• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.35-50
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• 2022

As the water shortage has become a noticeable issue due to climate change, forests play an importance role as the provider of water supply service. There is, however, little information about the relationships between the factors used in the estimation of water supply service and coarse pore fraction of forest soil which determines the potential of water supply. To find out whether there would be an amelioration in the scoring system of water supply service estimation, we examined all factors except meteorological one and additionally, analyzed 4 extra factors that might be related with coarse pore fraction of soil. A total of 2,214 soil samples were collected throughout South Korea to measure coarse pore fractions from 2015 to 2020. First, the result of average coarse pore fraction of all samples showed 32.98±6.59% which was consistent with previous studies. And the results of non-parametric analysis of variance indicated that only two of eleven factors that was used in the scoring system matched the results of coarse pore fraction of forest soils. Tree canopy coverage showed no difference among categories, and slope also showed no significance at level of 0.05 in the linear regression analysis. Additionally, the applicability of 4 extra factors were confirmed, as the result of coarse pore fractions of soil samples were different for various categories of each factor. Therefore, the scoring system of water supply service of forest should be revised to improve accuracy.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.92-101
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• 2016

Objectives: Outdoor workers are exposed to thermally stressful work environments. In this study, heat stress indices for harbor workers in summer were calculated to evaluate thermal comfort based on a human heat balance model. These indices are Physiological Subjective Temperature (PST), Dehydration Risk (DhR), and Overheating Risk (OhR) according to respective stage of cargo work in a harbor. In addition, we constructed a forecast system to provide heat stress information. Methods: Thermophysiological indices in this study were calculated using the MENEX model (i.e. the human heat balance model), which used as inputs the meteorological parameters, clothing insulation, and metabolic rate for each stage of cargo work in the harbor of Masan over the course of seven days, including a four-day heat wave. The forecast heat stress information constructed for Masan harbor was based on meteorological data supported by the Dong-Nae Forecast from the KMA (Korea Metrological Administration) and other input parameters. Results: According to higher metabolic rate, thermophysiological indices showed a critical level. In particular, PST was evaluated as reaching the 'Very hot' or 'Hot' level during all seven days, despite the heat occurring over only four. It is important in a regard to consider the work environment conditions (i.e. labor intensity and clothing in harbor). On a webpage, the forecast thermophysiological indices show as infographics to be easily understand. This webpage is comprised of indices for both current conditions and the forecast, with brief guidance. Conclusion: Thermophysiological indices show the risk level to health during a heat wave period. Heat stress information could help to protect the health of harbor workers. Further, this study could extend the applicability of these indices to a variety of outdoor workers in consideration of work environments.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of the Korean association of regional geographers
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• v.1 no.1
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• pp.45-60
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• 1995

The purpose of this study is to classify the Korean precipitation regions on the basis of the characteristics of extratropical cyclonic precipitation. From now on, extratropical cyclone is called cyclone in short. By using factor analysis and Ward method in cluster analysis, precipitation regions on the basis of the characteristics of cyclonic precipitation are classified The principal data used in this study are daily precipitation records obtained from 60 weather stations of the Korea Meteorological Service during the ten years($1981{\sim}1990$), and weather charts published by the Japan Meteorological Agency. The results obtained in this study are summarized as follows: (1) In the factor analysis using 43 variables which have relation to the extratropical cyclonic precipitations, They are seven factors whose eigenvalues are above 1.0. This explains 86 percent of total amount. The first factor explains the characteristics of precipitation in the middle-west area and its contribution degree has the highest 10.9 percent. (2) According to the cluster analysis method of Ward, extratropical cyclonic precipitation regions are classified seven macro regions(such as Kyungki and North Youngseo, Youngdong and Ullungdo, Hoseo and South Youngseo, Honam and Northwest Chejudo, Southeast Chejudo, North Youngnam, and South Youngnam), 22 meso regions. (3) The characteristics of precipitation regions have relations to the path of cyclone, the direction of air inflow and the strike of mountain ranges. As the conclusion, the Central China Low brings much precipitation in the southern coast and southern area of Korea as moving to the northeastward. The North China Low moves eastward and brings much precipitation in the western area of the Taeback mountain ranges. The probability of extratropical cyclonic precipitation is the lowest in the inland of Yeongnam and the eastern coastal areas which belong to the rain shadow region. Namely, The seasonal and spatial characteristics of precipitation are closely associated with the path of cyclone and the direction of air inflow according to its passage, and the strike of mountain ranges.

• Journal of Satellite, Information and Communications
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• v.7 no.3
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• pp.30-34
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• 2012

National Meteorological Satellite Center(NMSC) of Korea Meteorological Administration(KMA) has launched to implement the application development to get prepared for the space weather operation since 2010. As a action of KMA's space weather work, NMSC constructed Global Navigation Satellite System(GNSS) application system for meteorology and space weather. We will introduce NMSC's space weather application system which derives regional TEC(Total Electron Content) in near real time using nation-wide GNSS network data. First, We constructed system for collecting GNSS data, which is currently collecting about 80 stations operated by agencies like NGII(National Geographic Information Institute), Central Office of DGPS(Differential GPS), and KASI(Korea Astronomy and Space Science) including KMA's own data of 2 stations. In order to retreive regional TEC over Korean peninsular, we build up the automatic processes running every 1-hour. In these processes, firstly, GNSS data of every stations with 24 hours time window are processed to derive DCBs(Differential Code Biases) of each GNSS station and TEC values on every ionosphere piercing point(IPP). Then we made gridded regional TEC map with resolution of 0.25 degree from 31N, 121E to 41N, 135E by combination of all station results within 30 minutes window with assumption that TEC of a given point during a given 30 minutes window would have a constant value. The grid points without TEC value are interpolated using Barnes objective analysis. We presentour regional TEC maps, which can describe better on the status of ionosphere over Korean peninsular compared to IGS TEC maps.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.3
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• pp.261-269
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• 1989

