• Journal of the Korean earth science society
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• v.26 no.5
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• pp.395-403
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• 2005

This study is focused on the relationship between snowfall and the Bowen’s Ratio (sensible heat flux/latent heat flux) through calculation of heat exchange between air and sea for snowfall events in Jeju Island from 1993 to 2003. The four weather stations for this study are located at Jeju, Seoguipo, Seongsanpo and Gosan in Jeju Island. In order to improve the reliability of snowfall forecast, the Bowen’s Ratio for snowfall, which includes influences from the atmosphere such as wind, is compared with the temperature difference between air and sea for snowfall. As a results, in the case for fresh snowfall, the minimum temperature differences between air and sea were 10, 12.3, 11.5, and $14.3^{\circ}C$ at Jeju, Seoguipo, Seongsanpo and Gosan, respectively. The probabilities of fresh snowfall were 26, 29, 13, and $23\%$, respectively, when the temperature differences were higher than the previous values. On the other hand, the minimum Bowen ratios were 0.59, 0.60, 0.65 and 0.65 at Jeju, Seoguipo, Seongsanpo and Gosan, respectively. The probabilities of fresh snowfall were 33, 70, 31 and $58\%$ respectively, when the Bowen ratio is higher than those. The reason for this is because the probability of fresh snowfall with the Bowen ratio was higher than the probability with temperature difference between air and sea. This result occurred because heat exchange by wind increased the probability of snowfall, along with the temperature difference between air and sea, and the Bowen ratio. Therefore, snowfall forecast of Jeju Island is significantly influenced by the sea, whereas forecast with Bowen ratio seems to have higher reliability than that with the temperature difference between air and sea. The data analysis for the ten-year period $(1993\~2002)$ showed that when each fresh snowfall was within 0.0 to 0.9cm, the average Bowen’s ratio was 0.63 to 0.67, and when each fresh snowfall was 1.0 to 4.9 cm, the average Bowen’s ratio was over 0.72. Therefore, fresh snowfall shows a proportional relationship with the Bowen’s ratio during snowfall.

• Journal of Navigation and Port Research
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• v.34 no.10
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• pp.781-786
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• 2010

In this study, a new concept of ocean transport system, called the mobile harbor serving for a short distance transport of containers with cargo handling cranes between mother containerships and coastal ports, is introduced. Instead of direct berthing a very large containership at the coastal port, Mobile Harbor is moving to the offshore mooring basin with enough water depth condition. Therefore, investigation of the coastal environment, technical condition and limitation of the domestic trade ports for the application of Mobile Harbor, is essential process. To figure out the accessibility of mobile harbor, the environmental conditions, the cargo handling capacity and marine traffic volume and flow pattern has been analyzed with the tools for marine traffic simulation and virtual navigation aids system. The most proper Mobile Harbor mooring areas among trade ports of the south and east coast are selected by analyzing the obtained information and evaluating its application: (1) Under natural environmental conditions such as air and sea weather, three candidate areas are selected such as Masan port, Ulsan port, and Busan(New port) port. (2) Under marine traffic and appropriateness of water facilities, three candidate areas are selected as Mokpo port, Busan(New port) port, and Donghae & Mookho port (3) For a region-based analysis considering handling capacity and the local managed trade ports in vicinity, three candidate areas are selected as Busan region, Yosu & KwangYang region, and Mokpo region. Through this study, the basic guideline for selection of optimum trade port and offshore mooring basin for mothership and Mobile Harbor is recommended. In order to apply the Mobile Harbor to the real water, navigaton aids as the virtual route identification with AIS must be introduced for maritime safety in the vicinity of Mobile Harbor area which berthing and cargo handling is being conducted.

