• 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.30 no.2
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• pp.69-83
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• 2018

The South China Sea (SCS) is a typical marginal sea characterized with the deep basin, shelf break, shallow shelf, many straits, and complex bathymetry. This study investigated the tidal characteristics and propagation, and reproduced typhoon-induced storm surge in this region using the regional real-time tide-surge model, which was based on the unstructured grid, resolving in detail the region of interest and forced by tide at the open boundary and by wind and air pressure at the surface. Typhoon Haiyan, which occurred in 2013 and caused great damage in the Philippines, was chosen as a case study to simulate typhoon's impact. Amplitudes and phases of four major constituents were reproduced reasonably in general, and the tidal distributions of four constituents were similar to the previous studies. The modelled tide seemed to be within the acceptable levels, considering it was difficult to reproduce the tide in this region based on the previous studies. The free oscillation experiment results described well the feature of tide that the diurnal tide is prevailing in the SCS. The tidal residual current and total energy dissipation were discussed to understand the tidal and sedimentary environments. The storm-surge caused by typhoon Haiyan was reasonably simulated using this modeling system. This study established the regional real-time barotropic tide/water level prediction system for the South China Sea including the seas around the Philippines through the validation of the model and the understanding of tidal characteristics.

• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.703-707
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• 2009

In order to evaluate applicability of cylindrical slit type block breakwater to the field water, which was designed from the previous physical model study, it is analyzed the calmness of harbor area by the numerical model experiment. For a small fishery port in southern coast of Korea a SWAN model using the wave action balance equation was formulated. The reflection and transmission coefficients induced by the physical model test were introduced to the numerical model. The model response with cylindrical slit type breakwater was compared with the impermeable breakwater case and the possibility of water quality improvement through the water circulation by the new structure was investigated. For numerical simulation, parameters of deepwater design wave from the prediction report II for overall deepwater design wave by KORDI were used and wind parameters from the 50years return period observed for 37years(1970~2006) were adopted in the numerical model. The response of west breakwater in Mijo port applying the NE and NNE waves, which were dominant in this area, was analyzed. It was found that the transmission characteristic of designed cylindrical slit breakwater was well presented in the numerical model.

• Atmosphere
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• v.20 no.3
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• pp.239-246
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• 2010

In this study, the climatological characteristics of the landfall typhoons on North Korea are surveyed to estimate the frequency, the intensity, the track, and their damage. The data for the period of 1951-2008 are used from both RSMC (Regional Specialized Meteorological Center) Tokyo Typhoon Center and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research), EM-DAT (Emergency Events Database). There are the ten highest frequencies from 1961 to 1965 and is one frequency for the period of both 1966-1979 and 1976-1980 respectively. Even if a clear trend on the frequency of typhoon is not defined, it is noticeable the intensity has been weak since the frequency of TS (Tropical Storm) decreased. In order to figure out both the characteristic of intensity and the relation between the typhoon track and the expansion of North Pacific High (NPH), Typhoon's tracks are classified into three types as follows: (I) landing on the west coast of North Korea through the mainland of China, (II) landing on the west coast of North Korea, (III) landing on a central/eastern part of the Korean peninsula through South Korea. More often than not, the characteristic of Type (I) is the case of a landfall after it becomes extratropical cyclone. Type(II) and Type(III) show a landfall as TS grade, by comparision. On the relation between the typhoon's track and the expansion of NPH analyzed, Type (I) shows the westward expansion while both Type (II) and Type (III) show the northward expansion and development of NPH. This means the intensity of a typhoon landfall on North Korea is variable depending on the development of NPH. Finally, only two cases are found among total five cases in EM-DAT, reportedly that North Korea was damaged. And therefore, the damage by the wind of Prapiroon (the $12^{th}$ typhoon, 2000) and heavy rainfall with Rusa (the $15^{th}$ typhoon, 2002) landing on North Korea was analyzed. Moreover, it is estimated both Prapiroon and Rusa have done badly damaged to North Korea as the economical losses of as much as six billion and five hundred-thousand US dollar, respectively.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.245-252
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• 2016

The purpose of this study is to make up for missing of weather informations from ASOS and AWS using artificial neural networks. We collected temperature, relative humidity and wind velocity for August during 5-yr (2011-2015) and sample designed artificial neural networks, assuming the Seoul weather station was missing. The result of sensitivity study on number of epoch shows that early stopping appeared at 2,000 epochs. Correlation between observation and prediction was higher than 0.6, especially temperature and humidity was higher than 0.9, 0.8 respectively. RMSE decreased gradually and training time increased exponentially with respect to increase of number of epochs. The predictability at 40 epoch was more than 80% effect on of improved results by the time the early stopping. It is expected to make it possible to use more detailed weather information via the rapid missing complemented by quick learning time within 2 seconds.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.95-100
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• 2009

Fine fuel moisture code (FFMC), a main component of forest fire weather index(FWI) in the Canadian forest fire danger rating system(CFFDRS), indicated a probability of ignition through expecting a dryness of fine fuels. According to this code, a rising of temperature and wind velocity, a decreasing of precipitation and decline of humidity in a weather condition showed a rising of a danger rate for the forest fire. In this study, we analyzed a weather condition during 5 years in Kangwon province, calculated a FFMC and examined an application of FFMC. Very low humidity and little precipitation was a characteristic during spring and fall fire season in Kangwon province. 75% of forest fires during 5 years occurred in this season and especially 90% of forest fire during fire season occurred in spring. For developing of the prediction model for a forest fire occurrence probability, we used a logistic regression function with forest fire occurrence data and classified mean FFMC during 10 days. Accuracy of a developed model was 63.6%. To improve this model, we need to deal with more meteorological data during overall seasons and to associate a meteorological condition with a forest fire occurrence with more research results.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.