• Journal of the Korean Data and Information Science Society
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• 제20권6호
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• pp.1085-1092
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• 2009

지표오존 농도는 국가의 중요한 환경 척도 중의 하나이다. 본 연구에서는 경기도 파주시 오존농도를 자기회귀오차모형과 신경망모형으로 분석하였다. 오존 분석을 위한 설명변수로는 이산화황, 이산화질소, 일산화탄소, 프로메툼10 등의 대기자료와 일 최고온도, 풍속, 상대습도, 강수량, 이슬점온도, 운량, 수증기압 등의 기상자료를 사용하였다. 분석 결과 전반적으로 신경망모형이 좋은 모형으로 나타났고, 자기회귀오차모형도 오존에 영향을 주는 설명변수를 첨가하면 좋은 모형이 될 것으로 생각된다.

• 대기
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• 제17권1호
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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.

• 한국기후변화학회지
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• 제7권4호
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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.

• 한국환경과학회지
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• 제21권9호
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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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• 제23권6호
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• pp.1117-1125
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• 2012

식중독 발생에 대한 기존 연구에서는 기온과 습도와 같은 기후변수가 주된 설명변수로 취급되어 왔다. 이 논문에서는 주별 식중독 발생건수와 기후변수 간에 관계를 고찰하고 식중독 발생건수를 예측하기 위한 모형으로 포아송 회귀모형과 자기회귀이동평균모형을 비교한다. 비교결과 우리나라 식중독 발생은 시차를 두고 기후 변수에 영향을 많이 받고 있으나 식중독 발생 예측은 이들 변수보다 이전 시점의 식중독 발생 건수에 더 많이 영향을 받는 것으로 나타났으며 포아송 회귀모형은 예측의 관점에서 문제가 있음을 보였다.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2007년도 정기총회 및 학술발표대회
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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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• 제14권3호
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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.

• 한국대기환경학회지
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• 제15권3호
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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.

• 한국환경과학회지
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• 제5권5호
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• pp.657-662
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• 1996

여름과 겨울철에 준건조 유역들 (Lucky Hills and Kendall)로부터 측정되어진 기상학적 그리고 flux data를 이용하여 증발산 산정을 위한 변수들의 민감도를 연구하였다. 상대적 민감도 분석을 이용하여 Pen농an의 잠재증발산 산정에 필요한 네 가지 기상학적 그리고 flux변수(순방사, 풍속, 공기온도 그리고 상대습도)들의 중요도가 검증되어졌다. 두 다른 유역으로부터의 연구 결과에 의하면, 여름철에 Pen-mim의 잠재증발산략의 변화는 공기 온도와 순방사에 의해서 지배되어지고, 겨울철에는 상대습도와 공기온도에 의해서 지배되어지는 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제26권5호
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• pp.1087-1095
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• 2015

인플루엔자는 흔히 독감으로 불리는 질병으로 인플루엔자 바이러스가 호흡기 (코, 인후, 기관지, 폐 등)에 감염되어 생기는 병이다. 감기와는 달리 심한 증상을 나타내거나 생명이 위험한 합병증 (폐렴 등)을 유발할 수도 있다. 본 연구에서는 인플루엔자에 대한 예측모형을 다루었으며, 주로 회귀적인 모형을 고려하였다. 기존의 연구들이 주로 기상요인을 예측변수로 사용한 반면, 본 연구에서는 소셜요인의 효과를 살펴보았으며 그 결과 기상요인과 대등한 설명력을 가짐을 확인하였다. 반응변수로는 국민건강보험공단에서 제공하는 인플루엔자 진료건수가 사용되었고, 설명변수에는 기상청에서 제공하는 기상정보와 트위터에서의 인플루엔자 연관키워드 빈도가 사용되었다. 모형의 비교를 위해 시계열 모형도 함께 제시되었다.