• Atmosphere
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• v.29 no.2
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• pp.183-196
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• 2019

High-quality and high-resolution meteorological information is essential to reduce damages due to disastrous weather phenomena such as flash flood, strong wind, and heat/cold waves. There are many meteorological observation stations operated by Korea Meteorological Administration (KMA) in Seoul Metropolitan Area (SMA). Nonetheless, they are still not enough to represent small-scale weather phenomena like convective storm cells due to its poor resolution, especially over urban areas with high-rise buildings and complex land use. In this study, feasibilities to use additional pre-existing networks (e.g., operated by local government and private company) are tested by investigating the effects of network density on the gridded horizontal distribution of two meteorological variables (temperature and precipitation). Two heat wave event days and two precipitation events are chosen, respectively. And the automatic weather station (AWS) networks operated by KMA, local-government, and SKTechX in Incheon area are used. It is found that as network density increases, correlation coefficients between the interpolated values with a horizontal resolution of 350 m and observed data also become large. The range of correlation coefficients with respect to the network density shows large in nighttime rather than in daytime for temperature. While, the range does not depend on the time of day, but on the precipitation type and horizontal distribution of convection cells. This study suggests that temperature and precipitation sensors should be added at points with large horizontal inhomogeneity of land use or topography to represent the horizontal features with a resolution higher than 350 m.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.561-569
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• 2010

In this study, the impact of climate change on extreme drought events is investigated by comparing drought severity-area-duration curves under present and future climate. The depth-area-duration analysis for characterizing an extreme precipitation event provides a basis for analysing drought events when storm depth is replaced by an appropriate measure of drought severity. In our climate-change impact experiments, the future monthly precipitation time series is based on a KMA regional climate model which has a $27km{\times}27km$ spatial resolution, and the drought severity is computed using the standardized precipitation index. As a result, agricultural drought risk is likely to increase especially in short duration, while hydrologic drought risk will greatly increase in all durations. Such results indicate that a climate change vulnerability assessment for present water resources supply system is urgent.

• Atmosphere
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• v.25 no.1
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• pp.99-115
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• 2015

To investigate the influences of the observational nudging and the analysis nudging on the WRF simulation for the heavy snowfall event in Yeongdong area on 26 February 2012, the sensitivity experiments in relation to nudging effects were conducted. We initially set the magnitude of nudging coefficient of $6.0{\times}10^{-4}s^{-1}$ to apply to the analysis nudging experiments and observational experiments. To select the optimized options for the observational nudging, the radius influence experiment was carried out with radii ranging from 10 to 25 km at 5 km intervals. Among the observational nudging experiments, the experiment, which was conducted with the option of the radius influence of 15 km and that of the nudging coefficient of $6.0{\times}10^{-4}s^{-1}$ (ONG exp.), showed a best result. As giving the nudging effect only directly on D1 and D2 brought about a better result for the analysis nudging, we set the analysis nudging experiment as above (ANG exp.). We compared and analyzed the results from the control experiment, ONG experiment, and ANG experiment to reveal nudging effects. It was found that the control experiment brought about a result that it overestimated its precipitation in comparison with the observation and failed to properly simulate the time zone of rainfall concentration. When either of the two nudging (observational and analysis nudging) was applied to the data assimilation, it brought about a better result than the control experiment. Especially the observational nudging led to a meaningful result for the wind field, while the analysis nudging had the best result for the precipitation distribution among the experiments.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.246-251
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• 2007

The purpose of this study is to develop a grid based model for calculating the critical nonpoint source (NPS) pollution load (BOD, TN, TP) in Nak-dong area in South Korea. In the last two decades, NPS pollution has become a topic for research that resulted in the development of numerous modeling techniques. Watershed researchers need to be able to emphasis on the characterization of water quality, including NPS pollution loads estimates. Geographic Information System (GIS) has been designed for the assessment of NPS pollution in a watershed. It uses different data such as DEM, precipitation, stream network, discharge, and land use data sets and utilizes a grid representation of a watershed for the approximation of average annual pollution loads and concentrations. The difficulty in traditional NPS modeling is the problem of identifying sources and quantifying the loads. This research is intended to investigate the correlation of NPS pollution concentrations with land uses in a watershed by calculating Expected Mean Concentrations (EMC). This work was accomplished using a grid based modelling technique that encompasses three stages. The first step includes estimating runoff grid by means of the precipitation grid and runoff coefficient. The second step is deriving the gird based model for calculating NPS pollution loads. The last step is validating the gird based model with traditional pollution loads calculation by applying statistical t-test method. The results on real data, illustrate the merits of the grid based modelling approach. Therefore, this model investigates a method of estimating and simulating point loads along with the spatially distributed NPS pollution loads. The pollutant concentration from local runoff is supposed to be directly related to land use in the region and is not considered to vary from event to event or within areas of similar land uses. By consideration of this point, it is anticipated that a single mean estimated pollutant concentration is assigned to all land uses rather than taking into account unique concentrations for different soil types, crops, and so on.

