• Journal of Korean Society for Atmospheric Environment
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• v.23 no.3
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• pp.322-331
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• 2007

In this study, we have analyzed $PM_{10}$ concentration measured at Incheon Regional Air Monitoring Network (10 stations) and meteorological data at Incheon Weather Station to investigate factors (i.e. wind direction, wind speed, relative humidity, major meteorological phenomenon, and sea-land breezes existence) influencing $PM_{10}$ concentration in Incheon during 2005. Statistical differences among meteorological factors were assessed by Kruskal-Wallis test or Mann-Whitney U test. The main conditions causing high $PM_{10}$ concentration are summarized below; 1. When westerly wind prevailed (however, $PM_{10}$ decreased when winds were blowing from the east or north). 2. When the winds were calm, owing to accumulation of nearby emissions under stagnant conditions, or when the wind speed is in excess of 6 m/s, which shows the effect of fugitive dust produced by wind erosion. 3. Under the condition of high relative humidity and poor diffusion based on meteorological phenomenon such as fog, mist, and haze. 4. When the Sea-Land breezes existed, which occurred 70 days in Incheon during 2005 and contributed significantly to high $PM_{10}$ concentration in the coastal urban area. In conclusion, we have found that the meteorological factors have influence on $PM_{10}$ concentration in Incheon.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.233-239
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• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Journal of the Korean Society of Tobacco Science
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• v.26 no.2 s.52
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• pp.93-101
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• 2004

This study was conducted to investigate the relationship between weather factors during the growing season and chemical components of flue-cured tobacco. Chemical components used in this study was from 'Farm Leaf Tobacco Test' conducted at KT&G Central Research Institute from 1986 through 2003. Data of weather factors during growing season(April to July) were collected in 10 districts measured at Korea Meteorological Adminstration(KMA). Nicotine and total sugar contents, and total sugar to nicotine(TS/Nic.) ratio were increased, whereas total nitrogen to nicotine(TN/Nic.) ratio and chloride content were decreased from 1986 through 2003. Year to year variation of rainfall was the largest, followed by that of sunshine hour. Month to month variation of rainfall also was the largest, followed by that of mean daily air temperature(MDAT). Rainfall was correlated positively with relative humidity(RH), but negatively with sunshine hour. Nicotine content was correlated positively with MDAT(in July, June$\~$July, May$\~$July and average), but negatively with rainfall(in May$\~$July) and with RH(in June, July, May $\~$June, June $\~$July, April$\~$June, May $\~$July and average). Total sugar content was correlated positively with MDAT(in May), but negatively with sunshine hour(average) and RH(in June, July, June$\~$July, April$\~$June, May$\~$July and average). The positive correlation was found between total nitrogen content and sunshine hour(in April, May, April$\~$May, May$\~$July, April$\~$June and average). The negative correlation was found between TS/Nic. ratio and sunshine hour(in May$\~$July and average). TN/Nic. ratio was correlated positively with sunshine hour(in May and April$\~$May) and with RH(in July and June$\~$July), but negatively with MDAT(in July, June$\~$July, May$\~$July and average). Ether extraction content was correlated positively with MDAT(in July, June$\~$July, May$\~$July and average) and with sunshine hour(in July and June$\~$July), but negatively with rainfall(in April, July, May­July and average). Chloride content was correlated positively with sunshine hour(in April, July, April$\~$May, June$\~$July, April$\~$June, May$\~$July and average), but negatively with rainfall(in April, April$\~$June and average).

