• Atmosphere
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• v.26 no.1
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• pp.111-126
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• 2016

In the numerical weather model, surface properties can be defined by various parameters such as terrain height, landuse, surface albedo, soil moisture, surface emissivity, roughness length and so on. And these parameters need to be improved in the Seoul metropolitan area that established high-rise and complex buildings by urbanization at a recent time. The surface roughness length map is developed from digital elevation model (DEM) and it is implemented to the high-resolution numerical weather (WISE-WRF) model. Simulated results from WISE-WRF model are analyzed the relationship between meteorological variables to changes in the surface roughness length. Friction speed and wind speed are improved with various surface roughness in urban, these variables affected to temperature and relative humidity and hence the surface roughness length will affect to the precipitation and Planetary Boundary Layer (PBL) height. When surface variables by the WISE-WRF model are validated with Automatic Weather System (AWS) observations, NEW experiment is able to simulate more accurate than ORG experiment in temperature and wind speed. Especially, wind speed is overestimated over $2.5m\;s^{-1}$ on some AWS stations in Seoul and surrounding area but it improved with positive correlation and Root Mean Square Error (RMSE) below $2.5m\;s^{-1}$ in whole area. There are close relationship between surface roughness length and wind speed, and the change of surface variables lead to the change of location and duration of precipitation. As a result, the accuracy of WISE-WRF model is improved with the new surface roughness length retrieved from DEM, and its surface roughness length is important role in the high-resolution WISE-WRF model. By the way, the result in this study need various validation from retrieved the surface roughness length to numerical weather model simulations with observation data.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of the Korean Geographical Society
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• v.43 no.4
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• pp.477-494
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• 2008

Stable isotope analyses on lake or wetland sediments are useful to reconstruct paleoclimate. Organic and inorganic carbonates obtained from lake sediment are isotopically analyzed to get oxygen and carbon isotopic ratios. Oxygen isotope ratios can be used to quantitatively and qualitatively reconstruct paleo-temperature or humidity while carbon isotope ratios be used to reveal environmental changes around the lake or human impacts on the area. Peat mosses in peat bogs are nice samples for the carbon isotope analysis, which derives paleo-temperature and paleo-atmospheric $CO_2$ changes. In coastal area, the reconstruction of past sea-level is possible because terrestrial originated organic matter is carbon isotopically different from marine originated organic matter. Also, scientists can do research on Asian Monsoon based on the fact that $\delta^{13}C$ of C3 plants and C4 plants are consistently different each other and that they are distributed differently with respect to salinity. In Korea, paleoenvironmental studies using stable isotopes are not popular yet because of low academic interests on the methodology and difficulties of obtaining proper sediment samples. Interesting results can be produced to answer paleoenvironmental questions of Korea if scientists isotopically analyze sediment cores from a paleo-lake such as Hanon in Jeju island, peat bogs such as Mujechi-Neup and Yong-Neup, and coastal wetlands.

• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.1-15
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• 2023

This study focuses on modeling the impact of ammonia leakage from the storage tank in a combined cycle power plant's flue gas denitrification facility. It employs accident impact assessments and diffusion models to determine the optimal scenarios for ammonia storage tank leakage accidents. The study considers the operating conditions of variables as standard conditions for predicting the extent of damage. The Taean combined cycle power plant is chosen as the target area, taking into account seasonal factors such as temperature, humidity, wind speed, atmospheric stability, and wind direction. By utilizing a Gaussian diffusion model, the concentration of ammonia gas at various locations is estimated to assess the potential extent of external damage resulting from a leak. The study reveals that in conditions of high temperature and stable atmosphere within the specified range, lower wind speeds contribute to increased damage to the human body due to ammonia diffusion.

