• International Journal of Ocean System Engineering
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• v.1 no.4
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• pp.211-221
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• 2011

Tugboats are widely used near harbors to assist with various operations such as the berthing and deberthing of very large vessels and the towing of barges. Capsizing accidents involving tugboats occasionally take place when the tugboat makes rapid turns in harsh weather conditions. When there is little evidence suggesting how the accident occurred and when the crew members are missing, it is necessary to predict the time history of the towing vessel’s attitude and trajectory from its departure point to when and where it capsized, depending on various input parameters using a numerical simulation. In this paper, the dynamics of a tugboat and a towed barge in conjunction with the external force and moment were established, and the possible input parameters and operational scenarios which might influence the large roll motion of the tugboat were identified. As a result of analyzing the simulated time history of the excessive roll motion of the tugboat, it was found that roll motion can take place when the tugboat is situated on the crest of a wave and when it is pulled by a towed barge through a towing line. The main cause of the accident would be the parameters that primarily influence such situations. These are the wave parameters, course changing scenario, and the amount of tension.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.45-52
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• 2018

This study identifies the cause of the accident and presents a new concept for safe urban stream management by numerical simulating the flood event of Cheonggyecheon on August 17, 2017, using rain data measured through a dense weather observation network. In order to simulate water retention in the CSO channel listed as one of the causes of the accident, a reliable urban runoff model(XP-SWMM) was used which can simulate various channel conditions. Rainfall data measured through SK Techx using SK Telecom's cell phone station was used as rain data to simulate the event. The results of numerical simulations show that rainfall measured through AWSs of Korea Meteorological Administration did not cause an accident, but a similar accident occurred under conditions of rainfall measured in SK Techx, which could be estimated more similar to actual phenomena due to high spatial density. This means that the low spatial density rainfall data of AWSs cannot predict the actual phenomenon occurring in Cheonggyecheon and safe river management needs high spatial density weather stations. Also, the results of numerical simulation show that the residual water in the CSO channel directly contributed to the accident.

• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• Solar Energy
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• v.17 no.3
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• pp.3-11
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• 1997

Recently the construction of atrium buildings has increased but along with it many problems in thermal environment have arised. since the exterior wall of glass, indoor temperature is greatly influenced by weather conditions and since the space volume is very large, the vertical air temperature is not uniform. So, in this study, a Vertical Temperature Distribution Model was developed to predict the vertical air temperature of an atrium and evaluate the effects of the design parameters on the air temperature distribution of an atrium. To consider the characteristics of the vertical air temperature distribution in an atrium, the Satosh Togari's Macroscopic Model was used basically for the calculation of the vertical air temperature distribution in large space and the solar radiation analysis model and natural ventilation analysis model in atrium. And to calculate the unsteady-state inside wall surface temperature(boundary condition), the finite difference method was used. For the verification of the developed temperature distribution program, numerical evaluation of air flow by the ${\kappa}-{\varepsilon}$ turbulence model and in-situ test was conducted in parallel. The results of this study, the developed temperature distribution program was seen to predict the thermal condition of the atrium very accurately.

• Journal of Ocean Engineering and Technology
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• v.32 no.6
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• pp.474-484
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• 2018

In this study, a new numerical modeling system was proposed to predict oil spills, which increasingly occur at sea as a result of abnormal weather conditions such as global warming. The hydrodynamic conditions such as the flow velocity needed to calculate oil dispersion were estimated using a three dimensional hydrodynamic model based on the Navier-Stokes equation, which considered all of the physical variations in the vertical direction. This improved the accuracy compared to those estimated by the conventional shallow water equation. The advection-diffusion model for the spilled oil was combined with the hydrodynamic model to predict the movement and fate of the oil. The effects of absorption, weathering, and wind were also considered in the calculation process. The combined model developed in this study was then applied to various test cases to identify the characteristics of oil dispersion over time. It is expected that the developed model will help to establish initial response and disaster prevention plans in the event of a nearshore oil spill.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.22-28
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• 2010

This study analyzed the performance of passive solar system for office building. A unit model of the passive solar system was proposed in order to predict its performance under varying parameters and Seoul weather date. Steady state heat transfer equations were set up using a energy balanced equations and solved using a inverse matrix method. Numerical simulation program to analyze system was developed by using MATLAB. As the results, the passive solar system performance of office building was determined by the insolation and the outdoor air temperature. Also the passive solar system indicate 6.7~16.2% of annual average efficiency. In the comparison with other systems of the conventional wall, mass wall could reduce the heating loads of 7.1% and trombe wall could reduce heating loads of 11.5%. Through this study, performance of passive solar system for office building was verified by numerical method. Consequently, the passive solar system could operate an important role as the alternative for saving energy consumption of office building, and the additional studies should be made through the experimental method for the commercialization.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• The Journal of Information Systems
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• v.29 no.3
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• pp.35-52
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• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.9
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• pp.735-742
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• 2015

In this paper, we compare and verify the prediction accuracy and feasibility for wind resources on a wind farm using the Weather Research and Forecasting (WRF) model, which is a numerical weather-prediction model. This model is not only able to simulate local weather phenomena, but also does not require automatic weather station (AWS), satellite, or meteorological mast data. To verify the feasibility of WRF to predict the wind resources required from a wind farm pre-feasibility study, we compare and verify measured wind data and the results predicted by WAsP. To do this, we use the Pyeongdae and Udo sites, which are located on the northeastern part of Jeju island. Together with the measured data, we use the results of annual and monthly mean wind speed, the Weibull distribution, the annual energy production (AEP), and a wind rose. The WRF results are shown to have a higher accuracy than the WAsP results. We therefore confirmed that WRF wind resources can be used in wind farm pre-feasibility studies.

• Atmosphere
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• v.29 no.3
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• pp.257-267
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• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.