• Journal of Ecology and Environment
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• v.44 no.3
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• pp.115-125
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• 2020

Background: The phenomenon of tree dieback in forest ecosystems around the world, which is known to be associated with high temperatures that occur simultaneously with drought, has received much attention. Korea is experiencing a rapid rise in temperature relative to other regions. Particularly in the growth of evergreen conifers, temperature increases in winter and spring can have great influence. In recent years, there have been reports of group dieback of Pinus densiflora trees in Korea, and many studies are being conducted to identify the causes. However, research on techniques to diagnose and monitor drought stress in forest ecosystems on local and regional scales has been lacking. Results: In this study, we developed and evaluated an index to identify drought and high-temperature vulnerability in Pinus densiflora forests. We found the Drought Stress Index (DSI) that we developed to be effective in generally assessing the drought-reactive physiology of trees. During 2001-2016, in Korea, we refined the index and produced DSI data from a 1 × 1-km unit grid spanning the entire country. We found that the DSI data correlated with the event data of Pinus densiflora mass dieback compiled in this study. The average DSI value at times of occurrence of Pinus densiflora group dieback was 0.6, which was notably higher than during times of nonoccurrence. Conclusions: Our combination of the Standard Precipitation Index and growing degree days evolved and short- and long-term effects into a new index by which we found meaningful results using dieback event data. Topographical and biological factors and climate data should be considered to improve the DSI. This study serves as the first step in developing an even more robust index to monitor the vulnerability of forest ecosystems in Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.135-143
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• 2020

Natural drought is a three-dimensional phenomenon that simultaneously evolves in time and space. To evaluate the link between meteorological and hydrological droughts, we defined a drought event from a three-dimensional perspective and analyzed the propagation characteristics in time and spaces. Overall results indicated that 77 % of the total cases of spatio-temporal droughts was propagated based on the single category relationship between meteorological and hydrological drought events, while 23 % was affected by multiple meteorological drought events to the occurrence of hydrological drougts. Especially, it turned out that the hydrological drought was caused by the spatio-temporal effects of the propagation of four meteorological drought events generated due to long-term lack of precipitation in 1994-1995. In addition, the meteorological drought caused by the lack of precipitation in the summer of 2001 lasted for several months, and was propagated to the hydrological drought in April 2002.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Water for future
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• v.28 no.6
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• pp.193-202
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• 1995

The subjective research attempts to apply a rainfall-runoff model capable of considering time-variation of soil water contents which are highly correlated to the river flows on the qpqyungchang river basin and to evaluate its performance for flow forecasting. The model used in this study is a physically-based conceptual time-continuous model, which is composed of the Sacramento soil moisture accounting model and the nonlinear multiple conceptual reservoirs model. The daily precipitation and evaporation data for 7 years and for 3 years were used for the parameter estimation and the model verification, respectively. As a result, the flows including a significant flood event were well simulated, and the cross-correlation coefficient between observed flows and computed flows for the verification periods was 0.87, but in general computed flows were underestimated for the low-flow periods. Also, the effects of precipitation and soil water content to the river flows were analysed for the flood and the drought.

• Bulletin of the Korean Space Science Society
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• 2006.10a
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• pp.52-52
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• 2006

Many spacecraft failures and anomalies have been attributed to energetic electrons in the Earth's magnetosphere. While the dynamics of these electrons have been studied extensively for several decades, the fundamental question of how they are accelerated is not fully resolved. Proposed theories have not been successful in explaining fast high energy increase such as REE (Relativistic electron enhancement). In this presentation, we show observations of energetic electron precipitation measured by the Korean satellite, STSAT-1 which simultaneously detect (100ev - 20 keV) and (170 - 360 keV) energy electrons at the 680 km orbit, when the RES event observed at the geosynchronous orbit on October 13, 2004. STSAT-1 observed intense electron precipitation in both energy ranges occurred in the midnight sector clearly demonstrating that electrons having wide energy band are injected from the plasma sheet. To make the balance between loss and injection, the injected electron flux should be also large. In this situation, the injected electrons can be trapped into the magnetosphere and produce REE, though they have low e-folding energies. We propose this plasma sheet injection might be the primary source of relativistic electron (1 MeV) flux increases.

• Water for future
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• v.25 no.4
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• pp.51-59
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• 1992

Recently the pysically based precipitation model was developed by Geogakakos and Bras(1984) for the storm event. This is a modified version of the model. In a different way from the model, in this paper, it is emphasized that the hyderometeor size distribution(HSD)is subject to rainfall intensity and effects on the productivity of precipitation. The to HSD functions are applied to the equation of the outflow after mass through the cloud top and base, products of rainfall rate at the ground level, storage of cloud layer. As an input we put the meterological data observed at Chonju in Korea in our models and adjust the parameters included in it. The result show that in the model there is significant deviation between the hourly calculated rainfall rate and the observed data, while it is very small in the our model based on the two HSD.