• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.413-427
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• 2010

Urbanization means the sudden increment of a population and the industrialization. The hydrologic water cycle causes many changes due to urbanization. Therefore, the affects that urbanization influences on the precipitation events were analyzed. But the precipitation events are very much influenced many meteorological and climatologically indices besides the effect of an urbanization. So, an analysis was performed by using precipitation data observed in many spots of the Korean peninsula. The analysis data are annual precipitation, the duration 1 daily maximum amount of precipitation, the rainy days, and 10 mm over the rainy days, and 80 mm. seasonal precipitation and seasonal rainy days. The analytical method classified 4 clusters in which the precipitation characteristic is similar through the cluster analysis. It compared and analyzed precipitation events of the urban and rural stations. Moreover, the representative rainfall stations were selected and the urban stations and rural stations were compared. In the analyzed result, the increment of the rainy days was conspicuous over 80mm in which it can cause the heavy rainfall. By using time precipitation data, the design precipitation was calculated. Rainfall events over probability precipitation on duration and return period were analyzed. The times in which it exceeds the probability precipitation in which the urban area is used for the hydrologic structure design in comparison with the rural area more was very much exposed to increase.

• Atmosphere
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• v.18 no.4
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• pp.475-483
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• 2008

The hourly precipitation data from 1973 to 2007 observed at 60 weather stations over Korea are used to characterize the diurnal and semidiurnal cycles of total precipitation amount, intensity and frequency and examine their spatial patterns and interannual variations. The results show that the diurnal cycle peaks in the morning (03-09LST) and the semidiurnal cycle peaks in the late afternoon (16-20LST). It is found that the spatial variations of the peak phase of diurnal or semidiurnal cycle relative to their corresponding seasonal mean cycle are considerably small (large) for total precipitation amount and intensity (frequency, respectively) in both winter and summer seasons. Also, the diurnal phase variations for individual years relative to the seasonal mean precipitation show the significant interannual variability with dominant periods of 2-5 years for all three elements of precipitation and the slightly decreasing trend in total precipitation amount and intensity. To compare the relative contributions of frequency and intensity to the diurnal and semidiurnal cycles (and their sum) of total precipitation amount, the percentage variance of each cycle of precipitation amount explained by frequency is estimated. The fractional variance accounted for by precipitation intensity is greater than that of frequency for these three cycles. All above analyses suggest that intensity plays a more important role than frequency in the diurnal variations of total precipitation amount.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.404-416
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• 2005

The seasonal and regional distribution of precipitation in Korea, terms of the amount of precipitation per day, number of days, and intensity was analyzed using precipitation data from 1971 to 2000. The significance level of the linear trend of these data was also investigated using the analysis of variance of each variable. The amount of precipitation per day less than 80 mm per day appeared in the Honam area which also shows a large number of precipitation day value during the fall and winter. However, the lowest amuont of precipitation per day was shown in the Youngnam area. The positive trend of the annual precipitation amount has also been detected in all stations except for a few station in Honam, and the positive trend of precipitation intensity is statistically significant in most of the stations at the Chungcheong and Gyeonggi area. The linear trend of precipitation intensity in these area is found to be significant at the $5\%$ level.

• Journal of Korea Water Resources Association
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• v.38 no.2
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• pp.111-119
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• 2005

The climatological characteristics of the area averaged monthly precipitation over the Han- and Nakdong-river basins were investigated. The data used for this study is monthly precipitation data from 51 meteorological stations for the period of 1954 to 2002. The magnitude of area averaged precipitation in the Han-river basin was about 10% larger than that in the Nakdong-river basin. However, the variability of two monthly precipitation time series exhibited similar characteristics: April precipitation tends to decrease and August precipitation increase significantly, while there was no significant trend for the other months. There were some indications of abrupt change around the 1970's in the periodicity of precipitation and relationship with El Nino index. September precipitation showed negative correlation with NINO3 index but November precipitation, positive correlation with NINO3 index, indicating a possible connection with the global-scale phenomena.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.329-329
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• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• The Journal of Engineering Geology
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• v.19 no.3
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• pp.313-321
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• 2009

The relationship between precipitation and groundwater level and the correlation between the moving average of precipitation and goundwater level were analyzed for the Han river watershed in Korean peninsular. Fourteen regions in the watershed were selected and there were somewhat different patterns of seasonal fluctuation of groundwater level data. The groundwater level data tends to decrease in dry spell and increase in wet spell however the range between maximum and minimum values is quite different for each gauging point. We could have stronger correlation between groundwater level for fractured rock aquifer and the moving average of precipitation than the groundwater level for alluvial aquifer. The critical infiltration, which is the maximum daily infiltration averaged throughout watershed, value is turned out to have the range of 10 to 90 mm. We could have stronger correlation when we consider critical infiltration and modify the original precipitation data than we use original precipitation data. We also could have higher correlation coefficient when we consider snowmelt effect for the watershed that has considerable snow event.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.393-403
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• 2008

The relationship between precipitation and groundwater level and the correlation between the moving average of precipitation and goundwater level were analyzed for the southern area of Korean peninsular. There were somewhat different patterns of seasonal fluctuation of groundwater level data. The groundwater level data tends to decrease in dry spell and increase in wet spell however the range between maximum and minimum values is quite different for each gauging point. The maximum correlation coefficient for each gauging station is obtained in a range of 20- to 130-day moving average period of precipitation. The critical infiltration, which is the maximum daily infiltration averaged throughout watershed, value is turned out to have the range of 10 to 90 mm and the moving average period is 10 to 150 days. We could have stronger correlation when we consider critical infiltration and modify the original precipitation data than we use original precipitation data.