• Fisheries and Aquatic Sciences
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• v.15 no.4
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• pp.345-352
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• 2012

To predict catches of Pacific anchovy Engraulis japonicus larvae, anchovy eggs were collected in the coastal waters off Gunsan, Korea, in the Yellow Sea during the main spawning season (June to July) from 2003 to 2009. A ring net was repeatedly towed vertically at 10 stations during the daytime to sample eggs. Catch data estimated by auction sales were obtained from the Fisheries Cooperatives Union of Gunsan City and daily water temperature data in the outer harbor of Gunsan City during the survey periods were obtained from the National Oceanographic Research Institute. A significant relationship was found between anchovy egg density from June to July and larval catch from July to October in the same year. Catch of anchovy larvae in Gunsan were also high when optimal growth temperatures were recorded in the coastal waters off Gunsan in July. Although the recruitment success or failure of anchovy larvae can be predicted from variability in egg density, we suggest that mean daily water temperature is a more efficient indicator for predicting variability in catches of larval anchovy in the Yellow Sea.

• Animal Bioscience
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• v.37 no.6
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• pp.1121-1129
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• 2024

Objective: Objective of the study was to reduce heat stress in Murrah buffaloes and maintain their milk production and other vital functions during heat stress. Methods: A total of 21 dyads of calf-mother Murrah buffalo were selected for the study and equally divided in 3 treatment groups. First treatment group was restricted calf contact (RCC), second treatment group was fence line calf contact (FCC) and third treatment groups fence line calf contact and heat stress protection (FCC-HSP [time-controlled fan-fogger system] in the shed). Present study was conducted from April to mid-September 2021. Results: Maximum temperature and temperature humidity index in FCC-HSP shed were significantly (p<0.05) lower than that in FCC and RCC shed. Higher (p<0.05) mean daily milk yield in both the treatment groups FCC (10.36±0.30) and FCC-HSP (10.97±0.31) than RCC (8.29±0.41) was recorded. Though no significant difference between FCC and FCC-HSP in daily milk yield but FCC-HSP yielded 600 gm more milk than FCC. Pulse rate (PR) and respiration rate (RR) were lowest in FCC-HSP followed by FCC and RCC, respectively. Cortisol and prolactin levels were lower (p<0.05) in FCC-HSP followed by FCC and RCC, respectively. Conclusion: Hence, FCC along with heat stress ameliorative measures helped the buffaloes to be free of stress and maintain milk yield during heat stress period of the year in tropical conditions.

• Journal of Environmental Health Sciences
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• v.32 no.4 s.91
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• pp.304-315
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• 2006

This study is performed to reexamine the association between ambient air pollution and daily mortality in seven major cities of Korea using a method of meta-analysis with the data filed for the period 1998-2001. These cities account for half of the Korean population (about 23 million). The observed concentrations of carbon monoxide (CO, mean=1.08 ppm), ozone ($O_3$, mean=33.97 ppb), particulate matter less than 10 ${\mu}m$ ($PM_{10},\;mean=57.11\;{\mu}g/m^3$), nitrogen dioxide ($NO_2$, mean=25.09 ppb), and sulfur dioxide ($SO_2$, mean=9.14 ppb) during the study period were at levels below Korea's current ambient air quality standards. Generalized additive models were applied to allow for the highly flexible fitting of seasonal and long-term time trends in air pollution as well as nonlinear associations with weather variables, such as air temperature and relative humidity. Also, we calculated a weighted mean as a meta-analysis summary of the estimates and its standard error. In city-specific analyses, an increase of $41.17{\mu}g/m^3(IQR)\;of\;PM_{10}$ corresponded to $1{\sim}12%$ more deaths, given constant weather conditions. Like most of air pollution epidemiologic studies, this meta-analysis cannot avoid fleeing from measurement misclassification since no personal measurement was taken. However, we can expect that a measurement bias be reduced in district-specific estimate since a monitoring station is better representative of air quality of the matched district. Significant heterogeneity was found for the effect of all pollutants. The estimated relative risks from meta-like analysis increased compared to those relative risks from pooled analysis. The similar results to those from the previous studies indicated existence of health effect of air pollution at current levels in many industrialized countries, including Korea.

