• 대기
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• 제26권2호
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• pp.243-256
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• 2016

Total number concentration of aerosols larger than 10 nm ($N_{CN10}$), 3 nm ($N_{CN3}$), and cloud condensation nuclei ($N_{CCN}$) were measured during four different ship cruises over the Yellow Sea. Average values of $N_{CN10}$ and $N_{CCN}$ at 0.6% supersaturation were 6914 and $3353cm^{-3}$, respectively, and the minimum value of $N_{CN10}$ was $2000cm^{-3}$, suggesting significant anthropogenic influence even at relatively clean marine environment. Although $N_{CN10}$ and $N_{CN3}$ increased near the coast due to anthropogenic influence, $N_{CCN}$ was relatively constant and therefore $N_{CCN}/N_{CN10}$ ratio tended to decrease, suggesting that coastal aerosols were relatively less hygroscopic. In general $N_{CN10}$, $N_{CN3}$, and $N_{CCN}$ during the cruises seemed to be significantly influenced by wet scavenging effects (e.g. fog) and boundary layer height variation. Only one new particle formation (NPF) event was observed during the measurement period. Interestingly, the NPF event occurred during a dust storm event and spatial scale of the NPF event was estimated to be larger than 100 km. These results demonstrate that aerosol and CCN concentration over the Yellow Sea can vary due to various different factors.

• 한국해안·해양공학회논문집
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• 제24권5호
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• pp.333-342
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• 2012

파랑 모델에서 시공간적인 해상도의 변경은 결과에 많은 영향을 끼친다. 본 연구에서는 모델 입력장의 해상도 변경, 파랑 모델의 해상도 변경 그리고 물리적 옵션에 따른 모델의 민감도를 분석하였다. 모델의 분석을 위해서 전지구 부이관측자료와 위성자료를 이용하였다. 고해상도의 입력장을 사용할 경우 유의파고를 과대 산정하는 경향을 보였고, RSME는 약간 감소하였다. 고해상도의 파랑 모델을 수행할 경우 평균편향 및 RMSE가 약간 증가하였다. 또한, 유효 해상풍 계수를 기존의 값인 1.4보다 작게 설정할 경우 편향과 RMSE가 모두 감소하였다.

• 통합자연과학논문집
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• 제17권1호
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• pp.1-12
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• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• 한국연초학회지
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• 제29권1호
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• pp.1-7
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• 2007

This study was conducted in order to predict the chemical and physical properties by climatic factors during the growing season of flue-cured tobacco as soon as possible. The data of eight chemical and five physical properties were collected from "Analysis of physical and chemical properties on farm leaf tobacco" conducted at KT&G Central Research Institute from 1987 through 2006. Data of climatic factors from April to July in 10 districts were collected from Korea Meteorological Adminstration. Except for yellowness(b), all probabilities of linear regression equations between the climatic factors(X) and the average contents of twelve grades(whole plant) for chemical and physical properties(Y) were significant($P{\leq}0.05$). The predicable probabilities within ${\pm}20%$ range of difference were 100% in ether extract content, in nicotine content, and in filling value, 90% in total nitrogen content, and 70% in total sugar content. These results suggest that the regression equations may be useful to predict the average content of twelve grades for eight chemical and four physical properties by climatic factors during the growing season of flue-cured tobacco at the beginning of August.

• 한국연초학회지
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• 제29권1호
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• pp.8-13
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• 2007

This study was conducted in order to predict the chemical and physical properties by climatic factors during the growing season of burley tobacco as soon as possible. The data of six chemical and five physical properties were collected from "Analysis of chemical and physical properties on farm leaf tobacco" conducted at KT&G Central Research Institute from 1987 to 2006. Data of climatic factors from April to July in 6 districts were collected from Korea Meteorological Adminstration. Except for total nitrogen, total nitrogen/nicotine and yellowness(b), all probabilities of linear regression equations between the climatic factors(X) and the average contents of twelve grades(whole plant) for chemical and physical properties(Y) were significant($P{\leq}0.05$). The predicable probabilities within ${\pm}20%$ range of difference were 100% in ether extract content, 95% in nicotine content, and 90% in filling value. These results suggest that the regression equations may be useful to predict the average content of twelve grades for four chemical and four physical properties by climatic factors during the growing season of burley tobacco at the beginning of August.

• 한국수자원학회논문집
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• 제54권3호
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• pp.145-156
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• 2021

우리나라와 세계 곳곳에서는 대형 산불이 빈번하게 발생하고 있으며, 이로 인한 피해가 증가하고 있다. 우리나라에서 산불은 대부분 입산자 실화, 소각 산불 등의 인위적인 원인으로 발생하지만, 기온과 습도, 풍속 등의 기상인자는 산불의 연소 환경에 큰 영향을 미친다. 본 연구에서는 최근 5년간 경기도 지역에서 발생한 산불을 바탕으로, 산불 발생에 영향을 미치는 요인으로 온도, 습도, 풍속, 강수, 가뭄 요인을 선정하여 이들 간의 인과관계를 정량적으로 평가하였다. 분석을 위한 기법으로 인과관계의 발견 및 잠재변수의 예측에 적합한 부분최소제곱 구조방정식모형을 활용하였다. 연구 모형의 평가 결과, 본 연구에서 구축한 측정모형과 구조모형은 6가지 평가기준 모두에서 통계적으로 유의한 것으로 나타났다. 영향정도를 표준화된 경로계수로 표현하면, 기상학적 요인인 습도, 온도 그리고 풍속은 산불 발생에 각각 -0.42, 0.23, 0.15만큼의 영향을 나타내며, 수문학적 요인인 가뭄은 산불 발생에 0.23만큼의 영향요인으로 작용하는 것으로 나타났다. 따라서 본 연구는 실제 적용가능한 방법으로써 산불 영향요인의 분석과 이에 대한 평가, 그리고, 산불 재난의 대응·대비 계획 수립에 활용될 수 있을 것이다.

