• Korean Journal of Soil Science and Fertilizer
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• v.51 no.1
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• pp.16-27
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• 2018

To predict the influence of volcano eruption on agriculture in South Korea we evaluated the dispersal ranges of the volcanic ashes toward the South Korea based on the possibilities of volcano eruption in Mt. Baekdu. The possibilities of volcano eruption in Mt. Baekdu have been still being intensified by the signals including magmatic unrest of the volcano and the frequency of volcanic earthquakes swarm, the horizontal displacement and vertical uplift around the Mt. Baekdu, the temperature rises of hot springs, high ratios of $N_2/O_2$ and $_3He/_4He$ in volcanic gases. The dispersal direction and ranges and the predicted amount of volcanic ash can be significantly influenced by Volcanic Explosivity Index (VEI) and the trend of seasonal wind. The prediction of volcanic ash dispersion by the model showed that the ash cloud extended to Ulleung Island and Japan within 9 hours and 24 hours by the northwestern monsoon wind in winter while the ash cloud extended to northern side by the south-east monsoon wind during June and September. However, the ash cloud may extent to Seoul and southwest coast within 9 hours and 15 hours by northern wind in winter, leading to severe ash deposits over the whole area of South Korea, although the thickness of the ash deposits generally decreases exponentially with increasing distance from a volcano. In case of VEI 7, the ash deposits of Daejeon and Gangneung are $1.31{\times}10^4g\;m^{-2}$ and $1.80{\times}10^5g\;m^{-2}$, respectively. In addition, ash particles may compact close together after they fall to the ground, resulting in increase of the bulk density that can alter the soil physical and chemical properties detrimental to agricultural practices and crop growth.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.1
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• pp.107-115
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• 1993

This study was carried out to find the characteristics of surface ozone concentration data obtained during 1988-1991 by the Korea Ministry of Environment. Seasonal data (spring, summer, autumn and winter) wre obtained in May, August, November and February respectively at Kwanghwamun in Seoul. The pollutants analyzed in this study are $SO_2, TSP, CO, NO, NO_2 and NO_2/NO$. Atmospheric factors such as solar radiation, wind speed, relative humidity, cloud amount and atmospheric temperature are also analyzed. The influence of pollutants and atmospheric factors that affect ozone concentration were analyzed by statistical method. The results are summarized as follows : 1. The ozone concentration varied seasonally. The maximum values were 23 ppb in spring, 33 ppb in summer, 16 ppb in autumn and 13 ppb in winter. So the seasonal ozone value was highest in Summer. 2. Te diurnal concentration of ozone was highest during 2-4 P. M. and was very low in the morning and evening. 3. The maximal correlation coefficients of each season between ozone concentration and the influencing pollutants or atmospheric factors asr as follows ; a. spring, r = 0.44(solar radiation) b. summer, r = -0.59(relative humidity) c. autumn, r = -0.55(relative humidity) d. winter, r = -0.58($NO_2$) 4. The major factor affecting the ozone concentration in spring was solar radiation, Relative humidity was the first affecting factor in summer, autumn and $NO_2$ concentration was dominant in winter.

• Journal of the Korean Institute of Gas
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• v.21 no.5
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• pp.56-63
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• 2017

Hydrogen charging station was invested and supported around the world. In this study, the extent of damage caused by VCE in the charging station handling liquefied hydrogen was calculated, and the human and material damage was estimated through the Probit model. In addition The optimal height of vent stack for low temperature hydrogen was set. The damage range is 8.24m in small scale, 14.10m in medium scale, and 22.38m in large scale based on interest overpressure 6.9kPa. In case of death due to pulmonary hemorrhage, 50m of the small and medium scale and 100m of the large scale were injured. Structural damage was 200m in small scale, 300m in medium scale and 500m in large scale. The optimum height of the vent stack is 4.7 m in small scale, 8.8 m in medium scale and 16.9 m in large scale.

• 한국지역지리학회:학술대회
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• 2009.08a
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• pp.108-115
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• 2009

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmentaldescription of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapor Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapor Explosion), Fireball and so on, among them.we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.94-101
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• 2016

Satellite data for remote sensing technology has limitations, especially with visible range sensor, cloud and/or other environmental factors cause missing data. In this study, using land surface temperature data from the MODerate resolution Imaging Spectro-radiometer(MODIS), we developed retrieving methods for satellite missing data and developed three methods; mean bias, regression analysis and local variation method. These methods used the previous day data as reference data. In order to validate these methods, we selected a specific measurement ratio using artificial missing data from 2014 to 2015. The local variation method showed low accuracy with root mean square error(RMSE) more than 2 K in some cases, and the regression analysis method showed reliable results in most cases with small RMSE values, 1.13 K, approximately. RMSE with the mean bias method was similar to RMSE with the regression analysis method, 1.32 K, approximately.

