• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Journal of the Korean Chemical Society
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• v.56 no.5
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• pp.609-616
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• 2012

Ba-ferrite ($BaFe_{12}O_{19}$) nanoparticles were synthesized by chemical coprecipitation method in an aqueous solution. The particle size and the crystallization temperature of the Ba-ferrite nanoparticles were controlled varying the precipitation temperature. The precipitate that was prepared at $0^{\circ}C$ showed the crystal structure of Ba-ferrite in X-ray diffraction when it was calcined at the temperature above $580^{\circ}C$, whereas what was prepared at $50^{\circ}C$ showed the crystallinity when it was calcined at the temperature higher than about $700^{\circ}C$. The particle sizes of the synthesized Ba-ferrite were in a range of about 20-30 nm when it was prepared by being precipitated at $0^{\circ}C$ and calcined at $650^{\circ}C$. When the precipitation temperature increased, the particle size also increased even at the same calcination temperature. The magnetic properties of the Ba-ferrite nanoparticles were also controlled by the synthetic condition of precipitation and calcination temperature. The coercive force could be appreciably lowered without a loss of saturation magnetization when the Ba-ferrite nanoparticles were prepared by precipitation and calcination both at low temperatures.

• Journal of the Korean Ceramic Society
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• v.34 no.8
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• pp.886-896
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• 1997

To investigate an effect of phase separation on precipitation characteristics of CuCl nanocrystals in CuCl doped nonlinear optical glasses, borosilicate glass systems with 9 different compositions with ~2wt% of CuCl were selected and CuCl doped glasses were prepared by melting and precipitation method. Microstructural properties of the CuCl doped glasses were analyzed by optical absorption spectroscopy, acid elution test, TEM, and EDXS. While phase separation did not occur in Glass A~D, interconnected and droplet microstructures due to phase separation were found in Glass E, F and Glass G~I, respectively. In the particular composition of the matrix glasses in this study, the precipitation of the CuCl particles was observed in the phase separable glasses, not in phase non-separable glasses. The CuCl particles were precipitated in both silica-rich phase region and boronrich phase region of the glass matrix. In the case of 7.7Na2O-36.6B2O3-52.7SiO2(mole%) glass, the larger CuCl particles than those in the silica-rich phase region were observed in the boron-rich phase region.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• Journal of the Korean Ceramic Society
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• v.27 no.2
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• pp.274-282
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• 1990

SnO2 powder was prepared by hydroxide method and oxalate method. In hydroxide method, the pH dependence of powder characteristics was investigated by using buffer solution. As increasing the pH of solution, SnO2 powder size was decreased because nucleation rate was inctreased by more supersaturation of solution. Also, we found that the powder by our method has larger specific surface area in comaprison with other method. And the degree of agglomeration of precipitate with the change of precipitation temperature was investigated in oxalate method. The SnC2O4 was angular shape precipitate, and the size of the SnC2O4 was increased with the increase of precipitation temperature in methanol solvent.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.3
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• pp.323-332
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• 1996

Precipitation samples were collected by the wet-only sampling method at Seoul from September 1993 to June 1995. Sample were analysed for the anions $(NO_3^-, NO_2^-, SO_4^{2-}, Cl^-, and F^-)$ and cations $(Na^+, K^+, Ca^{2+}, Mg^{2+}, and NH_4^+)$ in addition to pH and electric conductivity. In order to establish the chemical analysis data of high quality, the assurance checks for analytical data of precipitation were performed by considering the ion balance and by comparing the measured conductivity with the calculated conductivity. As we applied the various assurance checking methods by the ion balance used until recently to a data set measured in this study, the f value expressed as $\Sigma C/\Sigma A$ was found to be not appropriate for the data screening. Also, the scattering plot between cations and anions in each sample was found to show the general tendency of ion balance but was proved to not quantitate the standard of data screening at a data set of samples of various concentration levels. The h value defined as (A-C)/C for C $\geq$ A and (A-C)/A for C < A was used to check the ion balance. However, the standard of data screening by h value must very in response to total ion concentration of samples. In this study, the quality assurance of chemical analysis data was checked by considering both the ion balance of evaluating by h value and the conductivity balance. Further the quality control was achieved by these quality assurance methods. As the result, 67 samples among total 77 were obtained as valid. As the central tendency value for a statistical summary in the analytical parametr of samples, the volume-weighted mean value was found to represent more the general chemistry of precipitation rather than the arithmetic mean. The volume-weighted mean pH was 5.0 and 25% of samples was less than this mean. The concentrations of sufate and nitrate in precipitation were 90.4 ueq/L and 32.4 ueq/L which made up 59% and 21% of all anions. The raion of $SO_4^{2-}/(NO_3^- + NO_2^-)$ in precipitation was 2.7, which indicates that the contributions of $H_2SO_4$ and $HNO_3$ to the acidity of precipitation are 70% and 30%, respectively.

• Journal of Environmental Science International
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• v.25 no.1
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• pp.99-105
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• 2016

In this paper, a groundwater hydrological study of the Gyeongju well during the Silla period is conducted to investigate how sufficiently the Gyeongju well supplied water demand at the time. It is assumed that the current geology and soil condition in Gyeongju remain similar to the Silla period. Also, the land use and land coverage during the Silla period is estimated based on the current land condition in Gyeongju. Precipitation during the Silla period is analyzed using precipitation data from 1984 to 2014 provided by Gyeonju weather station. Precipitation analysis is applied based on 3 different scenarios; precipitation intensity during the Silla period was Case (1) the same as, Case (2) 30% more, and Case (3) 30% less than the precipitation intensity of the last decade (2005~2014). Furthermore, to observe the use of the well in Gyeongju during droughts, the following condition(Case (4)) is also considered; ten year drought during the Silla period was the same as the ten year drought from 1984 to 2014. Available amount of groundwater development is analyzed using NRCS-CN method. The results show that the potential amount of groundwater in Gyeongju during Silla era was for Case (1) $62,825,272m^3/year$, Case (2) $93,606,567m^3/year$, Case (3) $32,277,298m^3/year$, and Case (4)$32,870,896m^3/year$. Also, it has been shown that $45,260,000m^3$ of groundwater were required to supply to all households in Gyeongju during Silla era. Therefore, if the precipitation intensity during Silla era was similar with the last decade, the groundwater would provide enough supply to all households in Gyeongju. However, in the case that the precipitation intensity during Silla era was 30% less than the last decade or a ten year drought happened, it is predicted that the water use in Gyeongju would have been limited.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.179-188
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• 2011

In this study, the regional factors in Gangwondo were used to analysis the relationship between point precipitation and areal precipitation. The most province area in Gangwondo is consist of mountainous terrain. At the east part of the Taebaek Mountains, the slope is very steep and the coastal plains don't exist. At the west part of the Taebaek Mountains, the slope is mild, there are many rivers, such as South Han-river and North Han-river, and the regions are very complex terrain. The data of 66 stations in Gangwondo and the PRISM (Parameter-elevation Regression on Indepedent Slope Model) were used to estimate the spatial distribution of precipitation. According to the topographic conditions, such as elevation and slope, and the regional conditions, such as Youngdong and Youngseo, the spatial distribution of precipitation is well shown. At the results of cross-validation, the RRBIAS and the RRMSE are under 0.1 and therefore the analysis of the PRISM are well conducted. Consequently the PRISM in this study is a appropriate method to estimate the spatial distribution of precipitation in Gangwondo.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.