• Journal of Environmental Science International
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• v.27 no.11
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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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• v.10 no.S_4
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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.

• Atmosphere
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• v.30 no.3
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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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.

• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• Journal of the Korean Institute of Gas
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• v.14 no.2
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• pp.27-33
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• 2010

Analysis of gas accidents for 11.5 years, from 1998 to June, 2009, by types has been made in this study to prevent the recurrence of accidents through the analysis of gas accident occurring environment. Gas using environment and gas accidents are forced to be closely connected since the number of gas accidents has not only been decreased but occurred steadily and gas using types are changing by time period, weather, etc. in terms of accident contents. Gas accidents have been occurred more in capital areas with larger gas usage and specific local governments. The possibility of the gas accidents hit the highest when the weather is clear, the wind speed is low and the humidity is in the middle. In addition, leakage of gas, fire or explosion are also considered to be closely related with the weather as a result of model observance of gas accidents types. All the gas related possible accidents are also considered to be predictable if this result is to be analysed in association with the weather.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.561-564
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• 2007

Recently, The detection of weather conditions and weather related hazards with a weather radar are being actively investigated based on the echo intensity and the Doppler spectrum analysis. For this purpose, many types of simulated weather signals are needed for investigation. Therefore, this paper analyzed the method to simulate the many weather radar signals.

• Atmosphere
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• v.20 no.2
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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.

• Research in Plant Disease
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• v.30 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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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.