• 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 the Korean Solar Energy Society
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• v.29 no.6
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• pp.81-87
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• 2009

The purpose of this study is to evaluate of the outdoor thermal environment by building scale and block type as variable factors. In this study, 18 cases of office in central business district that have different condition are compared about their surface temperature, HIP(Heat Island Potential), and MRT(Mean Radiant Temperature). They are simulated with 3-dimension numerical simulation software named Hoyano-model. The output results contain visualized distribution chart and numerical data. The results of evaluation are as follows. (1)The surface temperature of the building becomes higher as building coverage ratio is higher but floor area ratio is lower. In same conditions, unified block type is maximum $3.2^{\circ}C$ higher than divided block type. (2)HIP shows different daily pattern as block type. During daytime, divided block type is much higher than unified block type but after sunset, it is changed. (3)MRT shows different distribution pattern as sunlight moves expecially at noon. (4)As the results of this study, cases that have high floor area ratio condition show lower surface temperature by tendency to stay low indoor temperature in office building and big rate of windows on building surface.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.28-34
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• 2011

This study was carried out to estimate monthly mean of daily maximum and minimum temperature across North Korea at a 30 m grid spacing for a climatological normal year (1971-2000) and the 4 decadal averages (1971-1980, 1981-1990, 1991-2000, and 2001-2010). A geospatial climate interpolation method, which has been successfully used to produce the so-called 'High-Definition Digital Climate Maps' (HD-DCM), was used in conjunction with the 27 North Korean and 17 South Korean synoptic data. Correction modules including local effects of cold air drainage, thermal belt, ocean, solar irradiance and urban heat island were applied to adjust the synoptic temperature data in addition to the lapse rate correction. According to the final temperature estimates for a normal year, North Korean winter is expected colder than South Korean winter by $7^{\circ}C$ in average, while the spatial mean summer temperature is lower by $3^{\circ}C$ than that for South Korea. Warming trend in North Korea for the recent 40 years (1971-2010) was most remarkable in spring and fall, showing a 7.4% increase in the land area with 15 or higher daily maximum temperature for April.

• Journal of the Korean Society of Tobacco Science
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• v.26 no.2 s.52
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• pp.85-92
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• 2004

This study was conducted to investigate the relationship between weather factors during the growing season and chemical components of burley tobacco. Chemical components used in this study was from 'Farm Leaf Tobacco Test' conducted at KT&G Central Research Institute from 1987 through 2002. Data of weather factors during growing season(April to July) were collected in 6 districts measured at Korea Meteorological Adminstration(KMA). Total nitrogen content was increased from 1987 through 2002. Year to year variation of rainfall was the largest, followed by that of sunshine hour. Month to month variation of rainfall also was the largest, followed by that of mean daily air temperature. A negative correlation was found between rainfall and sunshine hour. Relative humidity(R.H.) was correlated positively with rainfall, whereas negatively with sunshine hour. The negative correlations were found between nicotine content and rainfall(in June, May$\~$June, June$\~$July, May$\~$July and average), and R.H.(in June, May$\~$June, June$\~$July, May$\~$July and average), respectively. The negative correlations were found between crude ash content and rainfall(in June and May$\~$June), and R.H.(in June, May$\~$June, June$\~$July and May$\~$July), respectively. Ether extraction content was correlated positively with mean daily air temperature(in July, June $\~$July and May$\~$July) and with sunshine hour(in July, June$\~$July and May$\~$July), but negatively with rainfall(average) and with R.H.(in April, July, June$\~$July, April$\~$June, May­July and average), respectively. Chloride content was correlated positively with sunshine hour(in May, April$\~$May, May$\~$June, April$\~$June, May$\~$July and average), but negatively with rainfall(in June, May$\~$June, June$\~$July, April$\~$June, May$\~$July and average).

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.88-95
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• 1994

This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration. Two neural networks were developed to forecast daily evapotranspiration of the rice crop with back-propagation and counter-propagation algorithm. The neural network trained by back-propagation algorithm with delta learning rule is a three-layer network with input, hidden, and output layers. The other network with counter-propagation algorithm is a four-layer network with input, normalizing, competitive, and output layers. Training neural networks was conducted using daily actual evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity, and pan evaporation. During the training, neural network parameters were calibrated. The trained networks were applied to a set of field data not used in the training. The created response of the back-propagation network was in good agreement with desired values and showed better performances than the counter-propagation network did. Evaluating the neural network performance indicates that the back-propagation neural network may be applied to the estimation of evapotranspiration of the rice crop. This study does not provide with a conclusive statement as to the ability of a neural network to evapotranspiration estimating. More detailed study is required for better understanding and evaluating the behavior of neural networks.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.