• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone 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 and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Atmosphere
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• v.17 no.1
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• pp.45-53
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• 2007

In order to investigate the impacts of heat wave on human health, cluster analysis of meteorological elements (e.g., temperature, dewpoint, sea level pressure, visibility, cloud amount, and wind components) for identifying offensive synoptic air masses is employed. Meteorological data at Seoul during the past 30 years are used. The daily death data at Seoul are also employed. Occurrence frequency of heat waves which is defined by daily maximum temperature greater than the threshold temperature (i.e., $31.2^{\circ}C$) was analyzed. The result shows that the frequency and duration of heat waves at Seoul are increasing during the past 30 years. In addition, the increasing trend of the frequency and duration clearly appears in late spring and early autumn as well as summer. Factor analysis shows that 65.1% of the total variance can be explained by 4 components which are linearly independent. Eight clusters (or synoptic air masses) were classified and found to be optimal for representing the summertime air masses at Seoul, Korea. The results exhibit that cluster-mean values of meteorological variables of an offensive air mass (or cluster) are closely correlated with the observed and standardized deaths.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.421-426
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• 2016

Significant warming by anthropogenic influences over Korea is analyzed using CMIP5 projections (monthly mean, maximum and minimum temperatures) from RCP 8.5, 4.5, and 2.6 scenarios. Time of emergence (TOE) in JJA and DJF is chosen as the year when the magnitude of warming against the natural climate variability satisfies S/N>2 in 80% of the models in this study. Significant emergence in JJA is expected to appear in 2030s in three RCP scenarios, earlier than TOE in DJF. In DJF, TOE is expected to be 2040s in RCP 8.5 and is delayed in 2060s, 2080s in RCP 4.5, 2.6, respectively. Later emergence in low emission scenarios implies an importance of climate change mitigation consistent with previous studies. Maximum and minimum temperatures show similar results to the case of mean temperature. ToE is found to be affected by the amplitude of natural variability by season, variables and model spread, which requires further understanding.

• Journal of Environmental Science International
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• v.21 no.9
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• pp.1097-1113
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• 2012

Ozone is the secondary photochemical pollutant formed from ozone precursor such as nitrogen dioxide and non-methane volatile organic compounds(VOCs). The ambient concentration of ozone depends on several factors: sunshine intensity, atmospheric convection, the height of the thermal inversion layer, concentrations of nitrogen oxides and VOCs. Busan is located in the southeast coastal area of Korea so the ozone concentration of Busan is mainly affected from the meteorological variables related to the sea such as sea breeze. In this study the ozone concentrations of Busan in 2008~2010 were used to analyse the cause of the regional ozone difference in eastern area of Busan. The average ozone concentration of Youngsuri was highest in Busan however the average ozone concentration of Gijang was equal to the average ozone concentration of Busan in 2008~2010. The two sites are located in eastern area of Busan but the distance of two sites is only 9km. To find the reason for the difference of ozone concentration between Youngsuri and Gijang, the meteorological variables in two sites were analyzed. For the analysis of meteorological variables the atmospheric numerical model WRF(Weather Research and Forecasting) was used at the day of the maximum and minimum difference in the ozone concentration at the two sites. As a result of analysis, when the boundary layer height was lower and the sea breeze was weaker in Youngsuri, the ozone concentration of Youngsuri was high. Furthermore when the sea breeze blew from the south in the eastern area of Busan, the sea breeze at Youngsuri turned into the southeast and the intensity of sea breeze was weaker because of the mountain in the southern region of Youngsuri. In that case, the difference of ozone concentration between Youngsuri and Gijang was considerable.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1117-1125
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• 2012

The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.315-318
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• 2007

In order to reduce the amount of damage from natural disasters, we needs prevention meteorological database classified into the cause of disaster, damage elements etc. For this, we have analyzed four data, such as Statistical yearbook of calamities issued by the National Emergency Management Agency and Annual Climatological Report issued by the Korea Meteorological Administration and Recently 10 years for natural disaster damage and Statistics Yearbook from the Ministry of Government Administration and Human affairs. Through the analysis of disaster data, we have selected input variables, such as causes and elements, occurrence frequencies, vulnerable areas of natural disaster, etc. In order to reduce damage from natural disaster, the prevention activities and forecasting based on meteorological parameters and damage datas are required. In addition, it is necessary to process meteorological information for disaster prevention activities. Through these procedure, we have established the foundation of database about natural disasters. This database will be used to assess the natural disasters and build risk model and natural disasters mitigation plan.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.185-189
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• 2010

A micro-pulse LIDAR system (MPL) was employed to measure the aerosol over Pudong, Shanghai from July 2008 to January 2009. Based on Fernald method, aerosol optical variables such as extinction coefficient were retrieved and analyzed. Results show that aerosol exists mainly in low layers; aerosol loading reaches its maximum in the afternoon, and then decreases with time until its minimum at night. Most of the aerosol concentrates in the layer below 3 km, and optical extinction coefficient in the layer below 2 km contributes 84.25% of that below 6 km. Two extinction coefficient peaks appear in the near surface layer up to 500 m and in the level around 1000 m. Aerosol extinction coefficient shows a seasonal downward trend from summer to winter.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.3
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• pp.257-266
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• 1999

In order to analyze the effect of meteorological elements on ozone concentration, we carried out cross-correlation of the elements with ozone concentraton, and time series analysis on them. As a result, it revealed that temperature, wind speed and humidity are not independent variables with ozone concentrations, and also, solar radiation and mixing height are the major elements that affect them. We developed models for ozone with solar radiation and mixing height as dependent variables to verify the effect of major meteorological elements. The predicted ozone concentration has strong correlation coefficients, So, We could conclude that we can predict ozone concentreation only with solar raidation and mixing height as dependents.

• Journal of Environmental Science International
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• v.5 no.5
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• pp.657-662
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• 1996

Meteorological and flux data measured from semiarid watersheds (Lucky Hills and Kendall) during the summer rainy and winter periods were used to study the sensitivity of the those variables used in the estimation of evapotranspiration rates. Relative sensitivity was examined to compare the importance of four meteorological and flux variables (net radiation wind speed, air temperature, and relative humidity) on Penman potential evapotranspiration (PET) estimation. The study results show that variations in Penman PET rates during the summer rainy period at both watersheds appears to be controlled by air temperature adn net radiation. During the winter period at both watersheds, wariations in Penman PET rates appears to be controlled by relative humidity and air temperature.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1087-1095
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• 2015

Influenza, commonly known as "the flu", is an infectious disease caused by the influenza virus. We consider, in this paper, regression models as a prediction model of influenza disease. While most of previous researches use mainly the meteorological variables as a predictive variables, we consider social media information in the models. As a result, we found that the contributions of two-type of informations are comparable. We used the medical treatment data of influenza provided by Natioal Health Insurance Survice (NHIS) and the meteorological data provided by Korea Meteorological Administration (KMA). We collect social media information (twitter buzz amount) from Twitter. Time series model is also considered for comparison.