• 한국환경과학회지
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• 제16권10호
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• pp.1157-1167
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• 2007

In an effort to interpret the characteristics of fine particle concentrations in Busan, time variations of hourly monitored concentrations $PM_{10}$ (Particulate Matter with aerodynamic Diameter ${\le}10\;{\mu}m$) in Busan are analyzed for the period from 2000 to 2005. The characteristics of aerosol second generation formation process is also interpreted qualitatively, by using the statistical analysis of the meteorological variables including temperature, wind speed, and relative humidity. The result shows some significant annual, seasonal, weekly and diurnal variations of $PM_{10}$ concentrations. In particular, seasonal(i.e., spring) variations are governed by frequency of yellow sand events even for the non-yellow sand cases where yellow-sand days are eliminated in our analysis. However, in seasonal variation, summer season predominate lower $PM_{10}$ concentrations due to the frequent precipitation, and weekly and diurnal variations are both found to be reflecting the emission rate from traffic amount. Correlation coefficients between $PM_{10}$ concentration and meterological variables for non-yellow sand days show overall negative correlation with visibility, wind speed, cloud amounts, and relative humidity. However for non-precipitation days, during non-yellow sand period positive correlation are found clearly with relative humidity, suggesting the importance of secondary aerosol formation in Busan that can be achieved by both homogeneous aerosol formation and heterogeneous transformations resulting from hygroscopic aerosol characteristics.

• 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.

• 대한토목학회논문집
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• 제30권1B호
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• pp.71-80
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• 2010

본 연구에서는 도시화가 기상요소(기온, 풍속, 상대습도, 일사량, 강수량)와 기준증발산에 미치는 영향을 분석하였다. 이를 위하여 연구지역을 6개의 도시지역과 도시지역 인근에 위치한 6개의 비도시화지역으로 구분하였다. 기상청에서 운영하는 12개 기상관측소에서 관측된 월평균 일 기상자료를 수집하였고, 기상요소의 변화분석을 수행하였다. 본 연구결과에 의하면 도시지역의 경우 뚜렷한 기준증발산의 증가추세를 보이고 있는 반면에 비도시화 지역의 경우 기준증발산이 감소하는 추세인 것으로 나타났다. 특히 도시지역의 기준증발산 증가추세와 비도시화 지역의 기준증발산 감소추세로 인하여 도시화가 기준증발산에 미치는 도시영향은 증가하는 것으로 나타났다. 월별자료 분석결과 여름철에 해당하는 7월의 경우 다른 계절(1월, 4월, 10월)과 비교하여 도시화가 기준증발산에 미치는 도시효과는 증가하였다. 연별 및 월별 도시화가 기준증발산에 미치는 영향은 도시화가 일사량, 상대습도 그리고 기온 변화에 미치는 영향과 밀접한 상관성이 있으며, 도시화가 풍속에 미치는 영향과는 상관성이 적은 것으로 나타났다.

• 한국수자원학회논문집
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• 제45권9호
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• pp.887-900
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• 2012

가뭄은 자연의 무시할 수 없는 재해이며, 비록 가뭄의 정의가 많이 있지만 가뭄은 장기간의 강우의 부족으로부터 기인한다. 기상학적 가뭄심도의 정도를 표현하기 위해 널리 이용되는 표준강수지수(Standardized Precipitation Index, SPI)는 강수 이외의 기온과 관련된 변수를 고려하지 않기 때문에 기후변동으로 인한 강수, 증발산 등의 물수지 변화를 고려할 수 없다는 한계점이 있다. 그러나 최근에 SPI와 유사하지만 기후변동으로 인한 강수 변화 뿐만 아니라 기온의 변동성이 미치는 영향을 반영할 수 있는 새로운 개념의 가뭄지수인 표준강수증발산지수(Standardized Precipitation Evapotranspiration Index, SPEI)가 개발되었다. 본 연구에서는 기상청 산하의 60개 기상관측소의 1973~2011년까지 기상자료를 대상으로 SPEI를 적용하여 남한지역의 가뭄발생의 변화를 평가하였다. 적용결과, 전국적으로 SPI와 SPEI 모두 봄과 겨울에 가뭄이 심화되고 여름철에는 가뭄이 완화되는 경향을 보였으며, SPEI는 SPI보다 가뭄심도를 크게 나타내었다. 또한, 지속기간 12개월의 SPI와 SPEI는 전반적으로 6년 내외의 저빈도 주기성을 갖는 극심한 가뭄이 반복되고 있음을 보였다.

