• 한국환경과학회지
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• 제27권11호
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• pp.1129-1140
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• 2018

Temporal and spatial characteristics of the frequency of several weather types and the change in air pollutant concentrations according to these weather types were analyzed over a decade (2007-2016) in seven major cities and a remote area in Korea. This analysis was performed using hourly (or daily) observed data of weather types (e.g., mist, haze, fog, precipitation, dust, and thunder and lighting) and air pollutant criteria ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, CO, and $SO_2$). Overall, the most frequent weather type across all areas during the study period was found to be mist (39%), followed by precipitation (35%), haze (17%), and the other types (${\leq}4%$). In terms of regional frequency distributions, the highest frequency of haze (26%) was in Seoul (especially during winter and May-June), possibly due to the high population and air pollutant emission sources, while that of precipitation (47%) was in Jeju (summer and winter), due to its geographic location with the sea on four sides and a very high mountain. $PM_{10}$ concentrations for dust and haze were significantly higher in three cities (up to $250{\mu}g/m^3$ for dust in Incheon), whereas those for the other four types were relatively lower. The concentrations of $PM_{2.5}$ and its major precursor gases ($NO_2$ and $SO_2$) were higher (up to $69{\mu}g/m^3$, 48 ppb, and 16 ppb, respectively, for haze in Incheon) for haze and/or dust than for the other weather types. On the other hand, there were no distinct differences in the concentrations of $O_3$ and CO for the weather types. The overall results of this study confirm that the frequency of weather types and the related air quality depend on the geographic and environmental characteristics of the target areas.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• 제10권S_4호
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• pp.197-206
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• 2001

The long-range transport mechanisms of Asian dust were analyzed based on the synoptic weather system and numerical simulation by using NCEP/NCAR reanalysis and TOMS data during the periods of 1996-2001. We classified the whole weather types of eastern Asia during spring and created the representative weather types during the yellow sand events using cluster analysis and weather charts for the last 6 years(1996~2001). These long-range transport mechanisms were related to various pressure patterns including high and low, trough and ridge, and upper-level fronts. Case studies of the yellow sand events have performed by the simulation of MM5 with meteorological elements such as the horizontal wind of u and v component, potential temperature, potential vorticity, and vertical circulation during the episodic days(2~8 March 2001). In addition, the origin of the long-range transport was examined with the estimation of backward trajectory using HYSPLIT4 Model. In this paper, we concluded that three weather types at 1000 hPa, 850 hPa, 500 hPa, and 300 hPa levels were classified respectively. The dominant features were the extending continental outflow from China to Korea at 1000 hPa and 850 hPa levels, the deep trough passage and cold advection at 500 hPa and 300 hPa levels during the yellow sand events. And also, we confirmed the existence of pola $r_tropical jets in the upper-level, the behavior of potential vorticity over Korea, the estimation of potential vorticity through vertical cross section, and the transport of yellow sand through backward trajectories.es.

• 대기
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• 제30권3호
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 추계 학술논문 발표대회
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

• 한국빅데이터학회지
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• 제2권2호
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• pp.129-140
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• 2017

낮은 시정, 강우, 강풍, 고온 등 기상 상태는 운전 능력, 차량 성능(예: 마찰, 안정성, 조작력), 노면 마찰력, 도로 인프라, 추돌 위험, 교통 흐름 및 도로 관리자 생산성 등에 영향을 미친다. 최근에는 CCTV, 도로 센서, 차량 센서 등 다양한 도로 기상 빅데이터 소스들이 개발되면서 이러한 기상 관련 문제들 해결에 적용되고 있다. 본 연구는 이러한 도로 기상 빅데이터 소스들의 유형과 특징을 정의하고 국내외 실증 사례들을 통해 도로 기상 빅데이터 유형별로 관련 문제들 해결에 활용하는 전략에 대해 제시하고자 한다.

• 한국가스학회지
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• 제14권2호
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• pp.27-33
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• 2010

본 논문에서는 국내 가스사고의 발생 환경을 분석하여 가스사고의 재발을 방지하고자 1998년부터 2009년 6월까지 11년 6개월간의 가스사고를 유형별로 분석하였다. 가스사고는 감소하지 않고 지속적으로 발생하고 있고, 사고의 내용에서도 시기별, 날씨 등에 따라 가스사용형태가 변하고 있어 가스의 사용 환경과 가스사고는 밀접할 수밖에 없다. 가스사고는 가스사용이 많은 수도권과 특정 지자체에서 많이 발생하였고, 맑은 날 풍속이 낮고 습도가 중간정도일 때 가장 발생가능성이 높은 것으로 나타났다. 또한, 가스사고 발생 형태의 모형을 관찰한 결과, 가스로 인한 누출, 화재나 폭발 사고의 경우도 날씨와 밀접한 관계가 있을 것으로 판단되는데 이를 날씨와도 연계하여 분석해보면 발생 가능한 전체 가스사고도 예측 가능할 것으로 판단된다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.561-564
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• 2007

기상레이다는 최근에 와서 반사 신호의 강도뿐만 아니라 도플러 스펙트럼 분석을 통하여 다양한 정보를 추출함으로서 급변하는 기상현상 및 위험 등을 탐지할 수 있도록 하는 연구가 활발히 진행되어져 왔다. 이러한 목적으로 활용하기 위해서는 다양한 기상레이다 모의 신호의 구현이 필요하다. 따라서 본 논문에서에서는 기상레이다에서의 다양한 모의 신호 발생 기법에 관하여 분석하고 고찰하였다.

• 대기
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• 제20권2호
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• pp.153-160
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• 2010

In this paper, an attempt has been made to build and test self-configuring weather sensor networks and internet based observation system to gather atmospheric data. The aim is to provide integrated or real-time weather information in standard form using network data access protocol. This system was successfully developed to record weather information both digital as well as visual using sensor network and web-enabled surveillance cameras. These data were transformed by network based data access protocol to access and utilize for public domain. The competed system has been successfully utilized to monitor different types of weather. The results show that this is one of the most useful weather monitoring system.

• 식물병연구
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• 제30권1호
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• pp.34-49
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• 2024

The development of sustainable smut management techniques requires an understanding of the impacts of smut on sugarcane growth and the relationships between smut intensity and meteorological variables, varieties, and crop types. Thus, assessments were made with the objectives to 1) determine the effect of smut on the above-ground growth of sugarcane, and 2) quantify the association of smut with weather variables, varieties and crop types. The effect of smut on above-ground growth was assessed in six fields planted with NCo 334 (wider coverage) having 6 months of age in Fincha and Metehara fields in 2021. Data on above-ground growth were taken from 20 randomly selected smut-affected and healthy stools from each field. Besides, 6 years' data (2015 to 2021) on the numbers of smut-affected stools and smut whips of 79 fields were collected. Furthermore, 10 years' (2011 to 2021) weather data were acquired from the sugar plantations. The results demonstrated reduction in the above-ground growth of sugarcane in the range of 18.39% and 73.42% due to smut. In addition, weather variables explained about 68.48% and 66.58% of the variability in the number of smut-affected stools and whips respectively. Smut intensity increased with crop types for susceptible varieties. The tight association between the smut epidemic and crop types, varieties, and weather, implied that these parameters must be carefully considered in management decisions. Continuous monitoring of smut disease, meteorological variables, varieties, and crop types in all the sugarcane plantations could be done as a part of integrated smut management in the future.

• 산업경영시스템학회지
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• 제41권2호
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.