• Journal of the Korean earth science society
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• v.42 no.5
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• pp.514-523
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• 2021

Using the precipitation data observed at the Gochang Standard Weather Observatory (GSWO) during the winter seasons from 2014 to 2016, we analyzed the precipitation characteristics of the winter observation environment. For this study, we used four different types of precipitation gauges, i.e., No Shield (NS), Single Alter (SA), Double Fence Intercomparison Reference (DFIR), and Pit Gauge (PG). We analyzed the data from each to find differences in the accumulated precipitation, characteristics of the precipitation type, and the catch efficiency according to the wind speed based on the DFIR. We then classified these into three precipitation types, i.e., rain, mixed precipitation, and snow, according to temperature data from Gochang's Automated Synoptic Observing System (ASOS). We considered the DFIR to be the standard precipitation gauge for our analysis and the cumulative winter precipitation recorded by each other gauge compared to the DFIR data in the following order (from the most to least similar): SA, NS, and PG. As such, we find that the SA gauge is the most accurate when compared to the standard precipitation gauge used (DFIR), and the PG system is inappropriate for winter observations.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.103-113
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• 2009

In this study, we separated the precipitation zones using the geographic location of stations and precipitation characteristics (monthly, seasonal, annual) in Gangwon province. Precipitation data of 66 weather stations (meterological office: 11 locations, auto weather system (AWS): 55 places) were used, and statistical method, K-means cluster method, was conducted for division of the precipitation regions. As the results of regional classification, the five zones of precipitation (Yongdong: 1 region, Youngseo: 4 regions) were separated. Seasonal average precipitation in spring is similar throughout Gangwon Province, seasonal average precipitation in summer has high values at Youngseo, and seasonal average precipitation in autumn and winter have high values at Youngdong. The some areas, the vicinity of Misiryeong and Daegwallyeong, happens the orographic precipitation in spatial analysis, but the orographic effects didn't occur for the whole Gangwon areas. However, to achieve more accurate results, the expansion of observatories per elevation and AWS data are demanded.

• Atmosphere
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• v.32 no.2
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• pp.135-148
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• 2022

This study investigates the influence of atmospheric river (AR) on precipitation over South Korea with a focus on regional characteristics. The 42-year-long catalog of ARs, which is obtained by applying the automatic AR detection algorithm to ERA5 reanalysis data and the insitu precipitation data recorded at 56 weather stations across the country are used to quantify their relationship. Approximately 51% of the climatological annual precipitation is associated with AR. The AR-related precipitation is most pronounced in summer by approximately 58%, while only limited fraction of precipitation (26%) is AR-related in winter. The heavy precipitation (> 30 mm day^-1) is more prone to AR activity (59%) than weak precipitation (5~30 mm day^-1; 33%) in all seasons. By grouping weather stations into the four sub-regions based on orography, it is found that the contribution of AR precipitation to the total is largest in the southern coast (57%) and smallest in the eastern coast (36%). Similar regional variations in AR precipitation fractions also occur in weak precipitation events. The regional contrast between the northern and southern stations is related to the seasonal variation of AR-frequency. In addition, the regional contrast between the western and eastern stations is partly modulated by the orographic forcing. The fractional contribution of AR to heavy precipitation exceeds 50% in all seasons, but this is true only in summer along the eastern coast. This result indicates that ARs play a critical role in heavy precipitation in South Korea, thus routine monitoring of ARs is needed for improving operational hydrometeorological forecasting.

