• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.200-204
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• 2012

In engineering hydrology, an estimation of precipitation loss is one of the most important issues for successful modeling to forecast flooding or evaluate water resources for both surface and subsurface flows in a watershed. An accurate estimation of precipitation loss is required for successful implementation of rainfall-runoff models. Precipitation loss or hydrological abstraction may be defined as the portion of the precipitation that does not contribute to the direct runoff. It may consist of several loss elements or abstractions of precipitation such as infiltration, depression storage, evaporation or evapotranspiration, and interception. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The composite loss rate model developed is an exponential model similar to Horton's infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts prior to a storm event, and the decay factor of the loss rate is a composite decay of four losses.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.379-379
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• 2020

The influence of drought has increased due to global warming. In addition, forest fires have occurred more frequently due to droughts and resulted in property losses and casualty. In this study, the effects of drought on Goseong-Sokcho Forest Fire in 2019 were analyzed using high-resolution synthetic precipitation data. In order to determine the severity of drought, the average, 20%tile and 80%ile values were calculated using the synthetic precipitation data of the past 30 years and compared with the current climatology. We have investigated the multi-year accumulated precipitation data to determine the persistence of drought. In Goseong-Sokcho forest fire case, the two-year cumulative synthetic precipitation data shows a similar value to the climate, but the three-year cumulative synthetic precipitation data was close to the 20%ile lines of the climate value. It may expose that the shortage of precipitation in 2017 had persisted until 2019, despite abundant precipitation during the summer in 2018. Therefore, Goseong-Sokcho forest fire might be spread more rapidly by drought which has been persisted since 2017.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.141-153
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• 2012

In this study, we estimated probable precipitation amounts at the target year (2020, 2030, 2040) of 55 weather stations in Korea using the 24 hour annual maximum precipitation data from 1973 through 2009 which should be useful for management of agricultural reservoirs. Not only trend tests but also non-stationary tests were performed and non-stationary frequency analysis were conducted to all of 55 sites. Gumbel distribution was chosen and probability weighted moment method was used to estimate model parameters. The behavior of the mean of extreme precipitation data, scale parameter, and location parameter were analyzed. The probable precipitation amount at the target year was estimated by a non-stationary frequency analysis using the linear regression analysis for the mean of extreme precipitation data, scale parameter, and location parameter. Overall results demonstrated that the probable precipitation amounts using the non-stationary frequency analysis were overestimated. There were large increase of the probable precipitation amounts of middle part of Korea and decrease at several sites in Southern part. The non-stationary frequency analysis using a linear model should be applicable to relatively short projection periods.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.180-180
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• 2023

To improve global risk management, understanding the characteristics and distribution of precipitation is crucial. However, obtaining spatially and temporally resolved climatic data remains challenging due to sparse gauge observations and limited data availability, despite the use of satellite and reanalysis products. To address this challenge, merging available precipitation products has been introduced to generate spatially and temporally reliable data by taking advantage of the strength of the individual products. However, most of the existing studies utilize all the available products without considering the varying performances of each dataset in different regions. Comprehensively considering the relative contributions of each parent dataset is necessary since their contributions may vary significantly and utilizing all the available datasets for data merging may lead to significant data redundancy issues. Hence, for this study, we introduce a site-specific precipitation merging method that utilizes the Quadruple Collocation (QC) approach, which acknowledges the existence of error-cross correlation between the parent datasets, to create a high-resolution global daily precipitation data from 2001-2020. The performance of multiple gridded precipitation products are first evaluated per region to determine the best combination of quadruplets to be utilized in estimating the error variances through the QC approach and computation of merging weights. The merged precipitation is then computed by adding the precipitation from each dataset in the quadruplet multiplied by each respective merging weight. Our results show that our approach holds promise for generating reliable global precipitation data for data-scarce regions lacking spatially and temporally resolved precipitation data.

• Atmosphere
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• v.32 no.4
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• Journal of the Korean earth science society
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• v.27 no.2
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• pp.149-158
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• 2006

This study analyzes the seasonality precipitation using precipitation data from 1973 to 2001 over South Korea. The Seasonality Index and Annual variation of the Seasonality Precipitation were investigated from sixty-three observation stations. The Seasonality Precipitation means the degree of the precipitation falling intensively for some specific months. Spatially, precipitation that has a strong characteristic of regional shower is defined as seasonal precipitation. Precipitation forms are changed with various reasons and mainly the sporadic and local shower precipitation after rain spell in summer. Especially there appears a tendency that this kind of precipitation is sharply increasing in 1990's. Seasonality Index is used as a method to understand seasonal precipitation. If the yearly rainfall is concentrated for some specific months, Seasonality Index is growing gradually. It is confirmed that there is a tendency that all the from sixty-two observation stations Seasonality Index. While Seasonality Index over South of Korea concentrated from June to August because of the summer rain spell in the past ($1973{\sim}1982$), there appears to be a tendency that it concentrated from August and September since the mid 1990's. From the analysis of seasonal precipitation intensity distribution, most of southern Korea is under seasonality precipitation intensity 4. The seasonality precipitation intensity classification results are as follow: most of the observation stations were on a scale intensity of 3 and 4 in the past but currently reads seasonality precipitation intensities of 5 and 6.

