• Journal of the Korean earth science society
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• v.32 no.7
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• pp.770-783
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• 2011

In this study, we performed a downscaling of an ECHAM5 simulated dataset for the current and future climate produced under the Special Report on Emission Scenarios A1B (SRES A1B) by utilizing the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). The current climate simulation was performed for the period 1980-2000 and the future climate run for the period 2040-2070 for the COordinated Regional climate Downscaling EXperiment (CORDEX)'s East Asia domain. The RSM is properly able to reproduce the climatological fields from the evaluation of the current climate simulation. Future climatological precipitation during the summer season is increased over the tropical Oceans, the maritime-continent, and Japan. In winter, on the other hand, precipitation is increased over the tropical Indian Ocean, the maritime-continents and the Western North Pacific, and decreased over the eastern tropical Indian Ocean. For the East Asia region few significant changes are detected in the precipitation climatological field. However, summer rainfall shows increasing trend after 2050 over the region. The future climate ground temperature shows a clear increasing trend in comparison with the current climate. In response to global warming, atmospheric warming is clearly detected, which strengthens the upper level trough.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.1
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• pp.16-24
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• 1998

In this study, Ocean General Circulation Model (OGCM) has been developed as a counterpart of Atmospheric General Circulation (AGCM) for the study of coupled ocean-atmosphere climate system. The oceanic responses to given atmospheric boundary conditions have been investigated using the OGCM. In an integration carried out over 100 simulated years with climatological monthly mean data (EXP 1), most parts of the model reached a quasi-equilibrium climate reproducing many of the observed large-scale oceanic features remarkably well. Some observed narrow currents, however, such as North Equatorial Counter Current, were inevitably distorted due to the model's relatively coarse resolution. The seasonal changes in sea ice cover over the southern oceans around Antarctica were also simulated. In an experiment (EXP 2) under boundary condition of 10-year monthly data (1982-1991) from NCEP/NCAR Reanalysis Project model properly reproduced major oceanic changes during the period, including El Ni$\tilde{n}$os of 1982-1983 and 1986-87. During the ENSO periods, the experiment showed eastward expansion of warm surface waters and a negative vertical velocity anomalies along' the equator in response to expansion of westerly current velocity anomalies as westerly wind anomalies propagated eastward. Simulated anomalous distribution and the time behavior in response to El Ni$\tilde{n}$o events is consistent with that of the observations. These experiments showed that the model has an ability to reproduce major mean and anomalous oceanic features and can be effectively used for the study of ocean-atmosphere coupling system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.16 no.2
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• pp.75-82
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• 2004

The changes of sea bottom configuration, which may cause the coastal disasters, have been considered as social problems. It is obvious that the beach deformation is attributable to the sediment transport associated with erosion and acceration. The prediction method and countermeasures for them, however, are not on the level of satisfaction, which indicates that efforts should be made on developing them. In this study, it is found at the groin constructed in Sangju beach on e purpose of beach protection did the aversive function. The reason for this was judged that they accelerated the speed of erosion by increasing the velocity wave-induced current rather than brought storage effect of sediment. Authors found that the storage sediment estimation model by Sonu and Beek(1971) is a useful model at the Sangju beach with the use of topographical survey data from July, 1987 to March, 2003.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.4
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• pp.331-339
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• 1996

It is most important to understand the behaviour of fish in case of the betterment of the current fishing gear and methods or the development of the conditioning by acoustic sound in marine ranching. This investigation has been attempted to provide for the prediction of the response action of fish to acoustic sound. The experimental fish was conditioned with sound and bait. As the acoustic sound for stimulus, the pure tone of sine waveform at the frequency of 200Hz was used. This pure tone was determined from previous investigation about hearing ability of walleye pollock Theragra chalcogramma. The fork length of walleye pollock used in this experiment was 385~450mm. The conditioning proceeding was recorded in the video tape recorder. Frequency of appearance in the feeding area was analyzed with computer and video tape recorder. The position of fish was tracked using the mouse cursor and picture mixed on the superimpose board. The response of conditioned fish to sound stimulus was appeared in the 8th day firstly. The conditioned fish remembered the stimulus sound for 4 days. Average frequency of appearance in the feeding area during the 30 seconds sound projection or 1 minute after the sound stimulus was 51%, and was higher than before it.

• Ocean and Polar Research
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• v.31 no.1
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• pp.11-29
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• 2009

Variability of sea surface temperature (SST) in the Japan/East Sea (JES) was studied using complex empirical orthogonal function (CEOF) analysis. Two daily data sets were analyzed: (1) New Generation 0.05o-gridded SST from Tohoku University, Japan (July 2002-July 2006), and (2) 0.25o-gridded SST from the Japan Meteorological Agency (October 1993-November 2006). Linkages with wind stress curl were revealed using 6-h 1o-gridded surface zonal and meridional winds from ancillary data of the Sea- WiFS Project, a special National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) product (1998-2005). SST anomalies (SSTA) were obtained by removing the seasonal signal, estimated as the leading mode of the CEOF decomposition of the original SST. Leading CEOF modes of residual SSTA obtained from both data sets were consistent with each other and were characterized by annual, semiannual, and quasi-biennial time scales estimated with 95% statistical significance. The Semiannual Mode lagged 2 months behind the increased occurrence of the anticyclonic (AC) wind stress curl over the JES. Links to dynamic processes were investigated by numerical simulations using an oceanic model. The suggested dynamic forcings of SSTA are the inflow of subtropical water into the JES through the Korea Strait, divergence in the surface layer induced by Ekman suction, meridional shifts of the Subarctic Front in the western JES, AC eddy formation, and wind-driven strengthening/weakening of large-scale currents. Events of west-east SSTA movement were identified in July-September. The SSTA moved from the northeastern JES towards the continental coast along the path of the westward branch of the Tsushima Current at a speed consistent with the advective scale.

• Atmosphere
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• v.31 no.3
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.