• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.117-120
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• 2006

Heavy rainfall events are occurred exceedingly various forms by a complex interaction between synoptic, dynamic and atmospheric stability. As the results, quantitative precipitation forecast is extraordinary difficult because it happens locally in a short time and has a strong spatial and temporal variations. GOES-9 imagery data provides continuous observations of the clouds in time and space at the right resolution. In this study, an power-law type algorithm(KAE: Korea auto estimator) for estimating rainfall based on the rainfall type was developed using geostationary meteorological satellite data. GOES-9 imagery and automatic weather station(AWS) measurements data were used for the classification of rainfall types and the development of estimation algorithm. Subjective and objective classification of rainfall types using GOES-9 imagery data and AWS measurements data showed that most of heavy rainfalls are occurred by the convective and mired type. Statistical analysis between AWS rainfall and GOES-IR data according to the rainfall types showed that estimation of rainfall amount using satellite data could be possible only for the convective and mixed type rainfall. The quality of KAE in estimating the rainfall amount and rainfall area is similar or slightly superior to the National Environmental Satellite Data and Information Service's auto-estimator(NESDIS AE), especially for the multi cell convective and mixed type heavy rainfalls. Also the high estimated level is denoted on the mature stage as well as decaying stages of rainfall system.

• Journal of the Korean GEO-environmental Society
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• v.19 no.12
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• pp.15-23
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• 2018

The occurrence of soil erosions in Korea is mostly driven by flowing water which has a close relationship with rainfalls. The soil eroded by rainfalls flows into and deposits in the river and it polluted the water resources and making the rivers become difficult to be managed. Recently, the frequency of heavy rainfall events that are more than 30 mm/hr has been increasing in Korea due to the influence of climate change, which creating a favourable condition for the occurrence of soil erosion within a short time. In this study, we proposed a method to estimate the distribution of rainfall intensity and to calculate the energy produced by a single rainfall event using the cumulative distribution function that take into account of the physical characteristics of rainfall. The raindrops kinetic energy estimated by the proposed method are compared with the measured data from the previous studies and it is noticed that the raindrops kinetic energy estimated by the rainfall intensity variation is very similar to the results concluded from the previous studies. In order to develop an equation for estimating rainfall kinetic energy, rainfall particle size data measured at a rainfall intensity of 0.254~152.4 mm/hr were used. The rainfall kinetic energy estimated by applying the cumulative distribution function tended to increase in the form of a power function in the relation of rainfall intensity. Based on the equation obtained from this relationship, the rainfall kinetic energy of 1~80 mm/hr rainfall intensity was estimated to be $0.03{\sim}48.26Jm^{-2}mm^{-1}$. Based on the relationship between rainfall intensity and rainfall energy, rainfall kinetic energy equation is proposed as a power function form and it is expected that it can be used in the design of short-term operated facility such as the sizing of sedimentation basin that requires prediction of soil loss by a single rainfall event.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.995-1008
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• 2005

In this study, the principal components of rainfall in Korea are extracted by a method which consists of the independent component analysis combined with the wavelet transform, to examine the spatial correlation between seasonal rainfalls and global sea surface temperatures (SSTs). The 2-8 year band retains a strong wavelet power spectrum and the low frequency characteristics are shown by the wavelet analysis. The independent component analysis is performed by using the Scale Average Wavelet Power(SAWP) that is estimated by wavelet analysis. Interannual-interdecadal variation is the dominant variation, and an increasing trend is observed in the spring and summer seasons. The relationships between principal components of rainfall in the spring/summer seasons and SSTs existed in Indian and Pacific Oceans. Particularly, the SST zones, which represent a statistically significant correlation are located in the Philippine offshore and Australia offshore. Also, the three month leading SSTs in the same region we strongly correlated with the rainfall. Hence, these results propose a promising possibility of seasonal rainfall prediction by SST predictors.

• Journal of the Korean Solar Energy Society
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• v.38 no.4
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• pp.43-54
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• 2018

In this study, we assessed dependency of small hydropower potentials on the two different runoff such as the estimated runoff based on the rainfall amounts and measured runoff. The hydpropower potentials were evaluated using actural power generations taken from Deoksong, Hanseok, and Socheon small hydropower plants over Han and Nakdong river basins, respectively. As a result of comparing the actual power generation amount with the potential amount based on the rainfall amount and the estimated amount based on the observed flow amount by each small hydroelectric power plant, the degree of latent small hydro energy by the observed flow was confirmed to be high. It is confirmed that the potential hydroelectric power generation rate is estimated to be about average 30%Point higher than the actual generation amount as a result of the measured flow rate rather than using the rainfall amount. Based on this, a method for improving the degree of the actual generation amount is proposed.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of the Korean Geographical Society
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• v.28 no.3
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• pp.193-199
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• 1993

