• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.1 no.1
• /
• pp.55-59
• /
• 1968

The relation of air temperature between early summer and early autumn from 1916 to 1966 was investigated. The data are brought by the statistical analysis for the purpose of the long range weather forecast. he results are summarized as follows : 1. The air temperature in early autumn at Mokpo is largely influenced by that of early summer. That is, when the air temperature in early summer is higher than the average, the temperature in early autumn has the possibility of being higher temperature in early autumn than average, the possibility being as much as $60\%$. On the contrary, when the former is lower, the latter has a possibility of becoming $74\%$ below the normal year. 2. The monthly ranges of forcastable mean air temperature in early autumn will be computed by the types of total variation in early summer and the standard deviation in early autumn.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2011.11a
• /
• pp.32-32
• /
• 2011

• Journal of the Korean Geophysical Society
• /
• v.9 no.4
• /
• pp.397-408
• /
• 2006

Due to the temporal and spatial variability of the warming at and near the Antarctic Peninsular, it is required to better understand local climate at the issued region. The purpose of the study are to characterize surface radiation, air temperature and wind direction and investigate their relations at the King Sejong Station near the Antarctic Peninsular during last three and half years. While the study site was a weak radiative energy sink (positive net radiation) with annual mean of 15-20 Wm-2, it played a role as a strong sink in summer (December to January) with mean of 85 Wm-2, a magnitude that was significantly larger than those at other surface covered with snow or ice in Antarctica. Monthly averaged air temperature ranged from -7.7-2.8oC and the variations of monthly averaged air temperature showed the distinct differences with year. Northwesterly, westerly and easterly were dominant and the variability of air temperature could be explained by the variability of the frequency of wind direction with cold easterly and warm northwesterly/northerly to some degree, which in turn influenced radiation budget through albedo in summer.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1293-1300
• /
• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• Journal of Wetlands Research
• /
• v.20 no.4
• /
• pp.338-344
• /
• 2018

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

• Journal of Climate Change Research
• /
• v.6 no.2
• /
• pp.61-72
• /
• 2015

In this study, we have investigated monthly changes in temperature extremes in South Korea for the past (1921~2010) and the future (2011~2100). We used seven stations' (Gangneung, Seoul, Incheon, Daegu, Jeonju, Busan, Mokpo) data from KMA (Korea Meteorological Administration) for the past. For the future we used the closest grid point values to observations from the RCP8.5 scenario of 1 km resolution. The Expert Team on Climate Change Detection and Indices (ETCCDI)'s climate extreme indices were employed to quantify the characteristics of temperature extremes change. Temperature extreme indices in summer have increased while those in winter have decreased in the past. The extreme indices are expected to change more rapidly in the future than in the past. The number of frost days (FD) is projected to decrease in the future, and the occurrence period will be shortened by two months at the end of the $21^{st}$ century (2071~2100) compared to the present (1981~2010). The number of hot days (HD) is projected to increase in the future, and the occurrence period is projected to lengthen by two months at the end of the $21^{st}$ century compared to the present. The annual highest temperature and its fluctuation is expected to increase. Accordingly, the heat damage is also expected to increase. The result of this study can be used as an information on damage prevention measures due to temperature extreme events.

• Proceedings of the KIEE Conference
• /
• 2009.04a
• /
• pp.174-176
• /
• 2009

This paper aims to investigate generation conditions necessary for the most efficient generation by measuring electricity power under various irradiation conditions, since the photovoltaic generation system has high costs and low efficiency. This thesis aims to investigate generation conditions necessary for the most efficient generation by measuring electricity power under various irradiation conditions, since the photovoltaic generation system has high costs and low efficiency. Although the generation power increased with the irradiation, the former did not vary directly as the latter. This meant that the variation of the generation power was concerned in the temperature of a module, the ambient temperature, and the directions of irradiation as well as the irradiation. As for the monthly accumulated irradiation and monthly accumulated power, the maximum irradiation and generation power were observed in May and October and the irradiation, the power and the accumulated generation power were all the highest in spring, followed by fall, summer and winter.

• Journal of Environmental Science International
• /
• v.5 no.6
• /
• pp.763-770
• /
• 1996

This study was performed to analyze the variation characteristics of writer qulity, correlation analysis of water quality data at each site and among the items of water Quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, 7-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature +1.23 ($R^2$: 0.7616) . Results of the correlation analysis of water quality data showed that DO had high correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, 55 did site 5 and 6, 7-N did 2 and 3, 7-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. $DO_1$=4.46+0.59 DO, ($R^2$=0.8868), $BOD_1$ = 0, 52+0.63 BOD3 ($R^2$ = 0.6390) $COD_2$ = 0.44+0.71 $COD_1$ ($R^2$ = 0.9183), SS6 = 0.89+0.7055.($R^2$ = 0.9155) $TN_3$ = 0.151 +0.886 $TN_2$ ($R^2$ = 0.9415), $TP_4$ = 0.004+5.758 $TP_6$ ($R^2$ = 0.9669)

• Atmosphere
• /
• v.18 no.1
• /
• pp.33-41
• /
• 2008

This study provides an impact assesment of climate change on energy consumption, based on active-deal scenario. This approach assumes that the amount of electric energy consumption depends on human spontaneous acts against local (REC) has ben developed by using monthly mean temperature and monthly amount of electric energy consumption in the 6 major cities over the 19-205 period. The statistical model is utilized to estimate the past and future REEC, and to assess the economic benefits and damage in energy consumption sector. For an estimation of the future REEC, climate change scenario, which is generated by National Institute of Meteorological Research, is utilized in this study. According to the model, it is estimated that over the standard period (1999~2005), there might be economic benefits of about 31 bilion Won/year in Seoul due to increasing temperature than in the 1980s. The REC is also predicted to be gradually reduced across the Korean peninsula since the 2020s. These results suggest that Korea will gain economic benefits in the REC sector during the 21st century as temperature increases under global warming scenarios.

• Atmosphere
• /
• v.25 no.4
• /
• pp.669-683
• /
• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.