• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.3
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• pp.366-379
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• 2015

Objectives: The objective of this study is to assess airborne particulate matter(PM) pollution and its effect on health of residents living near Ansim Briquette Fuel Complex in Daegu metropolitan region. Methods: The California Puff(CALPUFF) dispersion model, version 5.8, which can estimate the dispersion direction and range of airborn $PM_{10}$ was used to determine the possible areas affected by $PM_{10}$ pollutants emitted from Ansim briquette fuel complex. The CALPUFF modeling with 200 m grid-cell resolution was performed based on $PM_{10}$ emissions estimated from the amount of coal consumption in the fuel complex for four months in 2012. The Weather Research and Forecasting(WRF) fields were processed using CALMET to produce CALPUFF-ready meteorological inputs. Also, the distance from Ansim Briquette Fuel Complex to the residence of each environmental pneumoconiosis patient was analyzed. In addition, the affecting region of the pollutants emitted from briquette factories in Ansim Briquette Fuel Complex was determined. Results: CALPUFF modeling results showed that the highest concentrations of $PM_{10}$ were found near around the fuel complex. The modeled $PM_{10}$ distributions were characterized by significant decreases in concentration with distance from the complex. Seasonally, the highest concentration of $45{\mu}g/m^3$ was calculated in October which was mostly due to the distinct variation of amount of emission. Additional modeling with the maximum $PM_{10}$ emission of about 88 tons per year in 1986 showed that the highest concentration in October was nearly increased by 8 times than the concentration modeled with emission of 2010. As a result of medical examination and interviews for the residents in Ansim Briquette Fuel Complex and its surroundings, 8 environmental pneumoconiosis patients were found. These patients do not have occupational exposure and history. These patients have lived 0.3~1.1 km area in Ansim Briquette Fuel Complex and its surroundings. Conclusions: Airborne particles emitted from Ansim Briquette Fuel Complex can contribute to significant increase in $PM_{10}$ concentration in residential areas near around the complex. Especially, the residents near fuel complex may exposed to the pollutants emitted from the factories in Ansim Briquette Fuel Complex.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.