• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.44-53
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• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• Water for future
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• v.11 no.2
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• pp.62-69
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• 1978

Interstational and interseasonal analyses of the correlation and variability in the seasonal and annual precipitation for 10 basic synoptic stations in South Korea, on the basis of rainfall record of over 40 years, are carried out. It is found that the climatic regions of precipitation could be classified by means of the interstational analysis for the correlations. Corrleation coefficients in interstational relationship of precipitation are lowest in autumn which characterizeds a strong locality while the highest value shows a relatively weak locality in winter. Interseasonal relationship between summer and winter precipitation shows mostly 10 percent significant level with all positive values. The magnitude of the variation coefficients are appeared to be in the order of winter, autumn, spring and summer. It is shown that the highest which is winter ranges between 0.33 0.58, and for the lowest summer, 0.26-0.44, respectively in the areal distribution of the coefficient. The secular changes of the variation coefficient in the recent trend show increases in spring at two station; Seoul and Incheon, in summer at Busan and in autumn at two stations; Busan and Incheon while in winter show devreases at the whole stations. An annual variation seems to show generally a constant trend as whole for all the stations.

• Macromolecular Research
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• v.17 no.7
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• pp.483-490
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• 2009

Stable poly(styrene-co-divinylbenzene) [P(St-co-DVB)] micro spheres with narrow size distribution were synthesized in the presence of 2,2'-azobis(2,4-dimethyl valeronitrile) (V-65) and co-initiator in an acetone/water mixture in the precipitation polymerization at $53^{\circ}C$ for 24 h. Potassium peroxodisulfate (KPS), ammonium peroxodisulfate (APS) and sodium peroxodisulfate (NaPS) were used as co-initiators. The optimum ratio of acetone to water for the formation of a narrow distribution of P(St-co-DVB) particles was 49:11 (g/g). The optimum co-initiator compositions for narrow distribution were 9:1 (g/g) for V-65 to KPS, 11:1 for V-65 to APS and 6:1 for V-65 to NaPS. The yield for these compositions was $54{\sim}57%$ and the largest particle size was obtained with the lowest zeta-potential and CV values. From the XPS measurements, the charge density was increased but the zeta potential decreased with increasing sulfur content, implying that the sulfate group provides the electrostatic stabilization on the particle surface. This suggested that the self-crosslinking between styrene and DVB, the electrostatic stabilization of initiators, and the balanced hydrophobic and hydrophilic properties of the solvents are responsible for the formation of stable P(St-co-DVB) spherical particles with narrow size distribution.

• Journal of the Korean earth science society
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• v.27 no.2
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• pp.149-158
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• 2006

This study analyzes the seasonality precipitation using precipitation data from 1973 to 2001 over South Korea. The Seasonality Index and Annual variation of the Seasonality Precipitation were investigated from sixty-three observation stations. The Seasonality Precipitation means the degree of the precipitation falling intensively for some specific months. Spatially, precipitation that has a strong characteristic of regional shower is defined as seasonal precipitation. Precipitation forms are changed with various reasons and mainly the sporadic and local shower precipitation after rain spell in summer. Especially there appears a tendency that this kind of precipitation is sharply increasing in 1990's. Seasonality Index is used as a method to understand seasonal precipitation. If the yearly rainfall is concentrated for some specific months, Seasonality Index is growing gradually. It is confirmed that there is a tendency that all the from sixty-two observation stations Seasonality Index. While Seasonality Index over South of Korea concentrated from June to August because of the summer rain spell in the past ($1973{\sim}1982$), there appears to be a tendency that it concentrated from August and September since the mid 1990's. From the analysis of seasonal precipitation intensity distribution, most of southern Korea is under seasonality precipitation intensity 4. The seasonality precipitation intensity classification results are as follow: most of the observation stations were on a scale intensity of 3 and 4 in the past but currently reads seasonality precipitation intensities of 5 and 6.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.732-743
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• 2005

The characteristics of precipitation over South Korea from 1973 to 2002 were investigated. The synoptic patterns inducing precipitation are classified by 10 categories, according to the associated surface map analysis. The annual mean frequency of the total precipitation, its duration time and amount for 30 years are 179 times, 2.9 hours, and 7.1 mm, respectively. About $59\%$ of the total precipitation events were associated with a synoptic low. The dominant patterns are identified with respect to seasons: A synoptic mobile low pressure pattern is frequent in spring, fall, and winter, whereas low pressure embedded within the Changma and orography induced precipitation are dominant in summer and in winter. For the amount of precipitation, precipitation originated from tropical air associated with typhoon, tropical convergence, and Changma is more significant than that with other pressure patterns. The statistical elapse time reaching to 80 mm, which is the threshold amount of heavy rainfall watch at KMA, takes 12.9 hours after the onset of precipitation. The probability distribution function of the precipitation shows that the maximum probability for heavy rainfall is located at the south-coastal region of the Korean peninsula. It is also shown that the geographical distribution of the Korean peninsula plays an important role in occurrence of heavy rainfall. For example, heavy precipitation is frequently occurred at Youngdong area, when typhoon passes along the coastal region of the back borne mountains in the peninsula. The climatological classification of synoptic patterns associated with heavy rainfall over South Korea can be used to provide a guidance to operational forecast of heavy rainfall in KMA.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1125-1139
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• 2006

