• Korean Journal of Remote Sensing
• /
• v.34 no.6_2
• /
• pp.1299-1310
• /
• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.1
• /
• pp.29-39
• /
• 2017

In this research, thermal design data such as heat transfer coefficient on the wall surface required for ventilation system design which is to prevent the temperature rise in the underground utility tunnel that three sides are adjoined with the ground was investigated in numerical analalysis. The numerical model has been devised including the tunnel lining of the underground utility tunnel in order to take account for the heat transfer in the tunnel walls. The air temperature in the tunnel, wall temperature, and the heating value through the wall based on heating value(117~468 kW/km) of the power cable installed in the tunnel and the wind speed in the tunnel(0.5~4.0 m/s) were calculated by CFD simulation. In addition, the wall heat transfer coefficient was computed from the results analysis, and the limit distance used to keep the air temperature in the tunnel stable was examined through the research. The convective heat transfer coefficient at the wall surface shows unstable pattern at the inlet area. However, it converges to a constant value beyond approximately 100 meter. The tunnel wall heat transfer coefficient is $3.1{\sim}9.16W/m^2^{\circ}C$ depending on the wind speed, and following is the dimensionless number:$Nu=1.081Re^{0.4927}({\mu}/{\mu}_w)^{0.14}$. This study has suggested the prediction model of temperature in the tunnel based on the thermal resistance analysis technique, and it is appraised that deviation can be used in the range of 3% estimation.

• Atmosphere
• /
• v.16 no.2
• /
• pp.97-110
• /
• 2006

This study investigates the response of a typhoon model to the change of the sea surface temperature (SST) throughout the model integration. The SST change is parameterized as a formulae of which the magnitude is given as a function of not only the intensity and the size but the moving speed of tropical cyclone. The formulae is constructed by referring to many previous observational and numerical studies on the SST cooling with the passage of tropical cyclones. Since the parameterized cooling formulae is based on the mathematical expression, the resemblance between the prescribed SST cooling and the observed one during the period of the numerical experiment is not complete nor satisfactory. The agreements between the prescribed and the observed SST even over the swath of the typhoon passage differ from case to case. Numerical experiments are undertaken with and without prescribing the SST cooling. The results with the SST cooling do not show clear evidence in improving the track prediction compared to those of the without-experiments. SST cooling in the model shows its swath along the incomplete simulated track so that the magnitude and the distribution of the sea surface cooling does not resemble completely with the observed one. However, we have observed a little improvement in the intensity prediction in terms of the central pressure of the tropical cyclone in some cases. In case where the model without the SST treatment is not able to yield a correct prediction of the filling of the tropical cyclone especially in the decaying stage, the pulling effect given by the SST cooling alleviates the over-deepening of the model so that the central pressure approaches toward the observed value. However, the opposite case when the SST treatment makes the prediction worse may also be possible. In general when the sea surface temperature is reduced, the amount of the sensible and the latent heat from the ocean surface become also reduced, which results in the weakening of the storms comparing to the constant SST case. It turns out to be the case also in our experiments. The weakening is realized in the central pressure, maximum wind, horizontal temperature gradient, etc.

• Journal of the Korean earth science society
• /
• v.40 no.3
• /
• pp.259-271
• /
• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.347-350
• /
• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• Journal of Korea Water Resources Association
• /
• v.53 no.4
• /
• pp.313-322
• /
• 2020

There is always a risk of water disasters due to sudden storms in mountainous regions in Korea, which is more than 70% of the country's land. In this study, a radar-based risk prediction technique for sudden downpour is applied in the mountainous region and is evaluated for its applicability using Mt. Biseul rain radar. Eight local heavy rain events in mountain regions are selected and the information was calculated such as early detection of cumulonimbus convective cells, automatic detection of convective cells, and risk index of detected convective cells using the three-dimensional radar reflectivity, rainfall intensity, and doppler wind speed. As a result, it was possible to confirm the initial detection timing and location of convective cells that may develop as a localized heavy rain, and the magnitude and location of the risk determined according to whether or not vortices were generated. In particular, it was confirmed that the ground rain gauge network has limitations in detecting heavy rains that develop locally in a narrow area. Besides, it is possible to secure a time of at least 10 minutes to a maximum of 65 minutes until the maximum rainfall intensity occurs at the time of obtaining the risk information. Therefore, it would be useful as information to prevent flash flooding disaster and marooned accidents caused by heavy rain in the mountainous area using this technique.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.41-55
• /
• 2017

