• 대기
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• 제23권4호
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• pp.389-400
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• 2013

Chugugi and Wootaeck rainfall data of Gyeonggi Province from 1830 to 1893 were restored from the "Gaksadeungnok" that is the government records between the central government and the local during the Joseon Dynasty. The restored data periods were 27, 10, 9 and 14 years for Kwangju, Suwon, Ganghwa and Gaeseong, and the total number of restored data was 655 for the Chugugi and 427 for the Wootaek, respectively. The variation pattern of monthly rainfall by Chugugi was investigated and it showed that the monthly rainfall more than 300 mm was recorded 25 times with 18 times in July, 5 times in August and 3 times in June. The cases of more than 500 mm were also recorded 8 times with the maximum 787 mm at the Kwangju in July 1862, showing the similar pattern to Seoul. The monthly mean rainfall for the Gyeonggi Province were 259 mm in July, 204 mm in August and 121 mm in June, which were about one third of that of Seoul. The correlation analysis between the Chugugi and Wootaek data was carried out to derive the quantitative values of Wootaek observations. It revealed that 1 'Ri' of Wootaek observation was equal to approximately 1 'Chon (Chugugi unit)' or 20 mm, while 1 'Seo' was very variable between 2 and 6 'Boon (Chugugi unit)' with the median value approximately 3 'Boon' or 6 mm. Recalculated Wootaek data showed that the monthly rainfall in July, August, and June were 289 mm, 154 mm, and 124 mm, respectively. Through this study, some features of the rainfall variation pattern during 1830~1893 were figured out, and quantitative interpretation of Wootaek data became possible based on the restored rainfall data from the "Gaksadeungnok". Though many pages of the book have been lost during the last hundreds years, "Gaksadeungnok" is still very meaningful and of practical use, for it contains plenty of the local data throughout the whole country during the latter part of Joseon Dynasty. Therefore, further studies are strongly recommended on the restoration of climate related data and on the climatic tendency of 19th century of Korean peninsular.

• 한국지리정보학회지
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• 제25권2호
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• pp.1-17
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• 2022

본 연구는 경상남도 창원시 국가산업단지 인근에 설치된 21개의 PM_2.5 간이 측정기에서2020년 6월부터 2021년 5월까지 1년 동안 측정된 자료를 활용하여 PM_2.5의 발생 패턴을 분석하였다. PM_2.5의 발생 패턴은 측정지점 주변의 토지이용현황과 기온 및 풍속의 기상적인 요인을 고려하였다. PM_2.5 농도는 계절별로는 겨울철인 11월부터 3월까지, 시간대별로는 새벽과 이른 아침인 1시부터 9시까지가 높았다. PM_2.5는 공업지역에 인접할수록 농도가 높았으나, 주거지역과 공공시설지역은 농도가 낮았다. 기상적인 요인에서는 높은 기온과 풍속일수록 PM_2.5의 농도는 낮았기 때문에 기상 상태는 PM_2.5의 확산에 영향을 미치는 것으로 판단된다. 본 연구의 결과는 창원국가산업단지 인근의 PM_2.5 발생 패턴을 파악할 수 있었다. 이 결과는 향후 도시지역의 PM_2.5를 포함한 대기질을 개선하기 위해 도시 및 환경계획에서 활용할 수 있는 유용한 자료가 될 것이다.

• 한국지구과학회지
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• 제32권4호
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• pp.360-372
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• 2011

백두산 화산분출물의 이류와 확산 예측 특성을 살펴보기 위하여 3차원 대기역학 모형인 WRF와 입자 확산 모형인 FLEXPART를 결합하여 다양한 수치실험을 실시하였다. 기상자료의 시간 평균화에 따른 영향을 보기 위하여 네가지 서로 다른 평균화 기간을 가진 수치 실험을 실시하였다. 본 연구에 적용된 기상자료의 평균화 기간은 각각 1개월, 10일, 3일이다. 또한 평균화를 실시하지 않은 실시간자료를 이용한 수치실험도 실시하였다. 기상자료의 평균화 시간이 길어질수록 주풍성분인 동서 방향의 운동이 뚜렷해지고, 짧을수록 주풍의 법선 방향인 남북 운동이 명확하게 나타나며, 화산분출물을 가정한 입자의 확산 역시 동일한 특성이 나타난다. 상하층의 바람은 강도의 차이가 명확하고, 평준화 기간에 따른 영향이 다르게 나타나기 때문에 상층입자와 하층입자의 이동과 지상 침적이 다양하게 나타난다. 또한 한반도의 지상 침적은 방출고도 2 km 이하의 입자가 주로 영향을 미친다. 따라서 기상자료의 평균화 시간 간격이 화산분출물의 지상 침적을 결정하는 하나의 요인으로 작용하기 때문에, 확산 실험 전에 적절한 기상자료의 시간평균을 결정할 필요가 있다.