• Atmosphere
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• v.23 no.4
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• pp.453-470
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• 2013

As a part of the KIAPS Observation Processing System (KOPS), we have developed the modules of satellite radiance data pre-processing and quality control, which include observation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extracted using the BUFR (Binary Universal Form for the Representation of meteorological data) decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminated by large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for different surface types since the errors from the skin temperature are caused by inaccurate surface emissivity. Correcting the bias caused by errors in the instruments and radiative transfer model is crucial in radiance data pre-processing. We have developed bias correction modules in two steps based on 30-day innovation statistics (observed radiance minus background; O-B). The scan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictors has been employed to correct the airmass bias.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.3-5
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• 2008

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmental description of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapour Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapour Explosion), Fireball and so on, among them, we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• Journal of Environmental Science International
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• v.13 no.12
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• pp.1079-1088
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• 2004

In order to understand chemical characteristics and dewfall formation in western Busan area, we analysed monthly distribution of dewfall, and investigated the correlation between dewfall formation amount and meteoro­logical factors. This study used the modified teflon plate $(1m{\times}1m)$ at Silla university in Busan from August 2002 to April 2003. In order to estimate qualitatively water soluble components, IC, ICP and UV methods for water soluble ions are also used respectively. Dewfall amount of sampling periods (47 day) collected 3.8 mm. Meteorological conditions for the formation of dewfall above $50\;g/m^{2}$ showed that temperature diurnal $range(^{\circ}C)\;was\;5.6^{\circ}C$ above, cloud amounts (1/10) at dawn of the sampling day was 7/10 below, mean wind speed at dawn (0~6hr) of the sampling day was 4.4 m/sec below, and mixing ratio at 6hr of the sampling day was 3.2 g/kg above. Distribution of water soluble ions in dewfall founded the highest concentration (206.1\;{\mu}eq/{\ell}\;for\;SO_{4}^{2-},\;42.4\;{\mu}eq/{\ell}\;for\;NH_{4}^{+},\;249.2\;{\mu}eq/{\ell}\;for\;Ca^{2+},\;and\;42.0\;{\mu}eq/{\ell}\;for\;Mg^{2+})$ during the March, the lowest concentration $(73.0\;{\mu}eq/{\ell}\;for\;SO_{4}^{2-},\;4.6\;{\mu}eq/{\ell}\;for\;NH_{4}^+\;and\;72.7\;{\mu}eq/{\ell}\;for\;Ca^{2+})$ during the August. Monthly equivalent ratio of $[SO_{4}^{2-}]/[NO_{3}^-]$ showed the highest value (4.99) during the October, the lowest value (1.84) during the August, and the mean value was 3.45.

• Atmosphere
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• v.27 no.2
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• pp.119-131
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• 2017

We present and discuss the Tropopause Folding Turbulence Detection (TFTD) algorithm for the Korean Communication, Ocean, Meteorological Satellite (COMS) which is originally developed for the Tropopause Folding Turbulence Product (TFTP) from the Geostationary Operational Environmental Satellite (GOES)-R. The TFTD algorithm assumes that the tropopause folding is linked to the Clear Air Turbulence (CAT), and thereby the tropopause folding areas are detected from the rapid spatial gradients of the upper tropospheric specific humidity. The Layer Averaged Specific Humidity (LASH) is used to represent the upper tropospheric specific humidity calculated using COMS $6.7{\mu}m$ water vapor channel and ERA-interim reanalysis temperature at 300, 400, and 500 hPa. The comparison of LASH with the numerical model specific humidity shows a strong negative correlation of 80% or more. We apply the single threshold, which is determined from sensitivity analysis, for cloud-clearing to overcome strong gradient of LASH at the edge of clouds. The tropopause break lines are detected from the location of strong LASH-gradient using the Canny edge detection based on the image processing technique. The tropopause folding area is defined by expanding the break lines by 2-degree positive gradient direction. The validations of COMS TFTD is performed with Pilot Reports (PIREPs) filtered out Convective Induced Turbulence (CIT) from Dec 2013 to Nov 2014 over the South Korea. The score test shows 0.49 PODy (Probability of Detection 'Yes') and 0.64 PODn (Probability of Detection 'No'). Low POD results from various kinds of CAT reported from PIREPs and the characteristics of high sensitivity in edge detection algorithm.