• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.5 no.3
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• pp.178-197
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• 1976

Based on the measurement data on Radiation on a Horizontal Surface at 84 different areas in South Korea which were collected and recorded by the Central Meteorological Office, the average values in the period of 5 years from 1971 thru 1975 at 12 particular areas of Gangreung, Gyeongju, Gwangju, Gimhae, Mogpo, Seoul, Iri, Jeju, Jinju, Cheongju and Chungju are indicated in this report. For above 12 areas the following data have been obtained and are indicated in each tables. Annual totalization of Radiation on a Horizontal Surface and its yearly averaged daily totalization$cdots$Table 1. Monthly totalization of Radiation on a Horizontal Surface and its monthly averaged daily totalization$cdots$Table 2 Maximum and minimum values of daily totalization of Radiation on a Horizontal Surface$cdots$Table 3 Daily variation of monthly averaged Radiation on a Horizontal Surface$cdots$Table 4 thru 15 Decade averaged Radiation on a Horizontal Surface$cdots$Table 16 thru 27

• Atmosphere
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• v.24 no.2
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.

• The Journal of Korean Academic Society of Nursing Education
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• v.15 no.2
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• pp.251-259
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• 2009

Purpose: Climate change affects human health and calls for a health forecasting service. The purpose of this study was to explore the students' actual use and their satisfaction with meteorological information and the demands on health forecasting at a university in South Kyungsang Province. Method: This study used a descriptive design through structured self-report questionnaires including frequency, contents, purpose, perception, satisfaction of meterological information and need and demand of health forecasting. Data were collected from June 1 to 5, 2009 and analyzed using the SPSS 17.0 program. Descriptive statistics, t-test, ANOVA, $\chi^2$ test and Person's correlation coefficient were used to analyze the data. Result: The majority of the students watched the daily weather information to decide about daily work, outdoor activity or habitually. The mean score of need for health forecasting was $3.44{\pm}.81$, and the demand for health forecasting was $2.93{\pm}1.05$. Significant differences were found in the need for health forecasting according to sex, major, and environmental disease. In addition, the higher the satisfaction of health forecasting, the higher the demand for it. Conclusion: I suggest improving the meteorological information system technically and developing a health forecasting service resulting in a healthier and more comfortable life.

• Journal of Environmental Health Sciences
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• v.26 no.2
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• pp.80-90
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• 2000

To determine the effect of air pollutants in Taejon City, we used medical insurance claim data for asthma and the data monitored from telemetry system of Ministry of Environment for air pollutants and meteorological factors. From November 1st, 1997 to October 31th, 1998, 95,629 asthma patients were covered by medical insurance at Taejon City. Subjects were composed of 49,563 males (51.8%) and 46.336 females(48.2%), and among the subjects, the proportion under 15 years old was 62.8% of the total. During the study period, daily mean concentrations of each air pollutants-So2, No2, CO, O3 and TSP-were 9.8 ppb, 17.6 ppm, 1.414 pp, 17.3 ppb and 60.6 $\mu\textrm{g}$/㎥, respectively, which were lower than each of the environmental recommended exposure levels, Through the simple correlation analysis between each air pollutants and meteorological factors, O3 was positively correlated with temperature, but negatively with relative humidity. TSP, SO2, NO2, CO were negatively correlated with meteorological factors, and these air pollutants except O3 were positively correlated with each other, O3 was correlated with TSP positively but the others negatively. Estimating relative risks of each air pollutant with the baseline general additive model, daily mean concentrations of TSP(Lag 0, 1, 4, 5day) were significantly associated with the increase of the asthma admission. Two age subgroups, under 15 and 15~54 have shown various association of all air pollutants concentration with the asthma admission. However, in case of over 54 age subgroup, only TSP(Lag 0 day) among all air pollutants was associated with the asthma admission. Each of ambient outdoor pollutant concentrations in Taejon City are significantly associated with the admission of asthma patients even though all concentrations in Taejon City are significantly associated with the admission of asthma patients even though all concentrations were much lower than the environmental recommended exposure levels. Therefore, continuous effort lowering air pollutant concentration and introducing an active environmental conservation policy should be implanted for preventing hazardous health effects. Considering major proportion of asthma admissions, high susceptibility and less confounding factors among the age subgroup under 15 will be a useful target population for assessing such health effect monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.135-143
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• 2020

Shortwave radiation and sunshine hours (SHOUR) are important variables having many applications, including crop growth. However, observational data for these variables have low horizontal resolution, rendering its application to related research and decision making on f arming practices challenging. In the present study, hourly solar radiation data were physically generated using the Land-Atmosphere Modeling Package (LAMP) at the National Center f or Agro-Meteorology, and then daily SHOUR fields were calculated through statistical downscaling. After data quality evaluation, including case studies, the SHOUR data were added to the existing publically accessible LAMP daily database. The LAMP daily dataset, newly updated with SHOUR, has been provided operationally as input data to the "Gyeonggi-do Agricultural Drought Prediction System," which predicts agricultural weather disasters and field crop growth status.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.3
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• pp.146-156
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• 2002

The objective of this study was to develop a modified CREAMS model for paddy field conditions. The model simulates daily balance of water and nutrient from irrigated paddies using meteorological, irrigation, and agricultural management data. The model simulates daily evapotranspiration of paddy using Penman equation and determines daily flooding depth changes. Total nitrogen and phosphorus concentrations within flooding water, surface runoff, and leaching water from a paddy field also can be simulated. Parameters of the model were calibrated using observed data of the Agricultural Experiment Station of the Seoul National University in Suwon Korea. The model was applied for the irrigation period of paddy field in Gicheon area when 1,234 mm annual rainfall was occurred. The simulated losses of the total nitrogen and total phosphorous were 11.27 kg/ha and 0.98 kg/ha, respectively. There was a good agreement between observed and simulated data. It was found that CREAMS-PADDY model was capable of predicting runoff and nutrient losses from irrigated paddy fields.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.128-134
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• 2020

The daily gridded meteorological information and climatology with high resolution (30m and 270m) was produced from 94 Automated Surface Observing System (ASOS) of Korea Meteorological Administration (KMA) for the past 50 years (1971-current) by different downscaling methods. In addition, the difference between daily meteorological data and the mean state of past 30 years (1981-2010) was calculated for the analysis of climate change. These datasets with GeoTiff format are available from the web interface (https://agecoclim. agmet.kr). The performance of the data is evaluated using 172 Automatic Weather S tation (AWS ) of Rural Development of Administration (RDA). The data have biases lower than 2.0, and root mean square errors (RMSE) lower than 3.8. This data may help to better understand the regional climatic change and its impact on agroecosystem in S outh Korea.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.765-777
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• 2009

Our study aimed to illustrate long term trend in 10 micrometer particular matters excluding confounding effect. Daily 10 micrometer particular matters data were measured in 27 places and meteorological data (maximum temperature, humidity and maximum wind speed, solar radiation) were obtained from the national institute of environmental research for the period from January, 1996 to December 2000. To estimate the increasing and decreasing long term trend in a set of observed data, set up the model. The model included regression spline smooth function on the time and meteorological factors to capture the seasonal time trend and any possible nonlinear relationship. The result was estimated to decrease slightly after adjusting for meteorological factors and seasonal time trend.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.