• Information and Communications Magazine
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• v.33 no.8
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• pp.52-62
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• 2016

해양에서 음파를 사용하여 수중통신을 시도할 경우 해양매질은 음향 도파관(acoustic waveguide)의 역할을 하게 되고, 이 경우 해양환경의 변동성과 그에 따른 음파와 매질의 간섭에 의해 수중통신 채널의 변동성이 발생한다. 수중음향 채널은 대역 제한 채널이면서 잔향음 제한 채널이고 강한 도플러 변이 채널이므로 수신된 통신 신호는 육상통신에 비해 심한 인접 심볼간 간섭(intersymbol interference)과 위상변이를 가지게 된다. 따라서 수중통신을 시도함에 있어 이러한 해양환경 변동성과 그에 따른 수중음향 채널 변동에 대한 충분한 고려가 필요하다. 본 논문은 수중통신 시스템 구성에 도움을 줄 수 있도록 수중통신 채널에 영향을 미치는 해양 매질의 기본적 특성에 대해 소개하고 수중통신 채널과의 상관성 및 환경 변동성에 따른 통신채널의 변동성에 대해 소개하고자 한다.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.819-832
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• 2004

Daily rainfall data from 14 stations during 1941 to 2000 were analyzed in order to examine the characteristics of the variation of summer rainfall and the identify relationship between the variation of summer rainfall and the variation of SOI(Southern Oscillation Index) and NPI(North Pacific Index), global temperature. For further investigation, study period is divided into two 30 year intervals, 1941-1970 and 1971-2000. There are the trend of increase in August and decrease in September in the later period compared with the earlier one. It was Mid-west in August where there is the largest variation. It is related to the increase of the frequency of heavy rainfall. The second period of extreme rainfall by ten days is absent, or it change from early in September to late in August. According to the result, the dry spell in August disappears and Changma is continued to early in September. Gradually, there is change from negative (or positive) to positive (or negative) to the rainfall anomaly of the mid of August and the mid of September (or July). The correlation between the variation of rainfall and oceanic variation and global temperature is statistically significant.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.137-137
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• 2010

해양 혼합층은 해양-대기 간의 상호작용을 통해서 기후 변화 뿐만 아니라, 식물성 플랑크톤 분포와 같은 생물학적 측면에도 큰 영향을 줄 수 있기 때문에 매우 중요하다. 따라서 본 연구에서는 우리나라 장단기 기후변동에 많은 영향을 주는 북동아시아 근해 내에서의 혼합층 깊이의 시공간 변동을 분석하였다. 기존에 해양 관측 자료가 절대적으로 부족했던 점을 극복하기 위해 2000년부터 전구 해양에서 실시간으로 수집되기 시작한 해양중층부이(ARGO) 자료를 활용하였다. 지금까지 제시되어 온 다양한 해양 혼합층 결정 기준 중 가장 널리 사용되고 있는 Levitus et al.(1997)의 기준을 적용하여 북동아시아 근해의 혼합층 깊이를 산출하였으며, 그 변화를 위도, 경도, 해안으로부터의 거리, 계절 등에 따라 분석하였다. 또한 계절적 변화에서 겨울철 해양 혼합층 변화의 역전이 나타나는 지역을 분석하였다. 이와 같은 분석결과는 추후 해양 혼합층 깊이 결정 방법에 대한 연구의 기초자료로 활용될 것으로 기대한다.

• Atmosphere
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• v.23 no.3
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• pp.283-291
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• 2013

This study investigates the changes in the atmosphere-ocean interactions over the South China Sea (SCS) by analyzing their variables in the period of 1979~2011 during the boreal summer (June-July-August). It is found that a simultaneous correlation coefficient between sea surface temperature (SST) and precipitation over SCS during summer is significantly changed before and after the late-1990s. That is, the variation of precipitation over SCS is negatively (positively) correlated with the SST variations before (after) the late-1990s. Our further correlation analysis indicates that the atmospheric forcing of the SST is dominant before the late-1990s accompanying with wind-evaporation feedback and cloud-radiation feedback. After the late-1990s, in contrast, the SST forcing of the atmosphere through the latent heat flux from the ocean to the atmosphere is dominant. It is found that the change in the relationship of atmosphere-ocean interactions over SCS are associated with the changes in the relationship with Northeast Asian summer precipitation. In particular, a simultaneous correlation coefficient between the precipitation over SCS and Northeast Asia becomes stronger during after the late-1990s than before the late-1990s. We argue that the increase of the SST forcing of the atmosphere over SCS may lead a direct relationship of precipitation variations between SCS and Northeast Asia after the late-1990s.

• Journal of the Korean earth science society
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• v.44 no.1
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• pp.1-12
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• 2023

Oceanic wind waves have been recognized as one of the important indicators of global warming and climate change. It is necessary to study the spatial and temporal variability of significant wave height (SWH) and wave direction in the Yellow Sea and a part of the East China Sea, which is directly affected by the East Asian monsoon and climate change. In this study, the spatial and temporal variability including seasonal and interannual variability of SWH and wave direction in the Yellow Sea and East China Sea were analyzed using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data. Prior to analyzing the variability of SWH and wave direction using the model reanalysis, the accuracy was verified through comparison with SWH and wave direction measurements from Ieodo Ocean Science Station (I-ORS). The mean SWH ranged from 0.3 to 1.6 m, and was higher in the south than in the north and higher in the center of the Yellow Sea than in the coast. The standard deviation of the SWH also showed a pattern similar to the mean. In the Yellow Sea, SWH and wave direction showed clear seasonal variability. SWH was generally highest in winter and lowest in late spring or early summer. Due to the influence of the monsoon, the wave direction propagated mainly to the south in winter and to the north in summer. The seasonal variability of SWH showed predominant interannual variability with strong variability of annual amplitudes due to the influence of typhoons in summer.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Proceedings of the Korean Quaternary Association Conference
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• 2005.10a
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• pp.40-44
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• 2005

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2001.10a
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• pp.362-363
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• 2001

명태는 한국, 일본 그리고 러시아 해역에 넓게 분포하고 있다. 그 중 일본 최대의 산란장인 홋카이도의 A지역(종곡, 유맹, 후지)어획량은 1976년부터 계속 감소하고 있다. 본 연구에서는 어획량 변동 요인을 분석하기 위해서, 일본해역에 있어서의 명태 어획량의 시공간적 변동(시공간적배동)의 특징을 검토한다. 일반적으로 어획량의 변동은 어장에 있어서의 어업형태 또는 내유(來遊)하는 계통군(系統群)의 변화, 해양환경 등에 영향을 받고 있다. (중략)

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.333-344
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• 2021

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model's RMS errors are 0.93 and 0.35~1.95, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.3
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• pp.309-325
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• 1994

Ocean circulation in the Gulf of Alaska is remarkably constant throughout the year despite of being forced by one of the largest seasonal wind stresses in the world. To explain the small seasonal changes in the transport of Alaska Stream. a set of numerical models is employed. First a diagnostic approach is applied to reproduce circulation from the observed density structure. The results reveals the very small seasonal changes in the Alaska Stream transport. Next a series of the prognostic models is used: a barotropic model. a flat bottom baroclinic model, and baroclinic model with topography. These models reveal the influence of topography and baroclinicity on the ocean's response to the seasonal wind forcing. The intercomparisons of the various model results suggest that the seasonal response of the baroclinic ocean is primary barotropic and the resultant barotropic circulation is weakened by the scattering effect of the bottom topography.