• Journal of the Korean Geographical Society
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• v.43 no.3
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• pp.324-336
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• 2008

This paper examines the recent changes of summer precipitation in the aspect of temporal and spatial features using long-term($1958{\sim}2007$) observed station data over South Korea. tong-term mean summer precipitation has revealed two precipitation peaks during summer(June to September); one is the Changma as the first peak, and the other is the post-Changma as the second peak. During the Changma period, the spatial distribution of the maximum precipitation areas is determined by the prevailing southwesterlies and the quasi-stationary front, which results in large amount of precipitation at the windward side of mountain regions over South Korea. However during the post-Changma period, the spatial distribution of the maximum precipitation areas is determined by the lower tropospheric circulation flows from the west and the southeast around the Korean peninsula, and the weather phenomena such as Typhoons, convective instability, and cyclones which are originated from the Yangtze river. The larger amount of precipitation is founded on the southern coastal region and mountain and coastal areas in Korea during the second peak. Time series of total summer precipitation shows a steady increase and the increasing trend is more obvious during the recent 10 years. Decadal variation in summer precipitation indicates a large increase of precipitation, especially in the recent 10 years both in the Changma and the post-Changma period. However, the magnitude of change and the period of the maximum peak presents remarkable contrasts among stations. The most distinct decadal change occurs at Seoul, Busan, and Gangnueng. The precipitation amount is increasing significantly during the post-Changma period at Gangnueng, while the precipitation increases in the period between two maximum precipitation peaks during summer at Seoul and Busan.

• Ocean and Polar Research
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• v.24 no.1
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• pp.19-31
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• 2002

Precipitation variability around King Sejong Station related with E1 $Ni\~{n}o$/Southern Oscillation (ENSO) is evaluated using the gauge-based monthly data of its neighboring stations. Though three Ant-arctic Stations of King Sejong (Korea), Frei (Chile), and Artigas (Uruguay) are all closely located within 10 km, their precipitation data show mostly insignificant positive or rather negative correlations among them in the annual, seasonal and monthly precipitation. This result indicates that there are locally large variations in the distribution of precipitation around King Sejong Station. The monthly data of Frei Station for 31 years (1970-2000) are analyzed for examining the ENSO signal in precipitation because of its longer precipitation record compared to other two stations. From the analysis of seasonal precipitation, it is seen that there is a tendency of less precipitation than the average during E1 $Ni\~{n}o$ events. This dryness is more distinct in fall to spring seasons, in which the precipitation decreases down to about 30% of seasonal mean precipitation. However, the precipitation signal related with La $Ni\~{n}a$ events is not significant. From the analysis of monthly precipitation, it is found that there is a strong negative correlation during 1980s and in the late 1990s, and a weak positive correlation in the early 1990s between normalized monthly precipitation at Frei Station and Sea Surface Temperature (SST) anomalies in the $Ni\~{n}o$ 3.4 region. However, this relation may be not applied over the region around King Sejong Station, but at only one station, Frei.

• 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.

• Atmosphere
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• v.33 no.4
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• pp.367-385
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• 2023

The variabilities of precipitation and particulate matters (i.e., PM₁₀ and PM_2.5) and the scavenging efficiency of PMs by precipitation were quantified using long-term measurements in Seoul, Korea. The 21 years (2001~2021) measurements of precipitation and PM₁₀ mass concentrations, and the 7 years (2015~2021) of PM_2.5 mass concentrations were used. Statistical analysis was performed for each period (i.e., year, season, and month) to identify the long-term variabilities of PMs and precipitation. PM₁₀ and PM_2.5 decreased annually and the decreasing rate of PM₁₀ was greater than PM_2.5. The precipitation intensity did not show notable variation, whereas the annual precipitation amount showed a decreasing trend. The summer precipitation amount contributed 61.10% to the annual precipitation amount. The scavenging efficiency by precipitation was analyzed based on precipitation events separated by 2-hour time intervals between hourly precipitation data for 7 years. The scavenging efficiencies of PM₁₀ and PM_2.5 were quantified as a function of precipitation characteristics (i.e., precipitation intensity, amount, and duration). The calculated average scavenging efficiency of PM₁₀ (PM_2.5) was 39.59% (35.51%). PM₁₀ and PM_2.5 were not always simultaneously scavenged due to precipitation events. Precipitation events that simultaneously scavenged PM₁₀ and PM_2.5 contributed 42.24% of all events, with average scavenging efficiency of 42.93% and 43.39%. The precipitation characteristics (i.e., precipitation intensity, precipitation amount, and precipitation duration) quantified in these events were 2.42 mm hr^-1, 15.44 mm, and 5.51 hours. This result corresponds to 145% (349%; 224%) of precipitation intensity (amount; duration) for the precipitation events that do not simultaneously scavenge PM₁₀ and PM_2.5.