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• pp.99-103
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• 2005

The ferrimagnetic resonance technique, with the inclusion of shaper anisotropy effects, was used to obtain information about the early stages in the precipitation of magnesium ferrite from iron-doped magnesia. The very small magnesioferrite particles were produced by precipitation method from solid solution of iron ion in single crystal magnesia. The temperature dependence of the resonance anisotropy field for a coherent assembly of hyperfine magnesium ferrite precipitates was investigated in the range 100~400K. The results are interpreted in terms of the shape anisotropy of the precipitates.

• Bulletin of the Korean Chemical Society
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• v.19 no.7
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• pp.766-770
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• 1998

On-line preconcentration by direct precipitation with hydroxide has been developed and applied for the analysis of Cd in Inductively Coupled Plasma Atomic Emission Spectrometry. Cadmium is continuously precipitated with hydroxide and dissolved by nitric acid in on-line mode. Currently, the enrichment factor is more than 90 times for 20.0 mL of sample and could be further increased very easily. For a large sample throughput, 1.0 mL of sample loop is used and the enrichment factor is 4.5 with the sampling speed of 15/hr. The method has been applied to the analysis of NIST reference sample and has yielded good results with the certified value.