• Journal of Environmental Science International
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• v.16 no.10
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1B
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• pp.71-80
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• 2010

This study aimed to figure out the effect of urbanization on meteorological variables (air temperature, wind speed, relative humidity, solar radiation and precipitation) and reference evapotranspiration (RET). The research area of 6 urban areas and 6 rural areas near each urban area was selected. The monthly average daily data were collected from 12 ground stations operated by Korea Meteorological Administration (KMA) and the changes in climate variables were analyzed. Results of annual analysis have shown that the reference evapotranspiration (RET) tends to increase in urban areas while decreasing in rural areas. In particular, due to rising RET in urban areas and decreasing RET in rural areas, we can infer that the urbanization has affected to the RET. Results of monthly analysis showed that the urbanization has effects on the RET of July compared to other months (January, April and October). The yearly and monthly effects of urbanization on RET were closely related to solar radiation, relative humidity and change in temperature, and related to wind speed.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.887-900
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• 2012

Drought is a non-negligible disaster of nature and it is mainly caused by rainfall shortage for a long time though there are many definitions of drought. 'Standard Precipitation Index' (SPI) that is widely used to express the level of meteorological drought intensity has a limit of not being able to consider the hydrological changes such as rainfall and evapotranspiration caused by climate change, because it does not consider the temperature-related variables other than the precipitation. Recently, however, 'Standardized Precipitation Evapotranspiration Index' (SPEI), a drought index of new concept which is similar to SPI but can reflect the effect of temperature variability as well as the rainfall change caused by climate variation, was developed. In this study, the changes of drought occurrence in South Korea were analyzed by applying SPEI for meteorological data (1973~2011) of 60 climate observatories under Korea Meteorological Administration (KMA). As the result of application, both of SPI and SPEI showed the trend of deepening drought in spring and winter and mitigating drought in summer for the entire nation, with SPI showing greater drought intensity than SPI. Also, SPI and SPEI with 12 months of duration showed that severe droughts with low frequency of around 6 years are generally being repeated.

• Journal of Environmental Science International
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• v.18 no.5
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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.

• Journal of Environmental Science International
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• v.22 no.12
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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.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.562-568
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• 2014

There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.