• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.6
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• pp.803-808
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• 1999

Trend of sea level change has been analysed by using the tidal data gathered at the 12 tide stations along the coast of Korean peninsula. Analysis and prediction of the sea level change were performed by Principal Component Analysis (PCA). For the period of 20 years from 1976 to 1995, the trend generally shows a rising pattern such as 0.22 cm/yr, 0.29 cm/yr, and 0.59 cm/yr along the eastern, southern, and western coast of Korea, respectively. On the average the sea level around the Korean peninsula seems to be rising at a rate of 0.37 cm/yr. Adopting the average rate to the sea level prediction model proposed by EPA (Titus and Narrayanan, 1995), the sea level may be approximately 50$\~$60 cm higher than the present sea level by the end of the next century.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.891-898
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• 2017

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to estimate the sea level which can be applied to the tidal sensors to monitor the variation of sea level. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for tidal sensors. On the other hand, the proposed algorithm is very simple but precise since we use the measured data from the sensor to estimate the sea level value in short period such as one or two hours. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.

• Proceedings of KOSOMES biannual meeting
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• 2003.11a
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• pp.219-225
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• 2003

The present study intends to assess the long-term steric sea-level change and its prediction, and potential impacts to the sea-level rise due to the 21st global warming in the coastal zone of the Korea in which much socioeconomic activities have been occurred. The analysis of the 23 tide-gauge data near Korea reveals the overall mean sea-level trend of 2.31 mm/yr.In the satellite altimeter data (Topex/Poseidon and ERS), the sea-level trend in the East Sea is 4.6mm/yr. Both are larger than those of the global average value. However, it is quite questionable that the sea-level trends with the tide-gauge data on the neighboring seas of Korea relate to global warming because of the relatively short observation period and large spatial variability. It is also not clear whether the high trend of altimeter data in the East Sea is related to the acceleration of sea level rise in the Sea, short response time of the Sea, natural variability such as decadal variability, short duration of the altimeter. The coastal zone of Korea appears to be quite vulnerable to the 21st sea level rise such that for the I-m sea level rise with high tide and storm surge, the inundation area is 2,643 km2, which is about $1.2\%$ of total area and the population in the risk areas of inundation is 1.255 million, about $2.6\%$ of total population. The coastal zone west of Korea is appeared to be the most vulnerable area compared to the east and south. In the west of the Korea, the North Korea appears to be more vulnerable than South Korea. In order to cope with the future possible impact of sea-level rise to the coastal zone of Korea effectively, it is essential to improve scientific information in the sea-level rise trend, regional prediction, and vulnerability assessment near Korean coast.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.113-122
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• 2017

Natural disasters caused by climate change are increasing globally. There are few studies on the quantitative analysis methods for predicting damages in the island area due to sea level rise. Therefore, it is necessary to study the damage prediction analysis method using the GIS which can quantitatively analyze. In this paper, we analyze the cause and status of sea level rise, quantify the vulnerability index, establish an integrated terrestrial modeling method of the ocean and land, and establish a method of analyzing the damage area and damage scale due to sea level rise using GIS and the method of making the damage prediction figure was studied. In order to extract the other affected areas to sea level rise are apart of the terrain model is generated by one requires a terrain modeling of target areas are offshore and vertical reference system differences in land, found the need for correction by a tidal observations and geoid model there was. Grading of terrain, coastline erosion rate, coastal slope, sea level rise rate, and even average by vulnerable factors due to sea level rise indicates that quantitative damage prediction is possible due to sea level rise in the island area. In the case of vulnerable areas extracted by GIS, residential areas and living areas are concentrated on the coastal area due to the nature of the book area, and field survey shows that coastal changes and erosion are caused by sea level rise or tsunami.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.276-283
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• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.

• Journal of KSNVE
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• v.3 no.2
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• pp.155-161
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• 1993

The vibration generated by the machinery on board is transmitted to the hull and into the water. At the early design stage, the prediction of the hull vibration and the radiated noise level is very important to reduce their levels. In this study, SAE(Statistical Energy Analysis) technique is applied to predict structureborne noise level of the hull considering fluid loading. Rayleigh integral is applied to predict the radiated noise level. The results of comparision between the predictions and measurements for the reinforced cylindrical shell have shown good agreements.

• Atmosphere
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• v.30 no.3
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• pp.293-309
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• 2020

There are lots of indices that define the intensity of East Asian summer monsoon (EASM) in climate systems. This paper assesses the prediction skill for EASM indices in a Global Seasonal Forecasting System (GloSea5) that is currently operating at KMA. Total 5 different types of EASM indices (WNPMI, EAMI, WYI, GUOI, and SAHI) are selected to investigate how well GloSea5 reproduces them using hindcasts with 12 ensemble members with 1~3 lead months. Each index from GloSea5 is compared to that from ERA-Interim. Hindcast results for the period 1991~2010 show the highest prediction skill for WNPMI which is defined as the difference between the zonal winds at 850 hPa over East China Sea and South China Sea. WYI, defined as the difference between the zonal winds of upper and lower level over the Indian Ocean far from East Asia, is comparatively well captured by GloSea5. Though the prediction skill for EAMI which is defined by using meridional winds over areas of East Asia and Korea directly affected by EASM is comparatively low, it seems that EAMI is useful for predicting the variability of precipitation by EASM over East Asia. The regressed atmospheric fields with EASM index and the correlation with precipitation also show that GloSea5 best predicts the synoptic environment of East Asia for WNPMI among 5 EASM indices. Note that the result in this study is limited to interpret only for GloSea5 since the prediction skill for EASM index depends greatly on climate forecast model systems.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.267-267
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• 2016

Extreme events, such as Winnie(1987), Rusa(2002), Maemi(2003) at sea-side urban area, resulted not only economic losses but also life losses. The Korean sea-side characterisitcs are so complicated thar the prediction of sea level rise makes difficult. Geomophologically, Korean pennisula sits on the rim of the Pacific mantle so the sea level is sensitive to the surges due to earth quake, typoon and abnormal climate changes. These environmetns require closer investigation for the preparing the inundatioin due to the sea level rise with customized prediction for local basin. The goal of this research is provide the information of inundation risk so the sea side urban basin could be more safe from the natural water disastesr.

• Atmosphere
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• v.33 no.4
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.