• Journal of Forest and Environmental Science
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• v.34 no.3
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• pp.249-252
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• 2018

Climate is one of most important environmental factors on the forest ecosystem. This study was conducted to analyze the characteristics of meteorological factors in the forest area and weather stations from July 2015 to June 2016 in Cheuncheon and Hongcheon of Kangwon Province in Korea. The HOBO data logger was installed for meteorological analysis in forests area (site 1 and site 2). The meteorological data from the HOBO data logger compared with meteorological data of the weather station. The meteorological data used for the analysis was monthly mean temperature ($^{\circ}C$), monthly mean minimum temperature ($^{\circ}C$), monthly mean maximum average temperature ($^{\circ}C$), and monthly mean relative humidity (%). As a result of this study, the mean temperature ($^{\circ}C$) of forest area was relatively lower than weather station which is the outside the forest area, and the mean maximum temperature ($^{\circ}C$) of weather station was relatively higher than that of forest area. The mean relative humidity (%) was higher in forest area than weather station.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.3
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• pp.223-232
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• 2014

Monthly variations of radiative forcing (RF) and mean temperature changes by greenhouse gases emitted from commercial aircraft were estimated based on the simplified expression at four international airports (Incheon, Gimpo, Jeju, and Gimhae Airports) during the years of 2009~2010. The highest RF and mean temperature change in the study area occurred at Incheon Airport, whereas the lowest RF and mean temperature change at Gimhae Airport. During 2009~2010, the mean RF and mean temperature change estimated from aircraft $CO_2$ emissions at Incheon Airport were approximately 30.0 $mW/m^2$ and $0.022^{\circ}K$, respectively. The mean RF and mean temperature changes caused by other greenhouse gas $N_2O$ was significantly small (<<0.1 $mW/m^2$ and << $1{\times}10^{-3}^{\circ}K$). Meanwhile, $CH_4$ emissions caused negative mean RF ($-4.45{\times}10^{-3}mW/m^2$ at Incheon Airport) and the decrease of mean temperature ($-3.83{\times}10^{-6}^{\circ}K$) due to consumption of atmospheric $CH_4$ in the aircraft engine.

• Atmosphere
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• v.19 no.2
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• pp.107-125
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• 2009

Statistical downscaled surface temperature datasets by employing the cyclostationary empirical orthogonal function (CSEOF) analysis and multiple linear regression method are examined. For evaluating the efficiency of this statistical downscaling method, monthly surface temperature of the ECMWF has been downscaled into monthly temperature having a fine spatial scale of ~20km over the Korean peninsula for the 1973-2000 period. Monthly surface temperature of the ECHOG has also been downscaled into the same spatial scale data for the same period. Comparisons of temperatures between two datasets over the Korean peninsula show that annual mean temperature of the ECMWF is about $2^{\circ}C$ higher than that of the ECHOG. After applying to the statistical downscaling method, the difference of two annual mean temperatures reduces less than $1^{\circ}C$ and their spatial patterns become even close to each other. Future downscaled data shows that annual temperatures in the A1B scenario will increase by $3.5^{\circ}C$ by the late 21st century. The downscaled data are influenced by the ECHOG as well as observation data which includes effects of complicated topography and the heat island.

• 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.

• 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.

• 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.

• Korean Journal of Environmental Biology
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• v.18 no.1
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• pp.21-31
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• 2000

We investigated seasonal variation of microalgal assemblages, sea water temperature, salinity and suspended solid and the parameters measured daily from January 1998 to October 1999 at a nearshore shallow-water in Marian Cove, Maxwell Bay, King George Island, the Antarctic. Annual mean surface water temperature was -0.3$0^{\circ}C$ and the highest water temperature was 4.53$^{\circ}C$ (22 January 1999) and the lowest water temperature was -2.07$^{\circ}C$ (23 August 1998). Annual mean salinity was 33.38 psu, ranging from 42.80 psu (6 January 1999) to 19.50 psu (6 June 1999). Annual mean suspended solid (SS) during two years was 34.14 mgㆍ1$^{-1}$, ranging from 60.62 mgㆍ1$^{-1}$(7 March 1998) to 12.90 mgㆍ1$^{-1}$ (26 December 1998). Chlorophyll $\alpha$ (Chl $\alpha$) concentrations were measured in order to know seasonal variations of microalgae in the surface seawater. Annual mean of total Chl a concentration was 0.55$\mu\textrm{g}$ㆍ1$^{-1}$, the highest Chl $\alpha$ concentration (12.16$\mu\textrm{g}$ㆍ1$^{-1}$) appeared in 4 October 1998, the lowest Chl $\alpha$ concentration appeared 0.19$\mu\textrm{g}$ㆍ1$^{-1}$, Monthly mean total Chl $\alpha$ concentration was high in October 1998 (1.32$\mu\textrm{g}$ㆍ1$^{-1}$) and low in July on 1998 (0.28$\mu\textrm{g}$ㆍ1$^{-1}$). Annual mean nano-sized Chl $\alpha$ concentration was 0.40$\mu\textrm{g}$ㆍ1$^{-1}$, monthly mean nano -sized Chl $\alpha$ concentration was high in November 1998 (0.90$\mu\textrm{g}$ㆍ1$^{-1}$), and low in July 1999 (0.22$\mu\textrm{g}$ㆍ1$^{-1}$). Annual mean micro-sized Chl $\alpha$ concentration was 0.15$\mu\textrm{g}$ㆍ1$^{-1}$ monthly mean micro-sized Chl $\alpha$ concentration was high in October 1998 (0.81$\mu\textrm{g}$ㆍ1$^{-1}$), and low July 1998, January, February and September 1999 (0.05$\mu\textrm{g}$ㆍ1$^{-1}$). More than 65% of total Chl $\alpha$ was concentrated during spring and summer time between October and March. Microalgal variation appeared to be due to physical factors of seawater in the Antarctic nearshore from 1998 to 1999. The reason why micro-sized Chl $\alpha$ did not increase during austral summer was the bay had been frozen by decrease of water temperature. We think that total microalgal abundance was decreased because the summer microalgal abundance was determined by variation of water temperature during winter season. [Chl $\alpha$ concentration, Microalgal assembalges, Seasonal variation, the Antarctic nearshore].