• 한국환경과학회지
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• 제18권5호
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• pp.539-550
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• 2009

This study found that tropical cyclones (TCs) formed for fall in 2007 over the western North Pacific were distributed in high-latitudes comparing to 56-year (1951-2006) climatological mean. The frequency and latitude of TC genesis became higher than 56-year climatological mean from September onward in 2007 and all the TCs that formed to the north of 20$^{\circ}$N was also distributed after September in 2007. These characteristics of TC genesis for fall in 2007 could be confirmed through analyzing various variables, such as a large-scale atmospheric circulation, outgoing longwave radiation (OLR), vertical zonal wind shear, and sea surface temperature (SST). On the other hand, a frequency of the TC that occurred to the north of 200N showed a clear interdecadal variation and its decreasing trend was distinctive in recent years. Its intensity was also weaker that TCs that did to the south of 20$^{\circ}$N. However, a latitude of TC genesis showed an increasing trend until recent years, whose variation was consistent with trend that through a SST analysis, warm SST went north in recent years.

• 한국환경과학회지
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• 제22권12호
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• pp.1633-1641
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• 2013

The objective of this study was to analyze correlation between phenological characteristics of Salix spp. and meteorological factors in the Upo wetlands. Phenology of Salix subfragilis Andersson and Salix chaenomeloides Kimura was monitored from 2007 to 2012. Meteorological variables were monitored by Korea Meteorological Administration (Hap-chon). Average date of flowering, fruiting, seed dispersion was 86, 113, 136 days for S. subfragilis and 112, 140, 164 days for S. chaenomeloides as Julian days. Flowering of S. subfragilis and S. chaenomeloides were correlated with daily mean air temp. in March (r=-0.92, r=-0.85, p<0.05). Fruiting of S. subfragilis was correlated with total precipitation between Jan and March of previous year (r=-0.90, p<0.01), however, the fruiting of S. chaenomeloides was highly correlated with max. temp. in Jan of previous year (r=0.99, p<0.01). Seed dispersion of both species is correlated with min. temp. in Feb. Phenology monitoring will contribute to understanding Salix spp. response against climate change.

• 한국토양비료학회지
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• 제50권4호
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• 한국대기환경학회지
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• 제33권6호
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• pp.554-569
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• 2017

Sensitivity analysis on $PM_{10}$ forecasting simulations was carried out by using two different initial and boundary conditions of meteorological fields: NCEP/FNL (National Centers for Environmental Prediction/Final Analysis) reanlaysis data and NCEP/GFS (National Centers for Environmental Prediction/Global Forecast System) forecasting data, and the comparisons were made between two different simulations. The two results both yielded lower $PM_{10}$ concentrations than observations, with relatively lower biased results by NCEP/FNL than NCEP/GFS. We explored the detailed individual meteorological variables to associate with $PM_{10}$ prediction performance. With the results of NCEP/FNL outperforming GFS, our conclusion is that no particular significant bias was found in temperature fields between NCEP/FNL and NCEP/GFS data, while the overestimated wind speed by NCEP/GFS data influenced on the lower $PM_{10}$ concentrations simulation than NCEP/FNL, by decreasing the duration time of high-$PM_{10}$ loaded air mass over both coastal and metropolitan areas. These comparative characteristics of FNL against GFS data such as maximum 3~4 m/s weaker wind speed, $PM_{10}$ concentration control with the highest possible factor of 1.3~1.6, and one or two hour difference of peak time for each case in this study, were also reflected into the results of statistical analysis. It is implying that improving the surface wind speed fluctuation is an important controlling factor for the better prediction of $PM_{10}$ over Korean Peninsula.

• 한국대기환경학회지
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• 제26권6호
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• 한국지능시스템학회논문지
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• 제24권5호
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• pp.562-568
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• 2014

기상레이더에는 강수에코와 비강수 에코가 섞여 존재한다. 이런 모호한 지점의 판단이 난해함으로 정확한 일기 예보를 하기는 매우 어려운 일이다. 본 논문에서는 기상청 레이더의 UF 데이터로부터 데이터를 추출하였다. 설계하는 두 분류기의 입출력 데이터는 강수 에코와 비 강수 에코의 특성분석을 통해 구성된다. 더 좋은 성능을 나타나는 입력변수를 사용 하였으며, 에코분류기는 퍼지 뉴럴 네트워크를 기반으로 설계한다. 에코 판단모듈 1과 판단모듈 2를 고려하여 에코분류기의 성능 비교연구를 수행 한다.