• Ocean and Polar Research
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• v.43 no.1
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• pp.15-30
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• 2021

In this study, using 16 ORA-IP (Ocean Reanalysis Intercomparison Project) data, we investigated spatial and temporal changes of net surface heat flux in the East Asian seas and presented a new ensemble net surface heat flux index. The ensemble net surface heat flux index is produced considering the data distribution and the standard deviation of each ORA-IP. From the correlation analysis with air temperature averaged over the Korean Peninsula, ensemble net heat flux around the Korea Strait shows the highest correlation (0.731) with a 3 month time lag. For the correlation study regarding precipitation over the Korean Peninsula, it also shows significant correlation especially in winter and spring seasons. Similar results are also found in comparison with climate indices (AO, PDO, and NINO3.4), but ensemble net surface heat flux data in winter season reveals the strongest correlation patterns especially with winter temperature and spring precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.525-533
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• 2008

Global circulation models (GCMs) have been used to study impact of climate change on water resources for hydrologic models as inputs. Recently, regional circulation models (RCMs) have been used widely for climate change study, but the RCMs have been rarely used in the climate change impacts on water resources in Korea. Therefore, this study is intended to use a set of climate scenarios derived by RegCM3 RCM ($27km{\times}27km$), which is operated by Korea Meteorological Administration. To begin with, the RCM precipitation data surrounding major rainfall stations are extracted to assess validation of the scenarios in terms of reproducing low frequency behavior. A comprehensive comparison between observation and precipitation scenario is performed through statistical analysis, wavelet transform analysis and EOF analysis. Overall analysis confirmed that the precipitation data driven by RegCM3 shows capabilities in simulating hydrological low frequency behavior and reproducing spatio-temporal patterns. However, it is found that spatio-temporal patterns are slightly biased and amplitudes (variances) from the RCMs precipitation tend to be lower than the observations. Therefore, a bias correction scheme to correct the systematic bias needs to be considered in case the RCMs are applied to water resources assessment under climate change.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.75-92
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• 2003

• Atmosphere
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• v.17 no.1
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• pp.55-68
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• 2007

Future climate changes over East Asia are projected by anthropogenic forcing of greenhouse gases and aerosols using ECHO-G/S (ECHAM4/HOPE-G). Climate simulation in the 21st century is conducted with three standard SRES scenarios (A1B, B1, and A2) and the model performance is assessed by the 20th Century (20C3M) experiment. From the present climate simulation (20C3M), the model reproduced reliable climate state in the most fields, however, cold bias in temperature and dry bias of summer in precipitation occurred. The intercomparison among models using Taylor diagram indicates that ECHO-G/S exhibits smaller mean bias and higher pattern correlation than other nine AOGCMs. Based on SRES scenarios, East Asia will experience warmer and wetter climate in the coming 21st century. Changes of geographical patterns from the present to the future are considerably similar through all the scenarios except for the magnitude difference. The temperature in winter and precipitation in summer show remarkable increase. In spite of the large uncertainty in simulating precipitation by regional scale, we found that the summer (winter) precipitation at eastern coast (north of $40^{\circ}N$) of East Asia has significantly increased. In the 21st century, the warming over the continents of East Asia showed much more increase than that over the ocean. Hence, more enhanced (weakened) land-sea thermal contrast over East Asia in summer (winter) will cause strong (weak) monsoon. In summer, the low pressure located in East Asia becomes deeper and the moisture from the south or southeast is transported more into the land. These result in increasing precipitation amount over East Asia, especially at the coastal region. In winter, the increase (decrease) of precipitation is accompanied by strengthening (weakening) of baroclinicity over the land (sea) of East Asia.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• Journal of Powder Materials
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• v.23 no.4
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• pp.287-296
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• 2016

$Ni_{1/3}Co_{1/3}Mn_{1/3}(OH)_2$ powders have been synthesized in a continuously stirred tank reactor via a co-precipitation reaction between aqueous metal sulfates and NaOH using $NH_4OH$ as a chelating agent. The co-precipitation temperature is varied in the range of $30-80^{\circ}C$. Calcination of the prepared precursors with $Li_2CO_3$ for 8 h at $1000^{\circ}C$ in air results in Li $Ni_{1/3}Co_{1/3}Mn_{1/3}O_2$ powders. Two kinds of obtained powders have been characterized by X-ray diffraction (XRD), scanning electron microscopy, particle size analyzer, and tap density measurements. The co-precipitation temperature does not differentiate the XRD patterns of precursors as well as their final powders. Precursor powders are spherical and dense, consisting of numerous acicular or flaky primary particles. The precursors obtained at 70 and $80^{\circ}C$ possess bigger primary particles having more irregular shapes than those at lower temperatures. This is related to the lower tap density measured for the former. The final powders show a similar tendency in terms of primary particle shape and tap density. Electrochemical characterization shows that the initial charge/discharge capacities and cycle life of final powders from the precursors obtained at 70 and $80^{\circ}C$ are inferior to those at $50^{\circ}C$. It is concluded that the optimum co-precipitation temperature is around $50^{\circ}C$.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.229-229
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• 2015

Downscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods in order to assess whether each method can model the climate conditions at various spatial scales properly. This study introduces a fundamental research from Jang and Kavvas(2015) that precipitation variability from a popular statistical downscaling method (BCSD) and a dynamical downscaling method (MM5) that is based on the NCAR/NCEP reanalysis data for a historical period and on the CCSM3 GCM A1B emission scenario simulations for a projection period, is investigated by means of some spatial characteristics: a) the normalized standard deviation (NSD), and b) the precipitation change over Northern California region. From the results of this study it is found that the BCSD method has limitations in projecting future precipitation values since the BCSD-projected precipitation, being based on the interpolated change factors from GCM projected precipitation, does not consider the interactions between GCM outputs and local geomorphological characteristics such as orographic effects and land use/cover patterns. As such, it is not clear whether the popular BCSD method is suitable for the assessment of the impact of future climate change at regional, watershed and local scales as the future climate will evolve in time and space as a nonlinear system with land-atmosphere feedbacks. However, it is noted that in this study only the BCSD procedure for the statistical downscaling method has been investigated, and the results by other statistical downscaling methods might be different.

• Journal of the Korean Geographical Society
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• v.32 no.1
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• pp.31-46
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• 1997

The purposes of this paper are to classify the spatial distribution types of precipitation by making daily isohyetal maps based on the winter daily precipitation and to analyse both the distributional characteristics of precipitation during the winter in South Korea and the synoptic characteristics related to them. Also, the correspondence between the spatial distribution types of precipitation and the synoptic characteristics occuring among them is examined with regards to pressure patterns and then precipitation distribution types. In addition, the characteristics of the pressure fields and temperature fields in 850hPa, 700hPa, and 500hPa level were analysed to find out the difference between the Ullung-do type and the Ullung-do${\cdot}$Honam type, which have similar characteristics on the surface weather map. As a result, the Ullung-do area showed a high frequency of occurrence regardless of precipitation classes, the East Coast area revealed a higher frequency of occurrence in over the 5mm section, while the Honam area had high frequency of occurrence in the 1~5mm section. There are twelve distribution types of precipitation during the winter. These distribution types show clear changes according to the season. The difference in precipitation distribution between the Ullung-do type and the Ullung-do${\cdot}$Honam type has a close relationship with the aspect of the upper cold air advection rather than the direction and the speed of the wind.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.77-89
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• 2021

In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.