• Journal of the Korean Geographical Society
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• v.28 no.2
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• pp.112-122
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• 1993

Since more than 50${\%}$ of annual precipitation in Korea falls during Changma, the rainy season of early summer, and Late Changma, the rainy season of late summer, forcasting the onset days Changmas, and the amount related rainfalls would be necessary not only for agriculture but also for flood-control. In this study the authors attempted to build a prediction model for the forecast of the onset date of Changmas. The onset data of each Changma was derived out of daily rainfall data of 47 stations for 30 years(1961~1990) and weather maps over East Asia. Each station represent any of the 47 districts of local forecast under the Korea Meteorological Administration. The average onset dates of Changma during the period was from 21 through 26 June. The dates show a tendency to be delayed in El Ni${\~{n}}o years while they come earlier than the average in La Nina years. In 1982, the year of El Ni${\~{n}}o, the date was 9 Julu, two weeks late compared with the average. The relation of sea surface temperature(SST) over Pacific and Northern hemispheric 500mb height to the Changma onset dates was analyzed for the prediction model by polynomial regression. The onset date of Changma over Korea was correlated with SST in May(SST${_(5)}{^\circ}$C) of the district (8${^\circ}$~12${^\circ}S, 136${^\circ}~148${^\circ}W)of equatirial middle Pacific and the 500mb height in March (MB${_(3)}$"\;"m)over the district of the notrhern Hudson Bay. The relation between this two elements can be expressed by the regression: Onset=5.888SST${_5}"\;"+"\;"0.047MB${_(3)}$"\;"-251.241. This equation explains 77${\%}$ of variances at the 0.01${\%}$ singificance level. The onset dates of Late Changma come in accordance with the degeneration of the Subtro-pical High over northern Pacific. They were 18 August in average for the period showing positive correlation(r=0.71) with SST in May(SST)${_(i5)}{^\circ}$C) over district of IndiaN Ocean near west coast of Australia (24${^\circ}$~32${^\circ}$S, 104${^\circ}$~112${^\circ}$E), but negativ e with SST in May(SST${_(p5)}{^\circ}$ over district (12${^\circ}$~20${^\circ}$S,"\;"136${^\circ}$~148${^\circ}$W)of equatorial mid Pacific (r=-0.70) and with the 500mb height over district of northwestern Siberia (r=-0.62). The prediction model for Late Changma can be expressed by the regression: Onset=706.314-0.080 MB-3.972SST${_(p5)}+3.896 SST${_(i5)}, which explains 64${\%}$ of variances at the 0.01${\%}$ singificance level.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.1-23
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• 2021

In response to the increase in atmospheric carbon dioxide and greenhouse gases, the global mean temperature is rising rapidly. In particular, the warming of the Arctic is two to three times faster than the rest. Associated with the rapid Arctic warming, the sea ice shows decreasing trends in all seasons. The faster Arctic warming is due to ice-albedo feedback by the presence of snow and ice in polar regions, which have higher reflectivity than the ocean, the bare land, or vegetation, higher long-wave heat loss to space than lower latitudes by lower surface temperature in the Arctic than lower latitudes, different stability of atmosphere between the Arctic and lower latitudes, where low stability leads to larger heat losses to atmosphere from surface by larger latent heat fluxes than the Arctic, where high stability, especially in winter, prohibits losing heat to atmosphere, increase in clouds and water vapor in the Arctic atmosphere that subsequently act as green house gases, and finally due to the increase in sensible heat fluxes from low latitudes to the Arctic via lower troposphere. In contrast to the rapid Arctic warming, in midlatitudes, especially in eastern Asia and eastern North America, cold air outbreaks occur more frequently and last longer in recent decades. Two pathways have been suggested to link the Arctic warming to cold air outbreaks over midlatitudes. The first is through troposphere in synoptic-scales by enhancing the Siberian high via a development of Rossby wave trains initiated from the Arctic, especially the Barents-Kara Seas. The second is via stratosphere by activating planetary waves to stratosphere and beyond, that leads to warming in the Arctic stratosphere and increase in geopotential height that subsequently weakens the polar vortex and results in cold air outbreaks in midlatitudes for several months. There exists lags between the Arctic warming and cold events in midlatitudes. Thus, understanding chain reactions from the Arctic warming to midlatitude cooling could help improve a predictability of seasonal winter weather in midlatitudes. This study reviews the results on the Arctic warming and its connection to midlatitudes and examines the trends in surface temperature and the Arctic sea ice.