Agricultural activities in Chejudo require more specialized weather services in this region. The meteorological information available from the Korea Meteorological Service (KMS) is limited in its areal coverage because the KMS stations are located along the narrow band of coastal area. topoclimatological technique which makes use of empirical relationships between the topography and the weather can be applied to produce reasonable estimates of the climatic variables such as air temperature and precipitation over remote land area where routine observations are rare. Presentation of these estimates in a from of fine-mesh grid map can also be helpful to upgrade the quality of weather services in this region. Altitude values of the 250 m grid points were read from a 1: 25000 topographic map and the mean altitude, the mean slope, and the aspect of the slope were determined for each 1 km$^2$ land area from these altitude data. Daily minimum air temperature data were collected from 18 points in Chejudo during the winter period from November 1987 to February 1988. The data were grouped into 3 sets based on synoptic pressure pattern. Departures from the KMS observations were regressed to the topographical variables to delineate empirical relationships between the local minimum temperature under specific pressure patterns and the site topography. The selected regression equations were used to calculate the daily minimum temperature for each 1 km$^2$ land area under the specific pressure patterns. The outputs were presented in a fine-mesh grid map with a 6-level contour capability.

• Journal of Positioning, Navigation, and Timing
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• v.3 no.4
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• pp.131-141
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• 2014

Over the last decade, the Global Navigation Satellite System (GNSS) has been increasingly utilized as a meteorological research tool. The Korea Astronomy and Space Science Institute (KASI) has also been developing a near real-time GNSS precipitable water vapor (PWV) information management system that can produce a precise PWV for the Korean Peninsula region using GNSS data processing and meteorological measurements. The goal of this paper is to evaluate whether the precise point positioning (PPP) strategy will be used as the new data processing strategy of the GNSS-PWV information management system. For this purpose, quality assessment has been performed by means of a comparative analysis of the troposphere zenith total delay (ZTD) estimates from KASI PPP solutions (KPS), KASI network solutions (KNS), and International GNSS Service (IGS) final troposphere products (IFTP) for ten permanent GNSS stations in the Korean Peninsula. The assessment consists largely of two steps: First, the troposphere ZTD of the KNS are compared to those of the IFTP for only DAEJ and SUWN, in which the IFTP are used as the reference. Second, the KPS are compared to the KNS for all ten GNSS stations. In this step, the KNS are used as a new reference rather than the IFTP, because it was proved in the previous step that the KNS can be a suitable reference. As a result, it was found that the ZTD values from both the KPS and the KNS followed the same overall pattern, with an RMS of 5.36 mm. When the average RMS was converted into an error of GNSS-PWV by considering the typical ratio of zenith wet delay and PWV, the GNSS-PWV error met the requirement for PWV accuracy in this application. Therefore, the PPP strategy can be used as a new data processing strategy in the near real-time GNSS-PWV information management system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.133-144
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• 2017

Recently, weather information is getting closer to our real life, and it is a very important factor especially in the transportation field. Although the damage caused by the abnormal climate changes around the world has been gradually increased and the correlation between the road risk and the possibility of traffic accidents is very high, the domestic research has been performed at the level of basic research. The Purpose of this study is to develop a risk map for the road hazard forecasting service of weather situation by linking real - time weather information and traffic information based on accident analysis data by weather factors. So, we have developed a collection and analysis about related data, processing, applying prediction models in various weather conditions and a method to provide the road hazard map for national highways and provincial roads on a web map. As a result, the road hazard map proposed in this study can be expected to be useful for road managers and users through online and mobile services in the future. In addition, information that can support safe autonomous driving by continuously archiving and providing a risk map database so as to anticipate and preemptively prepare for the risk due to meteorological factors in the autonomous driving vehicle, which is a key factor of the 4th Industrial Revolution, and this map can be expected to be fully utilized.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.137-144
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• 2007

In KORDI (Korea Ocean Research and Development Institute), the KOSC (Korea Ocean Satellite Center) construction project is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI (Geostationary Ocean Color Imager) instrument which is loaded on COMS (Communication, Ocean and Meteorological Satellite); it will be launched in 2008. Ansan (the headquarter of KORDI) has been selected for the location of KOSC between 5 proposed sites, because it has the best condition to receive radio wave. The data acquisition system is classified into antenna and RF. Antenna is designed to be $\phi$ 9m cassegrain antenna which has 19.35 G/T$(dB/^{\circ}K)$ at 1.67GHz. RF module is divided into LNA (low noise amplifier) and down converter, those are designed to send only horizontal polarization to modem. The existing building is re-designed and arranged for the KOSC operation concept; computing room, board of electricity, data processing room, operation room. Hardware and network facilities have been designed to adapt for efficiency of each functions. The distribution system which is one of the most important systems will be constructed mainly on the internet. and it is also being considered constructing outer data distribution system as a web hosting service for offering received data to user less than an hour.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.