• Atmosphere
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• v.29 no.4
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• pp.391-401
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• 2019

This study first investigates the changes of the mean and extreme temperatures and precipitation in East Asia (EA) under stabilized 1.5℃ and 2℃ warming conditions above preindustrial levels provided by HAPPI project. Here, five model with 925 members for 10-year historical period (2006~2015) and 1.5/2.0℃ future warming scenarios (2091~2100) have been used and monthly based data have been analyzed. The results show that the spatial distribution fields over EA and domain averaged variables in HAPPI 1.5/2.0℃ hindcast simulations are comparable to observations. It is found that the magnitude of mean temperature warming in EA and Korea is similar to the global mean, but for extreme temperatures local higher warming trend for minimum temperature is significant. In terms of precipitation, most subregion in EA will see more increased precipitation under 1.5/2.0℃ warming compared to the global mean. These attribute for probability density function of analyzed variables to get wider with increasing mean values in 1.5/2.0℃ warming conditions. As the result of vulnerability of 0.5℃ additional warming from 1.5 to 2.0℃, 0.5℃ additional warming contributes to the increases in extreme events and especially the impact over South Korea is slightly larger than EA. Therefore, limiting global warming by 0.5℃ can help avoid the increases in extreme temperature and precipitation events in terms of intensity and frequency.

• Korean Journal of Environmental Agriculture
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• v.14 no.2
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• pp.155-162
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• 1995

To find out the relationship between duration of sunshine and mean air temperature, monthly climatic data were analyzed in several locations in Korea. Even though mean air temperature was high in summer, duration of sunshine was shorter than winter in Kangneung. Net radiation showed a positive correlation with duration of sunshine and its regression coefficient was the highest in July. An increasing rate of sensible heat flux according to the increment of sunshine hours was significantly high in April and October, but was low in July. In spring and fall, duration of sunshine was positively correlated with the daily temperature difference, but in summer and winter it was negatively correlated with maximum temperature and with the minimum temperature, respectively. In January, one hour increase in sunshine hour lowered the mean air temperature by 1 to $1.7^{\circ}C$.

• Proceedings of the KSRS Conference
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• v.1
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• pp.352-355
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• 2006

In this study, the seasonal and inter-annual variation of sea surface current in the Gulf of Thailand were revealed through the use of WOD temperature and salinity data and monthly sea surface dynamic heights (SSDH) from TOPEX/Poseidon and ERS-2 altimetry data during 1995-2001. The mean dynamic height and mean geostrohic current were derived from the climatological data while SSDH data gave monthly dynamic heights and their geopstrophic currents. The mean geostrophic current showed strong southward and westward flow of South China Sea water along the gulf entrance. Counterclockwise eddy in the inner gulf and the western side of the gulf entrance associated with upwelling in the area. Seasonal geostrophic currents show basin-wide counterclockwise circulation during the southwest monsoon season and clockwise circulation during the northeast monsoon season. Upwelling was enhanced during the southwest monsoon season. The circulation patterns varied seasonally and inter-annually probably due to the variation in wind regime. And finally we found that congregation, spawning, and migration routes of short-bodied mackerel conform well with coastal upwelling and surface circulation in the gulf.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.6
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• pp.997-1004
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• 1997

The effect of the oceanic condition on variations of the catches of Alaska pollack (Theragra chalcogramma) in the East Sea (the Japan Sea) is examined using monthly catches data of this fish and temperature data during 1972 to 1991. Since 1973 the catches of Alaska pollack have gradually increased, showing a peak in 1981, and then rapidly decreased after 1986. A significant negative correlation was found between variations of the catches and the temperature at 50 m depth offshore Mukho. In 1981, the year of the highest catches in the study period, the water mass in the Eastern Korean Coastal Sea of the East Sea was extremely cold, while the year of poor catch, 1979, was much warmer than the annual mean temperature. The results show that the temperature variations around the Eastern Korean Coastal Sea play an important role in the variations of the catches of Alaska pollack, implying that the effect of the Tsushima Warm Current is also very important.

• Journal of Environmental Science International
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• v.7 no.2
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• pp.191-194
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• 1998

In this research, two stochastic models are considered to detect and estimate the effect of air temperature change due to Industrialization In Ulsan area. Using the monthly mean minimum air temperature anomalies, the data are divided Into pre-Industrialization part and Industrialization one for analysis. The ARM(autoregressive moving-average) model and intervention model have been applied to the data for the analysis. The results show that the variability of minimum temperature anomalies are very significant In 1989, and also significant In 1971 when the Industrialization have started. Therefore, It Is stochastically possible to estimate the time when the affection of Increase of the temperature concerning Industrialization to climate change In Usm area has happened.

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

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.2
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• pp.113-119
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• 1998

Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

• Journal of Environmental Science International
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• v.13 no.5
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• pp.469-478
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• 2004

Atmospheric bulk (wet and dry) samples were monthly collected in an urban environment (Daeyeon-dong) of Busan over a year, to assess the deposition flux and seasonality of dioxin-like polychlorinated biphenyls (DLPCBs) using stainless steel pots. Deposition fluxes of DLPCBs in bulk samples were determined using high resolution gas chromatography coupled to high resolution mass spectrometry (HRGC/HRMS). Particle deposition fluxes in the urban environment varied from 23 to 98 $mg^2$/year (mean 41 $gm^2$/year). DLPCB deposition fluxes in atmospheric bulk samples ranged from 0.09 to 0.77 ng-$TEQ/m^2$/year (mean 0.35 ng-$TEQ/m^2$/year). Seasonal atmospheric deposition fluxes of DLPCBs were high in winter and low in summer. Atmospheric deposition fluxes of particles and DLPCBs in this study were comparable to or slightly lower values than those of different locations in the world. Monthly DLPCB profiles in deposition bulk samples were similar over a year. Non-ortho PCBs were higher contributions to the total DLPCBs fluxes than mono-ortho PCBs. In particular, PCB 126 had the highest concentrartion (>75%) in all deposition samples, followed by PCB 169 and PCB 156. A highly positive correlation was found among the deposition fluxes of DLPCB species, suggesting the possibility of that the DLPCB contamination originated from one source. The deposition fluxes of DLPCBs were not significantly correlated with temperature and the amount of precipitation even though the summer season with the highest temperature and the largest amount of precipitation showed the lowest DLPCB deposition flux.

• Journal of the Korean earth science society
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• v.28 no.1
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• pp.35-44
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• 2007

This paper investigated the relationship between El $Ni\widetilde{n}o-Southern$ Oscillation (ENSO) and Indian Ocean Dipole (IOD) mode events and the impacts of these two phenomena on the climate, temperature and precipitation, of the Korean Peninsula. Data gathered from 1954 to 2004 were used for analysis, which included NINO 3 index, IOD index, and monthly mean precipitation and temperature at eleven locations in Korea. Statistical results showed that the IOD and ENSO were significantly correlated in Spring and Fall. It was clearly shown that the distribution of the sea surface temperature in the Indian Ocean has seen the Southern and Northern Oscillation in El $Ni\widetilde{n}o$ year, and Eastern and Western in IOD year. On the other hand, in El $Ni\widetilde{n}o$ you, the mean temperature of the Korea Peninsula was lower than normal in Summer and higher in Winter and its precipitation was more than normal in both Summer and Winter. However, significant correlation was not found in IOD year. In addition, the global atmospheric circulations during the major IOD years are less influential, unlike those of El $Ni\widetilde{n}o$ events.