• 한국환경과학회지
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• 제11권12호
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• pp.1235-1242
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• 2002

The purpose of this study is to estimate wet deposition flux and to investigate wet deposition characteristics by using the ADOM model. Wet deposition flux of highly reactive $SO_2$ is estimated by applying observed meteorological parameters and concentrations of chemical species to the ADOM model. Wet deposition is largely dependent on large scale precipitation and cloud thickness. Wet deposition flux of sulfate depends on $SO_2$ oxidation in clouds. When large amount of $SO_2$ is converted to sulfate, deposition flux of sulfate increases, but wet deposition flux of $SO_2$ is small. On the whole, the pattern of sulfate wet deposition flux agrees with the typical pattern of sulfate wet deposition that is high in the summer(July) and low in the winter(January).

• 전기학회논문지
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• 제65권6호
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• pp.934-939
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• 2016

This paper made short-term electric load forecasting by using temperature data at three-hour intervals (9am, 12pm, 3pm, and 6pm) provided by the Korea Meteorological Administration (KMA). In addition, the electric power pattern was created using existing electric power data, and temperature sensitivity was derived using temperature and electric power data. We made power load forecasting program using LabVIEW, a graphic language.

• Membrane and Water Treatment
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• 제10권1호
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• pp.39-44
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• 2019

LID facilities do not consider environmental factors, and due to inappropriate vegetation planting causing degradation in efficiency due to plant damage and difficulty in maintenance. Therefore, in this study, assessment of impact environmental factor by seasonal variation of chlorophyll and growth of vegetation planted in LID technologies and change of pollutant reduction were conducted. In the case of B-SJ and B-RI, growth rate decreased after summer (August), and B-MG showed steady growth until autumn (September). Chlorophyll was found to increase during spring season while it decreased during autumn season. The chlorophyll concentration was found to affect the plant growth pattern. TN reduction efficiency was highest with greater than 80% efficiency in summer, and it was analyzed that plants were identified as the main factor affecting the seasonal reduction efficiency of TN. Also, temperature and relative humidity were analyzed to affect plant growth, activity and pollutant removal efficiency. Plant type and growth pattern are considered as factors to be considered in selection of appropriate plant types in LID technologies.

• 한국대기환경학회지
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• 제20권5호
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• pp.671-683
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• 2004

This study was carried out to understand long-range transport of $SO_2$ using aircraft measurements for the identification of it's horizontal and vertical concentration and distribution pattern. Thirteen missions of aircraft measurements have been done around 37$^{\circ}$00'/124$^{\circ}$30' from October 1997 to November 2001. Concentrations of $SO_2$ was 1.5~2.0 ppb in the below mixing layer, 0.6~1.1 ppb in the above mixing layer. $SO_2$ was found to be relatively higher than marine background level, 0.08~0.2ppb, indicating the western coast being influenced by long-range transport except for the summer season. The vertical distribution of $SO_2$ was classified into 3 groups using its vertical sounding and meteorology pattern; the first is linear decay pattern, the second is exponential decay pattern, and the last is gaussian distribution pattern in the below mixing layer, 2 patterns of linear decay and gaussian distribution patterns in the upper layer. It is founded that vertical distribution pattern is strongly dependent on meteorological condition, for example atmospheric stability and predominant air flow.

• 대기
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• 제24권2호
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• pp.131-140
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• 2014

The "Barents Oscillation (BO)", first designated by Paul Skeie (2000), is an anomalous recurring atmospheric circulation pattern of high relevance for the climate of the Nordic Seas and Siberia, which is defined as the second Emperical Orthogonal Function (EOF) of monthly winter sea level pressure (SLP) anomalies, where the leading EOF is the Arctic Oscillation (AO). BO, however, did not attracted much interest. In recent two decades, variability of BO tends to increase. In this study, we analyzed the spatio-temporal structures of Atmospheric internal modes such as Arctic Oscillation (AO) and Barents Oscillation (BO) and examined how these are related with Arctic warming in recent decade. We identified various aspects of BO, not dealt in Skeie (2000), such as upper-level circulation and surface characteristics for extended period including recent decade and examined link with other surface variables such as sea-ice and sea surface temperature. From the results, it was shown that the BO showed more regionally confined spatial pattern compared to AO and has intensified during recent decade. The regional dipolelar structure centered at Barents sea and Siberia was revealed in both sea-level pressure and 500 hPa geopotential height. Also, BO showed a stronger link (correlation) with sea-ice and sea surface temperature especially over Barents-Kara seas suggesting it is playing an important role for recent Arctic amplification. BO also showed high correlation with Ural Blocking Index (UBI), which measures seasonal activity of Ural blocking. Since Ural blocking is known as a major component of Eurasian winter monsoon and can be linked to extreme weathers, we suggest deeper understanding of BO can provide a missing link between recent Arctic amplification and increase in extreme weathers in midlatitude in recent decades.

• 전기학회논문지
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• 제63권5호
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• 전기학회논문지
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• 제67권10호
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.