The cluster analysis of diurnal precipitation patterns is performed by using daily precipitation of 59 stations in South Korea from 1973 to 1996 in four seasons of each year. Four seasons are shifted forward by 15 days compared to the general ones. Number of clusters are 15 in winter, 16 in spring and autumn, and 26 in summer, respectively. One of the classes is the totally dry day in each season, indicating that precipitation is never observed at any station. This is treated separately in this study. Distribution of the days among the clusters is rather uneven with rather low area-mean precipitation occurring most frequently. These 4 (seasons)$\times$2 (wet and dry days) classes represent more than the half (59 %) of all days of the year. On the other hand, even the smallest seasonal clusters show at least $5\sim9$ members in the 24 years (1973-1996) period of classification. The cluster analysis is directly performed for the major $5\sim8$ non-correlated coefficients of the diurnal precipitation patterns obtained by factor analysis In order to consider the spatial correlation. More specifically, hierarchical clustering based on Euclidean distance and Ward's method of agglomeration is applied. The relative variance explained by the clustering is as high as average (63%) with better capability in spring (66%) and winter (69 %), but lower than average in autumn (60%) and summer (59%). Through applying weighted relative variances, i.e. dividing the squared deviations by the cluster averages, we obtain even better values, i.e 78 % in average, compared to the same index without clustering. This means that the highest variance remains in the clusters with more precipitation. Besides all statistics necessary for the validation of the final classification, 4 cluster centers are mapped for each season to illustrate the range of typical extremities, paired according to their area mean precipitation or negative pattern correlation. Possible alternatives of the performed classification and reasons for their rejection are also discussed with inclusion of a wide spectrum of recommended applications.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.265-271
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• 2020

Automotive radar that is one of the most important equipment in high-tech vehicles, is commonly used to detect the speed and range of objects such as cars. In this paper, in addition to objects detection, a method of retrieving precipitation information using the automotive radar data is proposed. The proposed method is based on the fact that the degree of attenuation of the returned radar signal differs depending on the precipitation intensity and the assumption that the distribution of precipitation is constant in short spatial and temporal observation. The purpose of this paper is to assesses the possibility of retrieving precipitation information using a vehicle radar. To verify the feasibility of the proposed method during actual driving, a method of estimating precipitation information for each time segment of various precipitation events was applied. From the results of driving field experiments, it was found that the proposed method is suitable for estimating precipitation information in various rainfall types.

• Atmosphere
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• v.27 no.1
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• pp.105-118
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• 2017

This study assesses the performance of the Weather Research and Forecasting (WRF) model in reproducing regional climate over CORDEX-East Asia Phase 2 domain with different cumulus parameterization schemes [Kain-Fritch (KF), Betts-Miller-Janjic (BM), and Grell-Devenyi-Ensemble (GD)]. The model is integrated for 27 months from January 1979 to March 1981 and the initial and boundary conditions are derived from European Centre for Medium-Range Weather Forecast Interim Reanalysis (ERA-Interim). The WRF model reasonably reproduces the temperature and precipitation characteristics over East Asia, but the regional scale responses are very sensitive to cumulus parameterization schemes. In terms of mean bias, WRF model with BM scheme shows the best performance in terms of summer/winter mean precipitation as well as summer mean temperature throughout the North East Asia. In contrast, the seasonal mean precipitation is generally overestimated (underestimated) by KF (GD) scheme. In addition, the seasonal variation of the temperature and precipitation is well simulated by WRF model, but with an overestimation in summer precipitation derived from KF experiment and with an underestimation in wet season precipitation from BM and GD schemes. Also, the frequency distribution of daily precipitation derived from KF and BM experiments (GD experiment) is well reproduced, except for the overestimation (underestimation) in the intensity range above (less) then $2.5mm\;d^{-1}$. In the case of the amount of daily precipitation, all experiments tend to underestimate (overestimate) the amount of daily precipitation in the low-intensity range < $4mm\;d^{-1}$ (high-intensity range > $12mm\;d^{-1}$). This type of error is largest in the KF experiment.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.750-760
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• 2011

In this study, the reproducibility of the simulated current climate by using two regional climate models, such as Seoul National University Regional Climate Model (SNURCM) and Weather Resuearch and Forecasting (WRF), is evaluated in advance to produce the standard regional climate scenario of future climate. Within the evaluation framework of a COordinated Regional climate Downscaling EXperiment (CORDEX), 28-year-long (1978-2005) regional climate simulation was conducted by using the Hadley Centre Global Environmental Model (HadGEM2-AO) global simulation data of the National Institute of Meteorological Research (NIMR) as a lateral boundary forcing. The simulated annual surface temperatures were in good agreement with the observation; the spatial correlation coefficients between each model and observation were over 0.98. The cold bias, however, were shown over the northern boundary in the both simulated results. In evaluation of the simulated precipitation, the skill was reasonable and good. The spatial correlation coefficients for the precipitation over the land area were 0.85 and 0.79 in SNURCM and WRF, respectively. It is noted that two regional climate models (RCMs) have different characteristics for the distribution of precipitation over equatorial and midlatitude areas. SNURCM shows better distribution of the simulated precipitation associated with the East Asia summer monsoon in the mid-latitude areas, but WRF shows better in the equatorial areas in comparison to each other. The simulated precipitation is overestimated in summer season (JJA) rather than in spring season (MAM), whereas the spatial distribution of the precipitation in spring season corresponds to the observation better than in summer season. Also the RCMs were capable of reproducing the annual variability of the maximum amount and its timing in July, in which the skills over the inland area were in better agreement with the observation than over the maritime area. The simulated regional climates, however, have the limitation to represent the number of days for extremely hot temperature and heavy rainfall over South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.