• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• 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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.25-28
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• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

• Atmosphere
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• v.22 no.1
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• pp.57-71
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• 2012

Influences of orographic and ocean effect, which depend on the detailed geographic characteristics, upon winter time (December-February) precipitation in the Yeongdong region are investigated. Most of precipitation events in the Yeongdong region during the wintertime are associated with moist northeasterly (coming from the northeast direction) winds and also the spatial distribution of precipitation shows a great difference between Mountain area (Daegwallyeong) and Coastal area (Gangneung). The linear correlation coefficient between the meteorological variables obtained from NCEP/NCAR Reanalysis Data and precipitation amount for each precipitation type is calculated. Mountain type precipitation is dominated by northeasterly wind speed of the low level (1000 hPa and 925 hPa) and characterized with more precipitation in mountain area than coastal area. However, Coastal type precipitation is affected by temperature difference between ocean and atmosphere, and characterized with more precipitation in coastal area than mountain area. The results are summarized as follows; In the case of mountain type precipitation, the correlation coefficient between wind speed at 1000 hPa (925 hPa) and precipitation amount at Daegwallyeong is 0.60 (0.61). The correlation is statistical significant at 1% level. In the case of coastal type precipitation, the correlation coefficient of temperature difference between ocean and 925 hPa (850 hPa) over the East sea area and precipitation amount at Gangneung is 0.33 (0.34). As for the mountain type precipitation, a detailed analysis was conducted in order to verify the relationship between precipitation amount at Daegwallyeong and low level wind speed data from wind profiler in Gangneung and Buoy in the East Sea. The results also show the similar behavior. This result indicates that mountain type precipitation in the Yeongdong region is closely related with easterly wind speed. Thus, the statistical analysis of the few selected meteorological variables can be a good indicator to estimate the precipitation totals in the Yeongdong region in winter time.

• Water for future
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• v.28 no.3
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• pp.143-157
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• 1995

A precipitation change in Korea due to atmospheric $CO_2$ doubling has been estimated with a mixed method(Robinson and Finkelstein, 1991) to represent regional precipitation distribution from the simulated precipitation data by three GCM(general circulation model) (CCC, UI, and GFDL GCM) experiments. As a result of this analysis, the precipitation change by atmospheric $CO_2$ doubling can be summarized as follows: The precipitation increases as much as 25mm/yr during spring season and more than 50mm/yr during summer and autumn. However, it decreases as much as 13mm/yr during winter. In terms of percentage with respect to current precipitation climatology, we may have more rain as much as 10%, 13% and 24%, respectively, for spring, summer and autumn than current precipitation. However, we may have less winter precipitation than current climatological average.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.252-252
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• 2011

Rainfall-runoff models are calibrated and validated by using a same data set such as observations. The past climate change effects the present rainfall pattern and also will effect on the future. To predict rainfall-runoff more preciously we have to consider the climate change pattern in the past, present and the future time. Thus, in this study, the climate change represents changes in mean precipitation and standard deviation in different patterns. In some river basins, there is no enough length of data for the analysis. Therefore, we have to generate the synthetic data using proper distribution for calculation of precipitation based on the observed data. In this study, Kajiyama model is used to analyze the runoff in the dry and the wet period, separately. Mean and standard deviation are used for generating precipitation from the gamma distribution. Twenty hypothetical scenarios are considered to show the climate change conditions. The mean precipitation are changed by -20%, -10%, 0%, +10% and +20% for the data generation with keeping the standard deviation constant in the wet and the dry period respectively. Similarly, the standard deviations of precipitation are changed by -20%, -10%, 0%, +10% and +20% keeping the mean value of precipitation constant for the wet and the dry period sequentially. In the wet period, when the standard deviation value varies then the mean NSE ratio is more fluctuate rather than the dry period. On the other hand, the mean NSE ratio in some extent is more fluctuate in the wet period and sometimes in the dry period, if the mean value of precipitation varies while keeping the standard deviation constant.

• Journal of the Korean association of regional geographers
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• v.9 no.2
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• pp.192-202
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• 2003

The purpose of this paper is to examine the characteristics of precipitation distribution of the Korean Peninsula according to the latitudinal location of the front for the Changma season. In the Korean Peninsula there are much rainfalls in the regions near the Changma Front and these regions have much annual mean rainfall. When the front is going north across the latitude of $30^{\circ}N$, precipitation is increased in the whole country and it is the beginning time of Changma. The day which has rainfall less than 10 mm a day appears frequently around the neighborhood of the Gaema plateau in the Changma season. In the basin of the Cheongcheon River the greater part of much mean rainfall of June and July is explained by the precipitation of the cases of no front in $128^{\circ}E$ and that for fronts of the latitude zone of $30{\sim}33^{\circ}N$ which is far from the basin, and this is a different point from the other much rainfall region in Korea.

• 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.