The interannual fluctuation, trends and perio-dicties in summer rainfall of South Korea were analyzed primarily by using Mann-Kendall rank method and Power Spectrum analysis for the period from 1920 to 1985. Their relations to Indian summer monsoon rainfall have also been examined. Increasing or decreasing rainfall tendencies are not found in South Korea. In Power Spectrum alalysis, 2.5 years periods are predominent at the 95 per cent confidence level in south Korea as a whole and Pusan. Also the period of 11.0 years is found in Seou. There are another prominent spectral peaks at 2.4, 3.1, 6.2 and 7.3 years period, which are significant at 90 per cent confidence level.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.57-67
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• 2023

Slope failures during rainfall have been observed in mountainous areas of South Korea as a result of the presence of solar power facilities. The seepage behavior and pore pressure distribution differ from typical slopes due to the presence of impermeable solar panels, and the load imposed by the solar power structures also affects the slope behavior. This study aims to develop a method for evaluating the stability of slopes with solar power facilities and to analyze vulnerable points by considering the maximum slope displacement. To assess the slope stability and predict behavior while considering rainfall seepage, a combined seepage analysis and finite difference method numerical analysis were employed. For the selected site, various variables were assumed, including parameters related to the Soil Water Characteristic Curve, strength parameters that satisfy the Mohr-Coulomb failure criterion, soil properties, and topographic factors such as slope angle and bedrock depth. The factors with the most significant influence on the factor of safety (FOS) were identified. The presence of solar power facilities was found to affect the seepage distribution and FOS, resulting in a decreasing trend due to rainfall seepage. The maximum displacement points were concentrated near the upper (crest) and lower (toe) sections of the slope.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.189-190
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• 2022

Power plants that produce electricity through thermal power plants mainly store coal in outdoor storage yards. In particular, coal is directly exposed to rainwater during rainfall, including torrential rain. There is no separate drainage facility in the outdoor coal yard, and coal is simply stored on the ground. Accordingly, during rainfall, coal dust flows down by rainwater, and a large amount of rainwater that is not drained overflows the outdoor coal yard, overflowing the surrounding facilities and causing environmental pollution. Therefore, in this study, a drainage system was developed for the rapid drainage of an outdoor coal mine, and the quality of the water was evaluated when rainwater mixed with drainage characteristics and coal dust was discharged through the drainage system.

• Land and Housing Review
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• v.15 no.2
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• pp.57-71
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• 2024

This study aims to develop an evaluation method for solar power facilities considering disaster impacts and to analyse the vulnerabilities of existing facilities. Haenam-gun in Jeollanam-do, where the reassessment of existing facilities is urgent, was selected as the study area. To evaluate the vulnerability from a more objective perspective, principal component analysis and entropy methods were utilised. Seven vulnerability assessment indicators were selected: maximum hourly rainfall, maximum wind speed, number of typhoon occurrence days, number of rainfall days lasting more than five days, maximum daily rainfall, impermeable area ratio, and population density. Among these, maximum hourly rainfall, maximum wind speed, maximum daily rainfall, and number of rainfall days lasting more than five days were found to have the highest weights. The overlay of the derived weights showed that the southeastern regions of Haenam-eup and Bukil-myeon were classified as Grade 1 and 2, whereas the northern regions of Hwawon-myeon, Sani-myeon, and Munnae-myeon were classified as Grade 4 and 5, indicating differences in vulnerability. Of the 2,133 facilities evaluated, 91.1% were classified as Grade 3 or higher, indicating a generally favourable condition. However, there were more Grade 1 facilities than Grade 2, highlighting the need for countermeasures. This study is significant in that it evaluates solar power facilities considering urban disaster resilience and is expected to be used as a basic resource for the installation of new facilities or the management and operation of existing ones.

• Atmosphere
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• v.17 no.2
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• pp.115-123
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• 2007

The present study uses the GOES IR brightness temperature to examine the temporal and spatial variability of cloud activity over the region $25^{\circ}N-45^{\circ}N$, $105^{\circ}E-135^{\circ}E$ and analyzes the coherence of eastern Asian summer season rainfall in Weather Research and Forecast (WRF) model. Time-longitude diagram of the time period from June to July 2005 shows a signal of eastward propagation in the WRF model and convective index derived from GOES IR data. The rain streaks in time-latitude diagram reveal coherence during the experiment period. Diurnal and synoptic scales are evident in the power spectrum of the time series of convective index and WRF rainfall. The diurnal cycle of early morning rainfall in the WRF model agrees with GOES IR data in the Korean Peninsula, but the afternoon convection observed by satellite observation in China is not consistent with the WRF rainfall which is represented at the dawn. Although there are errors in strength and timing of convection, the model predicts a coherent tendency of rainfall occurrence during summer season.