• 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.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.111-119
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• 2003

In order to improve navigational safety of ships, an ocean wave prediction model of high precision within a short time, dealing with multi-directional random waves from the information of the sea surface winds encountered at the planned ship's course, was introduced for construction of ocean wave forecasting system on the ship. In this paper, we investigated a sea disaster occurred by a stormy weather in the past. We analyzed the sea surface wind first and then carried out ocean wave hindercasting simulations according to the routes the sunken vessel. From the result of this study, we concluded that the sea disaster was caused by rapidly developed iou pressure system Okhotsk Sea and the predicted values by the third generation wave prediction model(WAM) was agreed well with the observed significant wave height, wave period, and directional wave spectrum. It gives a good applicability for construction of a practical on-board calculation system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.147-156
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• 2003

In order to improve navigational safety of ships, on ocean wave prediction model of high precision within a short time, dealing with multi-directional random waves from the information of the sea surface winds encountered at the planned ship's course, was introduced for construction of ocean wave forecasting system on the ship. In this paper, we investigated a sea disaster occurred by a stormy weather in the past. We analyzed the sea surface winds first and then carried out ocean wave hindercasting simulations according to the routes of the sunken vessel. From the result of this study, we concluded that the sea disaster was caused by rapidly developed low pressure system in Okhotsk Sea and the predicted values by the third generation wave prediction model(WAM) was agreed well with the observed significant wave height, was period, and directional wave spectrum. It gives a good applicability for construction of a practical on-board calculation system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Journal of Ocean Engineering and Technology
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• v.22 no.2
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• pp.34-41
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• 2008

As a basic study for developing a forecasting/estimating system that predicts water quality changes when Deep Sea Water (DSW) drains to the ocean after using it, this study was carried out as follows: 1) numerical simulation of the present state at DSW developing region in the East sea using SWEM, 2) numerical prediction of water quality changes by effluent DSW, 3) analysis of influence degree 'With defined DEI (DSW effect index) at F station. On the whole, when DSW drained to the ocean, Chl-a, COD and water-temperature were decreased and DIN, DIP and DO were increased by effluent DSW, and Salinity was steady. According to analysis of influence degree, the influence degree of DIN was the highest and it was high in order of Chl-a, COD, Water-temperature, DO, DIP and Salinity. The influence degree classified by DSW effluent position was predicted that suiface outflow was lower than bottom outflow. Ad When DSW discharge increased 10 times, the influence degree increased about $5{\sim}14$ times.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.268-273
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• 1998

This study applies an on-line system, which employes an adaptive reconstruction technique to monitor and forecast ocean surface changes. The system adaptively generates an appropriate synthetic time series with recovering missing measurements for sequential images. The reconstruction method incorporates temporal variation according to physical properties of targets and anisotropic spatial optical properties into image processing techniques. This adaptive approach allows successive refinement of the structure of objects that are barely detectable in the observed series. The system sequentially collects the estimated results from the adaptive reconstruction and then statistically analyzes them to monitor and forecast the change in surface characteristics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.11-19
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• 2018

Recently, many companies offer various web-based map that is based on GIS(Geographic Information System) information. Google Map, Open street, Bing Map, Naver Map, Daum Map, Vwolrd Map, etc are the few examples of such system. In this paper, we propose a method to visualize ocean forecasting model data considering the flow diagram of tidal current, streamline expression algorithm, and user convenience by using vector field data information that is currently being served. It is confirmed that the proposed method of the flow diagram of tidal current, and stream line expression algorithm is faster than that of conventional ocean prediction model data by more than 2 times.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.3
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• pp.217-223
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• 2018

Overfishing capacity has become a global issue due to over-exploitation of fisheries resources, which result from excessive fishing intensity since the 1980s. In the case of Korea, the fishing effort has been quantified and used as an quantified index of fishing intensity. Fisheries resources of coastal fisheries in the Korean waters of the East Sea tend to decrease productivity due to deterioration in the quality of ecosystem, which result from the excessive overfishing activities according to the development of fishing gear and engine performance of vessels. In order to manage sustainable and reasonable fisheries resources, it is important to understand the fluctuation of biomass and predict the future biomass. Therefore, in this study, we forecasted biomass in the Korean waters of the East Sea for the next two decades (2017~2036) according to the changes in fishing intensity using four fishing effort scenarios; $f_{current}$, $f_{PY}$, $0.5{\times}f_{current}$ and $1.5{\times}f_{current}$. For forecasting biomass in the Korean waters of the East Sea, parameters such as exploitable carrying capacity (ECC), intrinsic rate of natural increase (r) and catchability (q) estimated by maximum entropy (ME) model was utilized and logistic function was used. In addition, coefficient of variation (CV) by the Jackknife re-sampling method was used for estimation of coefficient of variation about exploitable carrying capacity ($CV_{ECC}$). As a result, future biomass can be fluctuated below the $B_{PY}$ level when the current level of fishing effort in 2016 maintains. The results of this study are expected to be utilized as useful data to suggest direction of establishment of fisheries resources management plan for sustainable use of fisheries resources in the future.

• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.