• Atmosphere
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• v.25 no.1
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.747-755
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• 1999

Effects of precipitation and cloud cover on high ozone days are examined by investigating the precipitation and average cloud cover before the ozone peak time within a day. High ozone days above 100 ppb in the Greater Seoul Area(GSA) for the ozone season from May to September are chosen for the analyses in terms of the surface meteorological data during 1990~1997. The result shows that the effect of precipitation on the rise of ozone concentration is definitely negative so that ozone concentration could not rise above 100ppb immediately after precipitation. But, the effect of cloud cover is associated with the variations of other meteorological parameters. The number of high ozone days with "zero" cloud cover is rather less than that with cloud cover of 1 to 4 since temperature is usually lower in "zero" cloud cover days. Furthermore, ozone concentration can rise above 100ppb even with full cloud cover when the wind is weak and the temperature is high.temperature is high.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.217-222
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• 2015

Clustering is one of important data mining techniques known as exploratory data analysis and is being applied in various engineering and scientific fields such as pattern recognition, remote sensing, and so on. The method organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. Weather radar observes atmospheric objects by utilizing reflected signals and stores observed data in corresponding coordinate. To analyze the radar data, it is needed to be separately organized precipitation and non-precipitation echo based on similarities. Thus, this paper studies to apply clustering method to radar data. In addition, in order to solve the problem when precipitation echo locates close to non-precipitation echo, fuzzy logic based clustering method which can consider both distance and other properties such as reflectivity and Doppler velocity is suggested in this paper. By using actual cases, the suggested clustering method derives better results than previous method in near-located precipitation and non-precipitation echo case.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.2
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• Journal of Korea Water Resources Association
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• v.30 no.3
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• pp.247-256
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• 1997

Some climatological features of summer precipitation in Korea were studyed using the precipitation data of 15 stations of Korea Meteorological Administration where more than 30 years data since 1961 are available. The study included statistical analysis of precipitation by climatological normal values, and comparison of inter-annual variation of annual precipitation, summer precipitation and precipitation during the Changma. The relationships between them were also analyzed. It was revealed that, in Korea, more than half of annual precipitation was concentrated in summer season (June to August), and it was usually influenced by the Changma. The ratio of summer and Changma precipitation to the annual precipitation showed that effect of Changma was bigger in the central inland area, while comparatively smaller in the east coastal area and Cheju Island due to topographical effects. It was also shown that the fluctuation of the annual precipitation was less variable than those of summer and Changma precipitations. Thus, it was suggested that understanding the variation features of summer precipitation associated with monsoon activities was very important to figure out the change of annual precipitation for the national water resources planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.163-163
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• 2023

Flooding has become an increasing event which is one of the major natural disasters responsible for direct economic damage in South Korea. Driven by climate change, precipitation extremes play significant role on the flood damage and its further increase is expected to exacerbate the socioeconomic impact in the country. However, the empirical evidence associating changes in precipitation extremes to the historical flood damage is limited. Thus, there is a need to assess the causal relationship between changes in precipitation extremes and flood damage, especially in agricultural region like Chungcheong region in South Korea. The spatial and temporal changes of precipitation extremes from 10 synoptic stations based on daily precipitation data were analyzed using the ClimPACT2 tool and Mann-Kendall test. The four precipitation extreme indices consisting of consecutive wet days (CWD), number of very heavy precipitation wet days (R30 mm), maximum 1-day precipitation amount (Rx1day), and simple daily precipitation intensity (SDII), which represent changes in intensity, frequency, and duration, respectively, and the time series data on flooded area and flood damage from 1985 to 2020 were used to investigate the causal relationship in the ARDL-ECM framework and pairwise Granger causality analysis. The trend results showed that majority of the precipitation indices indicated positive trends, however, CWD showed no significant changes. ARDL-ECM framework showed that there was a long-run relationship among the variables. Further analysis on the empirical results showed that flooded area and Rx1day have significant positive impacts on the flood damage in both short and long-runs while R30 mm only indicated significant positive impact in the short-run, both in the current period, which implies that an increase in flooded area, Rx1day, and R30 mm will cause an increase in the flood damage. The pairwise Granger analysis showed unidirectional causality from the flooded area, R30 mm, Rx1day, and SDII to flood damage. Thus, these precipitation indices could be useful as indicators of pluvial flood damage in Chungcheong region of South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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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 support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly 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 SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly 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.