This study predicts the average scores of top 150 PGA golf players on 132 PGA Tour tournaments (2013-2015) using data mining techniques and statistical analysis. This study also aims to predict the Top 10 and Top 25 best players in 4 different playoffs. Linear and nonlinear regression methods were used to predict average scores. Stepwise regression, all best subset, LASSO, ridge regression and principal component regression were used for the linear regression method. Tree, bagging, gradient boosting, neural network, random forests and KNN were used for nonlinear regression method. We found that the average score increases as fairway firmness or green height or average maximum wind speed increases. We also found that the average score decreases as the number of one-putts or scrambling variable or longest driving distance increases. All 11 different models have low prediction error when predicting the average scores of PGA Tournaments in 2015 which is not included in the training set. However, the performances of Bagging and Random Forest models are the best among all models and these two models have the highest prediction accuracy when predicting the Top 10 and Top 25 best players in 4 different playoffs.

• Journal of Broadcast Engineering
• /
• v.28 no.3
• /
• pp.285-292
• /
• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Korean Journal of Environment and Ecology
• /
• v.32 no.4
• /
• pp.413-424
• /
• 2018

The study analyzed the effects of topographic structures and altitude in mountainous parks in Mt. Namsan in Gyeongju on the generation of anions. The temperature was at ridge ($9.82^{\circ}C$) > valley ($8.44^{\circ}C$), the relative humidity valley (59.01 %) > ridge (58.64 %), the solar radiation ridge ($34.40W/m^2$) > valley($14.69W/m^2$), the wind speed ridge (0.63m/s) > valley(0.37m/s), and the negative ion valley($636.81ea/cm^3$) > ridge($580.04ea/cm^3$). In the valley, the correlation with altitude was verified for the temperature, relative humidity, solar radiation, and negative ion generation in the valley. The relative humidity, solar radiation, and negative ion indicated a positive correlation while the temperature had a negative correlation. In the ridge, the correlation with altitude was verified for the temperature, relative humidity, wind speed, solar radiation, and negative ion generation. The relative humidity, solar radiation, and negative ion generation indicated a positive correlation while the temperature and wind speed had a negative correlation. The regression analysis showed the prediction equation of y=-0.006x+9.663 (x=altitude, y=temperature) in the valley and y=-0.009x+11.595 (x=altitude, y=temperature) in the ridge for the temperature, y=0.027x+53.561 (x=altitude, y=relative humidity) in the valley and y=0.008x+56.646 (x=altitude, y=relative humidity) in the ridges for the relative humidity, and y=0.027x+53.561 (x=altitude, y=negative Ion generation) in the valley and y= 0.008x+56.646 (x=altitude, y=negative Ion generation) in the ridge for the negative ion generation.

• Journal of the Korean Society of Safety
• /
• v.31 no.2
• /
• pp.119-126
• /
• 2016

Across the world, the industrialization has increased the frequency of climate anomaly. The size of damage due to recent natural disasters is growing large and fast, and the human damage and economic loss due to disasters are consistently increasing. Urbanization has a structure vulnerable to natural disasters. Therefore, in order to reduce damage from natural disasters, both hardware and software approaches should be utilized. Currently, however, the development of a statistical access process for 'analysis of disaster occurrence factor' and 'prediction of damage costs' for disaster prevention and overall disaster management is inadequate. In case of local governments, overall disaster management system is not established, or even if it is established, unscientific classification system and management lead to low utility of natural statistics of disaster year book. Therefore, in order to minimize disaster damage and for rational disaster management, the disaster damage survey process should be improved. This study selected gale as the focused analysis target among natural disasters recorded in disaster year book such as storm, torrential rain, gale, high seas, and heavy snow, and analyzed disaster survey process. Based on disaster year book, the gale damage size was analyzed and the issues occurring from the correlation of gale and damage amount were examined, so as to suggest an improvement plan for reliable natural disaster information collection and systematic natural disaster damage survey.