• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.36-44
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• 2006

In this study, we constructed a forest fire danger map in the Yeongdong area of Gangwon-do and Northeastern area of Gyeongsangbuk-do using a forest fire rating model and geographical information system (GIS). We investigated the appropriate positions of the automatic weather station (AWS) and a comprehensive network solution (a system including measurement, communication and data processing) for the establishment of an optimum mountain meteorological observation network system (MMONS). Also, we suggested a possible plan for combining the MMONS with unmanned monitoring camera systems and wireless relay towers operated by local governments and the Korea Forest Service for prevention of forest fire.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.195-210
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• 2016

The purpose of this study was to understand characteristics of groups vulnerable to extreme heat and to reduce mortality caused by high temperature. For this purpose, relationship between socioeconomic factors and mortality-threshold temperatures were studied. The study area was limited to Seoul (South Korea) and climate data from 2000 to 2010 was used. Our results indicate that mortality-threshold temperatures for regions with a high proportion of aging population and a low proportion of aging population are $27.6^{\circ}C$ and $27.9^{\circ}C$, respectively. It was also found that a relative size of welfare dependant population did not affect mortality-threshold temperatures. However, regions with a high proportion of aging and welfare dependant population experienced $0.7^{\circ}C$ lower mortality-threshold temperature than other regions. This implies that low income and older people in Seoul are more easily affected by high temperature. Thus, this study suggests that it needs a policy targeted to low income and aging population to decrease mortality rate caused by extreme heat.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.72-78
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• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.11-19
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• 2012

Air-borne pollen, biogenically created aerosol particle, influences Earth's radiative balance, visibility impairment, and human health. The importance of pollens has resulted in numerous experimental studies aimed at characterizing their dispersion and transport, as well as health effects. There is, however, limited scientific information concerning the optical properties of airborne pollen particles contributing to total ambient aerosols. In this study, for the first time, optical characteristics of pollen such as aerosol backscattering coefficient, aerosol extinction coefficient, and depolarization ratio at 532 nm and their effect to the atmospheric aerosol were studied by lidar remotes sensing technique. Dual-Lidar observations were carried out at the Gwangju Institute of Science & Technology (GIST) located in Gwagnju, Korea ($35.15^{\circ}E$, $126.53^{\circ}N$) for a spring pollen event from 5 to 7 May 2009. The pollen concentration was measured at the rooftop of Gwangju Bohoon hospital where the building is located 1.0 km apart from lidar site by using Burkard trap sampler. During intensive observation period, high pollen concentration was detected as 1360, 2696, and $1952m^{-3}$ in 5, 6, and 7 May, and increased lidar return signal below 1.5km altitude. Pollen optical depth retrieved from depolarization ratio was 0.036, 0.021, and 0.019 in 5, 6, and 7 May, respectively. Pollen particles mainly detected in daytime resulting increased aerosol optical depth and decrease of Angstrom exponent.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.1
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• pp.11-21
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• 2014

The purpose of this study was to furnish basic information for forest community ecology and to accumulate vegetational datum related to hierarchy of forest community for the efficient management of forest vegetation in Mt. Mani. Samples were collected and analyzed by 32 releves from August to October in 2010 using phytosociological analysis methodology of Z-M school and importance value analysis. The results were summarized that the forest vegetation was classified into 5 units in total. Importance value at vegetation unit 1 indicated Pinus densiflora 54.31 (18.10%), Quercus mongolica 39.21 (13.07%), Carpinus coreana 37.29 (12.43%), at vegetation unit 2 Quercus mongolica 89.43 (22.23%), Rhododendron mucronulatum 57.75 (14.43%), Carpinus coreana 47.19(11.80%), at vegetation unit 3 Styrax japonica 53.97 (13.50%), Acer mono 33.60 (8.40%), Carpinus coreana 26.48 (6.62%), Quercus serrata 22.51 (5.64%), at vegetation unit 4 Carpinus coreana 47.70 (11.92%), Quercus acutissima 38.40 (9.60%) and at vegetation unit 5 Evodia daniellii 80.59 (20.14%), Robinia pseudoacacia 35.00 (8.74%), Pueraria thunbergiana 28.63 (7.15%), Quercus dentata 28.20 (7.05%) in the order, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.317-324
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• 2015

GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (T_m), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.