• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.46-53
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• 2009

Urbanization brings several changes to the natural environment. Its consequences can have a direct effect on climatic features, as in the Urban Heat Island Effect. One factor that directly affects the urban climate is the green area. In urban areas, vegetation is suppressed in order to accommodate manmade buildings and streets. In this paper we analyze the effect of green areas on the urban temperature in Seoul. The period selected for analysis was July 30th, 2007. The ground temperature was measured using Landsat TM satellite imagery. Land cover was calculated in terms of city area, water, bare soil, wet lands, grass lands, forest, and farmland. We extracted the surface temperature using the Linear Regression Model. Then, we did a regression analysis between air temperature at the Automatic Weather Station and surface temperature. Finally, we calculated the temperature decrease area and the population benefits from the green areas. Consequently, we determined that a green area with a radius of 500m will have a temperature reduction area of $67.33km^2$, in terms of urban area. This is 11.12% of Seoul's metropolitan area and 18.09% of the Seoul urban area. We can assume that about 1,892,000 people would be affected by this green area's temperature reduction. Also, we randomly chose 50 places to analysis a cross section of temperature reduction area. Temperature differences between the boundaries of green and urban areas are an average of $0.78^{\circ}C$. The highest temperature difference is $1.7^{\circ}C$, and the lowest temperature difference is $0.3^{\circ}C$. This study has demonstrated that we can understand how green areas truly affect air temperature.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.2
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• pp.135-145
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• 2020

Wave observation is widely used to direct observation method for observing the water surface elevation using wave buoy or pressure gauge and remote-sensing wave observation method. The wave buoy and pressure gauge can produce high-quality wave data but have disadvantages of the high risk of damage and loss of the instrument, and high maintenance cost in the offshore area. On the other hand, remote observation method such as radar is easy to maintain by installing the equipment on the land, but the accuracy is somewhat lower than the direct observation method. This study investigates the data quality of MIROS Wave and Current Radar (MWR) installed at Dokdo and improve the data quality of remote wave observation data using the wave buoy (CWB) observation data operated by the Korea Meteorological Administration. We applied and developed the three types of wave data quality control; 1) the combined use (Optimal Filter) of the filter designed by MIROS (Reduce Noise Frequency, Phillips Check, Energy Level Check), 2) Spike Test Algorithm (Spike Test) developed by OOI (Ocean Observatories Initiative) and 3) a new filter (H-Ts QC) using the significant wave height-period relationship. As a result, the wave observation data of MWR using three quality control have some reliability about the significant wave height. On the other hand, there are still some errors in the significant wave period, so improvements are required. Also, since the wave observation data of MWR is different somewhat from the CWB data in high waves of over 3 m, further research such as collection and analysis of long-term remote wave observation data and filter development is necessary.