• 한국농공학회지
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• 제30권3호
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• pp.95-105
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for irrigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. And the objective or this study is to develop a Daily Irrigation Reservoir Operation Model(DIROM) combining the inflow and the release models which depicts the daily water level fluctuations of an irrigation reservoir, and to evaluate the applicability of the model. DIROM was applied to four reservoirs and daily water levels were simulated and compared to the observed data. The model behaviour was also compared with that of a ten - day based model, Reservoir Operation Study(ROS) which has been applied for determining the design capacity of reservoirs. Various combinations of measured and simulated inflow and release rates for tested reservoirs were used to define the daily water level fluctuations. Simulated release rates and measured inflow data resulted in larger errors, and simulated inflow and release rates produced the smallest errors in water level comparison. Two resevoir operation models, DIROM and ROS were applied to the same reservoir and the simulation results compared. The computational errors of DIROM ware smaller than those of ROS, and DIROM was more sensitive to meteorological conditions. DIROM demonstrated its potenial applicability in water management and operation.

• 대기
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• 제21권4호
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• pp.447-454
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• 2011

As dust storms originated in Neimongu Plateau and Manchuria became more frequent in Korea, there was a growing need for Asian Dust (Hwangsa) monitoring stations in North Korea, which is a pathway of Asian Dust to South Korea. The South Korean and the North Korean Governments agreed to build the Automatic Weather System and the PM10 measurement instruments in the Gaeseong Industrial Zone and the Keumgangsan Tourist Region, North Korea in 2007. PM10 mass concentration data in the Keumgangsan Tourist Region could be collected only during the period from September 2007 to May 2008. In this study, daily, monthly and diurnal variations of PM10 mass concentration of the Keumgangsan are analyzed and compared with those of Sokcho and Gwangdeoksan. Three sites show similar variations in daily and monthly means. Correlation coefficients (r) between Sokcho and Keumgangsan, and between Gwangdeoksan and Keumgangsan are 0.89 and 0.67, respectively. But diurnal variation at Keumgangsan has a distinct feature compared to the other sites. Diurnal PM10 variation shows two peaks around 8 AM and 4-5 PM and very low at night. The difference between the daily maximum and minimum is $20{\sim}60{\mu}g\;m^{-3}$ during September to November 2007. Temperature, relative humidity and wind speed from the Keumgangsan AWS data were compared with those from the Changjon station, and showed good correlation each other except wind speed.

• 천문학회보
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• 제37권2호
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• pp.123.2-123.2
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• 2012

We investigate the solar flare occurrence rate and daily flare probability in terms of the sunspot classification supplemented with sunspot area and its changes. For this we use the NOAA active region data and GOES solar flare data for 15 years (from January 1996 to December 2010). We consider the most flare-productive eleven sunspot classes in the McIntosh sunspot group classification. Sunspot area and its changes can be a proxy of magnetic flux and its emergence/cancellation, respectively. We classify each sunspot group into two sub-groups by its area: "Large" and "Small". In addition, for each group, we classify it into three sub-groups according to sunspot area changes: "Decrease", "Steady", and "Increase". As a result, in the case of compact groups, their flare occurrence rates and daily flare probabilities noticeably increase with sunspot group area. We also find that the flare occurrence rates and daily flare probabilities for the "Increase" sub-groups are noticeably higher than those for the other sub-groups. In case of the (M+X)-class flares in the 'Dkc' group, the flare occurrence rate of the "Increase" sub-group is three times higher than that of the "Steady" sub-group. Our results statistically demonstrate that magnetic flux and its emergence enhance the occurrence of major solar flares.

• 한국농공학회지
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• 제30권2호
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• pp.95-104
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for inigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. And the objective of this study is to develop a reservoir release rate model and then to calibrata the parameters. The release rates model considers daily water demands , water supply for transplanting, minmum release for maintaining canal flow, and maxirnun and regular flooding depth for determining effective rainfall on paddy fields. Each of the factors in the model was regarded as a lumped pararuter representing the average condition of a whole irrigated area. The water demand was estimated form the potential evapotranspiration by Penman method, the effective rainfall, and the infiltration on paddy fields. The release model was found to be capable of adequately simulating daily reservoir releases based on meteorological data.

