• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.56-61
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• 2021

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.526-534
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• 2014

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.169.2-169.2
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• 2010

The research assesses the performance of the water-to-water heat pump system installed in Cheongju water treatment plant for cooling and heating ventilation. In summer season monthly averaged COP is ranged from 3.85 to 4.56 according to the water source temperature, and the performance is increased as the raw water temperature is dropped. While, heating performance is increased for the high temperature water source, and the monthly averaged COP is changed from 2.92 to 3.82. The correlation of the water source temperature and the heat pump performance shows a linear tendency by the simple regression of average data. In heating, the COP of heat pump system linearly rises according to the water source temperature. In comparison, the COP in cooling linearly reduces as the raw water temperature is raised. The goodness of fit at the simple regression shows the coefficient of determination 82% in cooling, 46% in heating. The electric cost of water-to-water heat pump is reduced by 40% compared to that of air source heat pump.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.2
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• pp.314-324
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• 1997

The purpose of this study was to evaluate the thermoregulatory response level on the heat and cold tolerance with investigating monthly changes of skin temperature in Koreans and to obtain the basal information for standard amount of clothing weight, indoor climate and working condition. Forty eight subjects in 5 age groups (6~11, 12~19, 20~44, 45~64, 65~76 years old) with both sexs were taken from Seoul and Kyunggi probince. All subjects were measured skin temperature, mean skin temperature, Total clothing weight in neutral condition in each month from June 1994 to May 1995. The results obtained are summarized as follows: 1. Skin temperature of all subjects was low on February, March and April, and was high on June, July and August. Temperatures of the torso (forehead and abdomen) and the lower limbs (leg and foot) were remarkably different. In general, most of skin temperatures except of hand, thigh and foot were higher in males. 2. Mean skin temperature was 0.5'c higher in males than female with ranging 32.5~34.5$^{\circ}C$ in males and 32.1~34.1$^{\circ}C$ in females. Also, mean skin temperature of 6~11 age group were higher than that of 45~ 64 age group in both sexs.

• The monthly packaging world
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• s.277
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• pp.85-89
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• 2016

• Journal of Korean Society on Water Environment
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• v.35 no.4
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• pp.308-315
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• 2019

The objective of this study was to investigate mountain stream water and air temperatures, area, latitude, altitude, and forest coverage in headwater catchments located in Kangwon-do, Mid-eastern Korea from 2015 to 2017. Daily mean value of mountain stream water temperature was approximately $6^{\circ}C$ lower than the daily mean value of air temperature on the monitoring sites during the observation period. Monthly mean value of mountain stream water temperature increased with increasing monthly mean value of air temperature from May to August during the observation period. Seasonal variations of mountain stream water temperature were dependent on air temperature rising and falling periods. Correlation analysis was conducted on mountain stream water temperature to investigate its relationship with air temperature, area, latitude, altitude, and forest coverage of air temperature rising and falling periods. The correlation analysis showed that there exists a relationship (Correlation coefficient: -0.581 ~ 0.825; p<0.05), particularly the air temperature showed highest correlation with mountain stream water temperature. Regression equations could be developed due to contribution of air temperature to affect mountain stream water temperature (Correlation coefficient: 0.742 and 0.825; p<0.01). Therefore, a method using various parameters based on air temperature rising and falling periods, could be recommended for predicting mountain stream water temperature.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.244-254
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• 2018

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard ($50{\mu}g/m^3$) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60-80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average $PM_{2.5}$ concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.

• Journal of Environmental Impact Assessment
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• v.15 no.3
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• pp.207-215
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• 2006

The purpose of this paper is to analyze the spatial distribution and change of the frequency of Yellow Sand days and to examine their relationship with atmospheric circular characteristics at the surroundings of the Korean peninsula. Yellow Sand days data are used by intensity, Siberian High Index and monthly mean temperature of the Northern Hemisphere. In the Middle-western region, the occurrence frequency of Yellow Sand days was higher during the study period (1973-2004). Also, the occurrence frequency of Yellow Sand days increased to latter half 16 years compared with the first half 16 years, and be clearer in Middlewest regions. Yellow Sand days frequency increased, and the trend was distinct in the Jungbu region during the study period. Increasing trend of Yellow Sand days frequency was significant for the recent 22 years. Yellow Sand days had a negative relationship with Siberian High Index in February and March. Therefore, Siberian High Index became weaker in the spring, and possibility for the occurrence of Yellow Sand days was generating larger. Yellow Sand days had a positive relationship in monthly mean temperature of the Northern Hemisphere. Especially, the case of the strong Yellow Sand days is significant. Recently, global warming might be affecting the occurrence of strong Yellow Sand days.

• Fisheries and Aquatic Sciences
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• v.10 no.3
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• pp.133-142
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• 2007

To investigate conditions for the survival and growth of Dungeness crab zoeae in situ, spatial and monthly changes of sea surface temperature (SST), sea surface salinity (SSS), Chlorophyll ${\alpha}$ (Chl ${\alpha}$), and zooplankton biomass were measured in four transects: upper Chatham, Icy Strait, Cross Sound, and Icy Point in southeastern Alaska from May to September, 1997-2004. Monthly mean SST was coldest in May, increased throughout the summer months, and decreased in September. SST was coldest in the Cross Sound transect, intermediate in the upper Chatham and Icy Strait transects, and warmest in the Icy Point transect. SSS of northern stations in the upper Chatham and Icy Strait transects decreased throughout the summer months and increased in September, while that of other transects did not vary. Monthly mean Chl ${\alpha}$ was highest in May and decreased thereafter. Chl ${\alpha}$ in the upper Chatham and Icy Strait transects were relatively higher from May through September than those in the Cross Sound and Icy Point transects. Mean zooplankton biomass was highest in the Icy Strait transect in May and lowest in the Icy Point transect in September. This research suggests that oceanographic conditions during the season of Dungeness crab zoeae in southeastern Alaska may not constrain the survival and growth of Dungeness crab zoeae.