• Research in Plant Disease
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• v.30 no.3
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• pp.300-303
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• 2024

Since its initial outbreak in Korea in 2015, fire blight has consistently emerged annually. Fire blight outbreaks usually begin in May, peak in June, and decline in July in Korea. In this study, we analyzed cases that exhibit a distinct pattern of disease occurrence based on yearly weather conditions from 2020 to 2023. In 2020, fire bight disease occurrence began in late May. Although the disease incidence started late by the low temperatures in April, which caused flowering period delayed, the incidence increased significantly due to the high risk of blossom infection. In 2021, the first outbreak began in late April because the flower infection started in early April. In 2022, despite the high blossom infection risk during the flowering period in April and the high incidence of fire blight in May, the incidence decreased sharply from June due to the low rainfall in May. In 2023, due to torrential rains and hail in late June, the incidence of fire blight increased even in July. Considering the weather factors that affect the increase of fire blight disease, it is suggested that control measures to prevent the fire blight infection should be carried out before and after wind-driven rains.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.193-199
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• 2000

In order to provide basic information for the estimation of reference crop evapotranspiration in the greenhouse, an lysimeter experiment was performed. Kenturky Blue Grass was used as a reference crop. Relationships between measured reference crop evapotranspiration and weather factors were analyzed. A multi-regression model was developed and tested.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.107-108
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• 2018

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.223-236
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• 2020

Purpose: In this study, a strong wind damage prediction function was developed in order to be used as a contingency during disaster management (preventive-preventive-response-recovery). Method: The predicted strong wind damage function proposed in this study took into account the re-enactment boy power, weather data and local characteristics at the time of damage. The meteorological data utilized the wind speed, temperature, and damage history observed by the Korea Meteorological Administration, the disaster year, and the recovery costs, population, vinyl house area, and farm water contained in the disaster report as factors to reflect the regional characteristics. Result: The function developed in this study reflected the predicted weather factors and local characteristics based on the history of strong wind damage in the past, and the extent of damage can be predicted in a short time. Conclusion: Strong wind damage prediction functions developed in this study are believed to be available for effective disaster management, such as decision making by policy-makers, deployment of emergency personnel and disaster prevention resources.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.351-362
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• 2017

Recently, analysis techniques to extract new meanings using big data analysis and various services using them have been developed. A disaster safety service among such services has been paid attention as the most important service. In this paper, we design and implement a flood disaster safety system using real time weather big data. The proposed system retrieves and processes vast amounts of information being collected in real time. In addition, it analyzes risk factors by aggregating the collected real time and past data and then provides users with prediction information. The proposed system also provides users with the risk prediction information by processing real time data such as user messages and news, and by analyzing disaster risk factors such a typhoon and a flood. As a result, users can prepare for potential disaster safety risks through the proposed system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.4
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• pp.520-526
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• 2015

A tragic disaster happened by capsizing O-Ryong 501 trawler at Western Bering Sea in 1st, Dec. 2014. The disaster was caused by the severe weather and the long deferred escape from the storm in fully developed high sea. Lots of sea water from poop deck rushed into the fish ponder with fishes all together after hauling net and then remove the fishes from codend. The vessel became to incline to the one side caused by the weight and the free surface effect of flood sea waters and fishes at initial stage. In spite of crews all effort to discharge the waters, but the work was not achieved successfully. For the worse thing, the order of abandon ship was issued too late. After all, the ship capsized and sank, then almost crews became to the victims of the casualty including captain. In this paper, author carried out restrictively the calculation of dynamic factors influenced on the disaster including the weather condition and effects of the flood sea waters, and found out that the most important causes of the disaster were the decrease of stabilities, GM was decreased from 0.9m to 0.08 m, and the high waves which led to the vessel disaster.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1105-1113
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• 2013

Understanding the characteristics of truck-involved crashes is of keen interest because such crashes are highly associated with greater potential leading to severer injury. The purpose of this study is to identify factors affecting injury severity of truck-involved crashes on freeways. In addition, a binary logistic regression technique is applied to identify causal factors affecting truck crash severity under normal and adverse weather conditions. Major findings from the analyses are discussed with truck operations strategies including speed enforcement, variable speed limit, and truck lane restriction, from the safety enhancement point of view. The results of this study would be useful for developing traffic control and operations strategies to reduce truck-involved crashes and injury severity in practice.