• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.233-246
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• 2009

The partitioning of evapotranspiration (ET) into evaporation (E) and transpiration (T) is critical in understanding the water cycle and the couplings between the cycles of energy, water, and carbon. In forests, the total ET measured above the canopy consists of T from both overstory and understory vegetation, and E from soil and the intercepted precipitation. To quantify their relative contributions, we have measured ET from the floors of deciduous and coniferous forests in Gwangneung using eddy covariance technique from 1 June 2008 to 31 May 2009. Due to smaller eddies that contribute to turbulent transfer near the ground, we performed a spectrum analysis and found that the errors associated with sensor separation were <10%. The annual sum of the understory ET was 59 mm (16% of total ET) in the deciduous forest and 43 mm (~7%) in the coniferous forest. Overall, the understory ET was not negligible except during the summer season when the plant area index was near its maximum. In both forest canopies, the decoupling factor ($\Omega$) was about ~0.15, indicating that the understory ET was controlled mainly by vapor pressure deficit and soil moisture content. The differences in the understory ET between the two forest canopies were due to different environmental conditions within the canopies, particularly the contrasting air humidity and soil water content. The non-negligible understory ET in the Gwangneung forests suggests that the dual source or multi-level models are required for the interpretation and modeling of surface exchange of mass and energy in these forests.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.717-726
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• 2018

PM(Particulate Matter) cause serious diseases of air pollution. Most of the studies have analyzed local distribution trends using satellite images or modeling techniques. However,the method using the spatial interpolation method based on the meteorological value is insufficient in Korea. In this study, monthly spatial distribution of $PM_{10}$ and $PM_{2.5}$ in January, February, March, and April of 2018 Seoul Metropolitan City were analyzed based on 39 PM monitoring networks. In addition, a distribution map showing the difference between $PM_{10}$ and $PM_{2.5}$ was based on the distribution obtained through this study. The regions of high $PM_{10}$ and $PM_{2.5}$ emissions were selected. In addition, the correlation between $PM_{10}$ and $PM_{2.5}$ was confirmed through the distribution map. This study analyzed the spatial distribution variation results of analyzing $PM_{10}$ and $PM_{2.5}$ in Seoulthrough spatial analysis technique. As a result of this study, it was confirmed that $PM_{10}$ shows high measured value on the roadside measurement station.

• Explosives and Blasting
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• v.37 no.3
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• pp.13-24
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• 2019

This paper was prepared to investigate the behavior of fragments in underwater torpedo explosion beneath a frigate or surface ship by using an explicit finite element analysis. In this study, a fluid-structure interaction (FSI) methodology, called the multi-material arbitrary Lagrangian-Eulerian (MM-ALE) approach in LS-DYNA, was employed to obtain the responses of the torpedo fragments and frigate hull to the explosion. The Euler models for the analysis were comprised of air, water, and explosive, while the Lagrange models consisted of the fragment and the hull. The focus of this modeling was to examine whether a worst-case fragment could penetrate the frigate hull located close (4.5 m) to the exploding torpedo. The simulation was performed in two separate steps. At first, with the assumption that the expanding skin of the torpedo had been torn apart by consuming 30% of the explosive energy, the initial velocity of the worst-case fragment was sought based on a well-known experimental result concerning the fragment velocity in underwater bomb explosion. Then, the terminal velocity of the worst-case fragment that is expected to occur before the fragment hit the frigate hull was sought in the second step. Under the given conditions, the possible initial velocities of the worst-case fragment were found to be very fast (400 and 1000 m/s). But, the velocity difference between the fragment and the hull was merely 4 m/s at the instant of collision. This result was likely to be due to both the tremendous drag force exerted by the water and the non-failure condition given to the frigate hull. Anyway, at least under the given conditions, it is thought that the worst-case fragment seldom penetrate the frigate hull because there is no significant velocity difference between them.

• Journal of Environmental Science International
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• v.28 no.3
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• pp.341-356
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• 2019