• Asian Journal of Atmospheric Environment
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• 제12권3호
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• pp.244-254
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• 2018

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard ($50{\mu}g/m^3$) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60-80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average $PM_{2.5}$ concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.

• 신재생에너지
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• 제20권1호
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• pp.79-87
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• 2024

This study introduces a method for estimating Aerosol Optical Depth (AOD) using Broadband Aerosol Optical Depth (BAOD) derived from direct normal irradiance and meteorological factors observed between 2016 and 2017. Through correlation analyses between BAOD and atmospheric components such as Rayleigh scattering, water vapor, and tropospheric nitrogen dioxide, significant relationships were identified, enabling accurate AOD estimation. The methodology demonstrated high correlation coefficients and low Root Mean Square Errors (RMSE) compared to actual AOD₅₀₀ measurements, indicating that the attenuation effects of water vapor and the direct impact of tropospheric nitrogen dioxide concentration are crucial for precise aerosol optical depth estimation. The application of BAOD for estimating AOD₅₀₀ across various time scales-hourly, daily, and monthly-showed the approach's robustness in understanding aerosol distributions and their optical properties, with a high coefficient of determination (0.96) for monthly average AOD₅₀₀ estimates. This study simplifies the aerosol monitoring process and enhances the accuracy and reliability of AOD estimations, offering valuable insights into aerosol research and its implications for climate modeling and air quality assessment. The findings underscore the viability of using BAOD as a surrogate for direct AOD₅₀₀ measurements, presenting a promising avenue for more accessible and accurate aerosol monitoring practices, crucial for improving our understanding of aerosol dynamics and their environmental impacts.

• 한국지구과학회지
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• 제40권6호
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• pp.599-605
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• 2019

작물 생육에 영향 요소인 기상 변수들을 이용하여 우리나라 쌀 생산량(kg 10a^-1)을 추정하였다. 이 연구는 기상 변수의 연 변동성을 기반으로 간단하지만 효과적인 통계 방법인 다중회귀모형을 이용하여 쌀 생산량에 대한 예측 가능성을 살펴보았다. 비균질적인 환경 조건의 특성을 고려하여, 연 쌀 생산량을 우리나라 도별로 추정하고 검증하였다. 기상청에서 제공하는 1986년부터 2018년까지 33년간 관측된 61개지점의 월 평균 기상 자료를 설명자료로 사용하였다. 11겹 교차검증(11-fold cross-validation)을 이용하여 추정된 쌀 생산량의 정확도를 추정하였다. 분석한 결과, 상관계수(0.7) 측면에서 간단한 과정으로도 도별 쌀 생산량의 시간적 변화를 잘 모의하였다. 또한 추정된 쌀 생산량은 0.7 kg 10a^-1 (0.15%)의 평균 오차를 가지며, 관측의 공간적 특성을 잘 모의하였다. 이 방법은 적시에 농업기상 예측 정보를 얻는다면 쌀 생산량에 대한 유용한 정보를 사전에 얻을 수 있을 것으로 생각된다.

• 한국컴퓨터정보학회논문지
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• 제29권4호
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• pp.177-182
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• 2024

소비자의 많은 의사결정은 제품에 대한 감정적 반응에 일어나며, 마케팅에서는 이러한 다듬어지지 않은 반응을 정서(Affect)라고 칭한다. 정서에는 평가(evaluation), 무드(mood), 감정(emotion)과 같은 반응 유형도 있으나, 무의식적인 변화를 의미하는 감성(Sensibility) 또한 이에 포함된다. 선행 연구들에 따르면 기상요소들은 사람들의 이러한 감성에 영향을 미치며, 이는 기상요소들이 소비자들의 의사결정에 영향을 미칠 수 있음을 나타낸다. 본 연구에서는 기상정보와 SNS상의 텍스트마이닝을 통한 위치기반 텍스트 정보를 활용, 기상정보를 통한 감성 예측 모형을 만들어내고, 이러한 감성 모형이 실제 온라인 쇼핑몰 구매자들의 주소 정보와 매칭한 구매자들의 구매 시기 날씨 정보와 결합하여, 기상요소들이 구매자들의 구매 빈도에 미치는 영향을 살펴본다. 본 연구 결과 일 강수량, 합계 일조시간, 평균 지면 온도, 평균 상대습도를 사용한 모형이 온라인 구매 행위 빈도에 유의미한 결과를 나타내었다.