• 복식문화연구
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• 제25권2호
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• pp.117-129
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• 2017

The purpose of this study was to investigate meteorological factors' effects on clothing sales based on empirical data from a leading apparel company. The daily sales data were aggregated from "A" company's store records for the Fall/Winter season from 2012 to 2015. Daily weather data corresponding to sales volume data were collected from the Korea Meteorological Administration. The weekend effect and meteorological factors including temperature, wind, humidity, rainfall, fine dust, sea level pressure, and sunshine hours were selected as independent variables to calculate their effects on A company's apparel sales volume. The analysis used a SAS program including correlation analysis, t-test, and multiple-regression analysis. The study results were: First, the weekend effect was the most influential factor affecting sales volume, followed by fine dust and temperature. Second, there were significant differences in the independent variables'effects on sales volume according to the garments' classification. Third, temperature significantly affected outer garments'sales volume, while top garments' sales volume was not influenced significantly. Fourth, humidity, sea level pressure and sunshine affected sales volume partly according to the garments' item. This study can provide proof of significant relationships between meteorological factors and the sales volume of garments, which will serve well to establish better inventory strategies.

• 한국환경과학회지
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• 제18권2호
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• pp.129-139
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• 2009

Development of an artificial neural network model was presented to predict the daily maximum $SO_2$ concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using $SO_2$ potential parameters estimated from meteorological and air quality data which are closely related to daily maximum $SO_2$ concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the $SO_2$ potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high $SO_2$ concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum $SO_2$ at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum $SO_2$ concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.

• 대기
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• 제29권5호
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• pp.599-607
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• 2019

The change and periodicity of Korean winter temperature in the period 1979-2018 are investigated. It is shown that the winter temperature is on a long-term rise, with two regime shifts of winter temperature during 40 years. In addition, the decrease in cold days is confirmed along with the rise in temperature. Analysis of the periodicity of daily temperature in winter is carried out by means of power spectral analysis. Of the spectral peaks that are statistically significant, the most frequent detection exists on the time scale between 7 and 8 days. It is found that the number of significant periods have decreased since 2014, particularly no longer existent around the period of 7 day. The longer periods than 7 days gradually increase during 40 years, while the shorter periods show the tendency of decrease but recently rebound. Spectral analysis calculated from high/low-pass filtered daily temperature data also shows similar results.

• 대기
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• 제21권4호
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• pp.481-495
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• 2011

This study is performed to consider the threshold values of heavy rain warning in Korea using 98 surface meteorological station data and 590 Automatic Weather System stations (AWSs), damage data of National Emergency Management Agency for the period of 2005 to 2009. It is in need to arrange new criteria for heavy rain considering concept of rainfall intensity and rainfall damage to reflect the changed characteristics of rainfall according to the climate change. Rainfall values from the most frequent rainfall damage are at 30 mm/1 hr, 60 mm/3 hr, 70 mm/6 hr, and 110 mm/12 hr, respectively. The cumulative probability of damage occurrences of one in two due to heavy rain shows up at 20 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 110 mm/12 hr, respectively. When the relationship between threshold values of heavy rain warning and the possibility of rainfall damage is investigated, rainfall values for high connectivity between heavy rain warning criteria and the possibility of rainfall damage appear at 30 mm/1 hr, 50 mm/3 hr, 80 mm/6 hr, and 100 m/12 hr, respectively. It is proper to adopt the daily maximum precipitation intensity of 6 and 12 hours, because 6 hours rainfall might be include the concept of rainfall intensity for very-short-term and short-term unexpectedly happened rainfall and 12 hours rainfall could maintain the connectivity of the previous heavy rain warning system and represent long-term continuously happened rainfall. The optimum combinations of criteria for heavy rain warning of 6 and 12 hours are 80 mm/6 hr or 100 mm/12 hr, and 70 mm/6 hr or 110 mm/12 hr.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.159-168
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• 2022

The recent outbreak of COVID-19 pandemic cases around the globe has affected Saudi Arabia with around 15, 00,000 confirmed cases within the initial 4 months of transmission. The present investigation analyzed the relationship between daily COVID-19 confirmed cases and meteorological parameters in seventeen cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. The meteorological parameters used in the present investigation are temperature, humidity, dew point, and wind speed. Pearson correlation and Spearman rank correlation tests were utilized for data analysis. The incubation period of COVID-19 varies from 1 day to 14 days as per available information. Therefore, an attempt has been made to analyze the effects of meteorological factors with bins of 1, 3, 7, and 14 days. The results suggested that the highest number of correlations (15 cities) was observed for temperature (maximum, minimum, and average) and humidity (12 cities) (minimum and average). The dew point showed relationships for 7 cities and wind showed moderate correlations only for 2 cities. The study results might be useful for authorities and stakeholders in taking specific measures to combat the Covid-19 pandemic.

• 상하수도학회지
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• 제10권2호
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.