In this study, the impacts of local meteorology caused by tidal changes in the West Sea on ozone distributions in the Seoul Metropolitan Area (SMA) were analyzed using a meteorological model (WRF) and an air quality (CMAQ) model. This study was carried out during the day (1200-1800 LST) between August 3 and 9, 2016. The total area of tidal flats along with the tidal changes was calculated to be approximately $912km^2$, based on data provided by the Environmental Geographic Information Service (EGIS) and the Ministry of Oceans and Fisheries (MOF). Modeling was carried out based on three experiments, and the land cover of the tidal flats for each experiment was designed using the coastal wetlands, water bodies (i.e., high tide), and the barren or sparsely vegetated areas (i.e., low tide). The land cover parameters of the coastal wetlands used in this study were improved in the herbaceous wetland of the WRF using updated albedo, roughness length, and soil heat capacity. The results showed that the land cover variation during high tide caused a decrease in temperature (maximum $4.5^{\circ}C$) and planetary boundary layer (PBL) height (maximum 1200 m), and an increase in humidity (maximum 25%) and wind speed (maximum $1.5ms^{-1}$). These meteorological changes increased the ozone concentration (about 5.0 ppb) in the coastal areas including the tidal flats. The increase in the ozone concentration during high tide may be caused by a weak diffusion to the upper layer due to a decrease in the PBL height. The changes in the meteorological variables and ozone concentration during low tide were lesser than those occurring during high tide. This study suggests that the meteorological variations caused by tidal changes have a meaningful effect on the ozone concentration in the SMA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.477-484
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• 2019

As the residence time in the vehicle increases, the passenger desires a pleasant and stable riding environment in addition to the high driving performance of the vehicle. The windshield defrosting performance is one of the performance requirements that is essential for driver's safe driving. In order to improve the defrosting performance of the windshield of a vehicle, relevant elements such as the shape of the defrost nozzle should be appropriately designed. In this paper, CFD based numerical analysis is conducted to improve defrost performance of small electric vehicles. The defrost performance analysis was performed by changing the angle of the defrost nozzle and the guide vane that spray hot air to the windshield of the vehicle. Numerical simulation results show that the defrosting performance is best when the defrost nozzle angle is $70^{\circ}$ and the guide vane installation angle is $60^{\circ}$. Based on the analytical results, the defrosting experiment was performed by fabricating the defrost nozzle and the guide vane. As a result of the experiment, it is confirmed that the frost of windshield is removed by 80% within 20 minutes, and it is judged that the defrost performance satisfying the FVMSS 103 specification is secured.

• Journal of Environmental Analysis, Health and Toxicology
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• v.21 no.4
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• pp.281-291
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• 2018

Recently, long-range atmospheric transport (LRAT) from China is regarded as a major reason for elevated levels of particulate matter (PM) in Korea. However, local emissions also play an important role in PM pollution, especially in large-scale industrial cities. In this study, PM samples were collected at suburban, residential, and industrial sites in Ulsan, Korea. Polycyclic aromatic hydrocarbons (PAHs) and heavy metals were analyzed, and a potential human health risk assessment was conducted. The concentrations of PAHs and heavy metals in total suspended particles (TSP) increased during high $PM_{10}$ episodes, and backward trajectory analysis verified the influence of LRAT from China during the high episodes. Furthermore, the concentrations of PAHs and heavy metals in $PM_{2.5}$ and $PM_{10}$ at the industrial site were higher than those at the residential site. The risk assessment of PAHs and heavy metals in $PM_{2.5}$ suggested no significant health effects. The highest levels of PAHs were measured in the particle size of $0.32{\sim}0.56{\mu}m$ at the residential site, and those of heavy metals were detected in the particle size of 1.8~5.6 and $>18{\mu}m$, reflecting different major emissions sources for both groups. On the basis of this preliminary study, we are planning long-term monitoring and modeling studies to quantitatively evaluate the influence of industrial activities on the PM pollution in Ulsan.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.548-557
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• 2021

Background: Due to the emergence of new diseases such as COVID-19, an increasing number of people are struggling with stress and depression. Interest is growing in forest-based recreation for physical and mental relief. Objectives: A prediction model equation using meteorological factors and data was developed to predict the quantities of medicinal substances generated in forests (monoterpenes) in real-time. Methods: The concentration of phytoncide and meteorological factors in the forests near Chuncheon in South Korea were measured for nearly two years. Meteorological factors affecting the observation data were acquired through a multiple regression analysis. A model equation was developed by applying a linear regression equation with the main factors. Results: The linear regression analysis revealed a high explanatory power for the coefficients of determination of temperature and humidity in the coniferous forest (R²=0.7028 and R²=0.5859). With a temperature increase of 1℃, the phytoncide concentration increased by 31.7 ng/Sm³. A humidity increase of 1% led to an increase in the coniferous forest by 21.9 ng/Sm³. In the deciduous forest, the coefficients of determination of temperature and humidity had approximately 60% explanatory power (R²=0.6611 and R²=0.5893). A temperature increase of 1℃ led to an increase of approximately 9.6 ng/Sm³, and 1% humidity resulted in a change of approximately 6.9 ng/Sm³. A prediction model equation was suggested based on such meteorological factors and related equations that showed a 30% error with statistical verification. Conclusions: Follow-up research is required to reduce the prediction error. In addition, phytoncide data for each region can be acquired by applying actual regional phytoncide data and the prediction technique proposed in this study.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.