• Korean Journal of Agricultural Science
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• v.6 no.1
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• pp.56-64
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• 1979

Two types of fiat-plate collector were designed and constructed for utilizing the solar energy as heating source of red pepper drying. It was performed to investigate the basic factors on using the collectors and the drying effect on various types of red pepper, and the results obtained are summarized as follows. 1. The optimum tilted angles of the collector in Daejeon area were ${\phi}-15^{\circ}$ in summer season and ${\phi}+15^{\circ}$ in winter season when it was adjusted two times per a year: 2. In the conditions during experiment period, average atmospheric temperature and relative humidity were $25.6^{\circ}C$ and 52.6%, respectively, and $42.0^{\circ}C$, 74.2% in the control chamber. The temperature in the drying chamber connected to the water heater was the highest but relative humidity in the chamber connected to the air heater was the lowest among the chambers. 3. The drying velocity of whole red pepper in the chamber connected to the water heater was the fastest as 2.3 times as compared to the whole type on the mat drying followed by air heater and control in decreasing order. The horizontally cut red pepper in the chamber connected to the water heater was dried exceedingly fast among twelve plots. 4. The content of capsaicine as pungent principle and of capsanthine as red pigment in the red pepper were reduced during drying but there were no differences significantly on the drying method, and it could not affect much on the quality of dried product.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.536-539
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• 2018

In this paper, we propose a system that analyzes environment information by using deep learning and then provides a suitable environment for users by predicting environmental information change. As the level of living improves, interest in improving the quality of life is increasing. In particular, as the air quality deteriorated due to the recent occurrence of dust, smog, fine dust, and ultrafine dust, the indoor air quality as well as the outdoor air became a serious problem. The increase of indoor pollution due to the lack of ventilation and the use of chemicals is a serious problem for modern people who have a lot of indoor living. In order to solve this indoor air pollution, a system has been proposed that measures the state of air quality through sensors and maintains proper temperature and humidity. However, existing system has a difficulty to apply most of the atmospheric environment information to various users depending on sensors only. The system proposed in this paper predicts the indoor environment by analyzing the indoor pollution information collected through the sensor using the deep learning. Then, the predicted indoor environment is modeled and learned in this system, and the environment suitable for the user is suggested. Afterwards, the system receives feedback from the user and repeats the process of re-learning the proposed environment so that it can create the optimal environment for the user.

• 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 Advanced Navigation Technology
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• v.23 no.6
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• pp.597-604
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• 2019

Artificial neural networks are algorithms that simulate learning through interaction and experience in neurons in the brain and that are a method that can be used to produce accurate results through learning that reflects the characteristics of data. In this study, a model using deep neural network was presented to improve the predicted wind speed values in the meteorological dynamic model. The wind speed prediction improvement model using the deep neural network presented in the study constructed a model to recalibrate the predicted values of the meteorological dynamics model and carried out the verification and testing process and Separate data confirm that the accuracy of the predictions can be increased. In order to improve the prediction of wind speed, an in-depth neural network was established using the predicted values of general weather data such as time, temperature, air pressure, humidity, atmospheric conditions, and wind speed. Some of the data in the entire data were divided into data for checking the adequacy of the model, and the separate accuracy was checked rather than being used for model building and learning to confirm the suitability of the methods presented in the study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Journal of agriculture & life science
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• v.46 no.3
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• pp.119-127
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• 2012

For the promotion of the evaporative cooling efficiency of hot air in greenhouse in summer, a fog sprayer consisted of a high volume spraying two-fluid nozzle with turbo fan and a blowing fan was set up at 2.2 m height from bottom of small glass greenhouse and tested to estimate the possibility of the greenhouse cooling. The mean droplet size and the volume sprayed by one of fog sprayer were $29{\mu}m$ and $160m{\ell}/min$. All the droplets sprayed and blown by the fog sprayer were evaporated within 2 m radius. The result from the cooling test that two sprayers set up in glass greenhouse of plane area $228m^2$ was represented lower cooling effect that the temperature and relative humidity of inside air of greenhouse were $28.8^{\circ}C$ and 87.5% when those of outside air of greenhouse were $30.2^{\circ}C$ and 81.2%. Through investigation of literatures and results of the cooling test, it was estimated that the water spraying rate of evaporative cooling of single span greenhouse with 50% light curtain and with air change rate of 1 volume/min was $10m{\ell}/min/m^2$ so that the inside air temperature may cool down $2{\sim}3^{\circ}C$ on the basis of $35^{\circ}C$ atmospheric temperature in summer of south korean area.