• Journal of Environmental Science International
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• v.23 no.1
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• pp.25-37
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• 2014

In the present study, we analyzed precipitation patterns and diurnal variation trends of hourly precipitation intensity due to climate change. To that end, we used the hourly precipitation data obtained from 26 weather stations around South Korea, especially Busan, from 1970 to 2009. The results showed that the hourly precipitation was concentrated on a specific time of day. In particular, the results showed the so-called "morning shift" phenomenon, which is an increase in the frequency and intensity of hourly precipitation during the morning. The morning shift phenomenon was even more pronounced when a higher level of hourly precipitation intensity occurred throughout the day. Furthermore, in many regions of Korea, including Busan, this morning shift phenomenon became more prevalent as climate change progressed.

• Atmosphere
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• v.22 no.4
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• pp.449-454
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• 2012

This study presents the long-term variability of spring precipitation over the Korean peninsula. It is found that the significant interdecadal change in the spring precipitation has occurred around year 1991. Over the Korean peninsula the precipitation for the post-1991 period increased by about 30 mm per year in CMAP and station-measured data compared to the precipitation prior to year 1991. Due to an increased baroclinicity during the later period, the low-level negative pressure anomaly has developed with its center over northern Japan. Korea is situated at the western end of the negative pressure anomaly, receiving moisture from westerly winds and producing more precipitation. Also, we estimate the change in the near future (years 2020~2040) spring precipitation using six best performing Coupled Model Intercomparison Project 3 (CMIP3) models. These best model ensemble mean shows that spring precipitation is anticipated to increase by about 4% due to the strengthened westerlies accompanied by the northwestern enhancement of the North Pacific subtropical high.

• Journal of Environmental Health Sciences
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• v.22 no.4
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• pp.62-68
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• 1996

Precipitation samples were collected by the wet-only event sampling method at Seoul from September 1991 to April 1995. Concentrations of samples for the ion components($NO_3^-, NO_2^-, SO_4^{2-}, Cl^-, F^-, Na^+, K^+, Ca^{2+}, Mg^{2+}$ and $NH_4^+$) were measured in addition to pH and electric conductivity. During the sampling period, 182 samples were collected, but only 163 samples were identified as valid. The pH, calculated from the volume-weighted $H^+$ concentration, was found to be 4.7, indicating a relatively intensive acidity compared with data from other regions of the world, where acid deposition was known to be a problem. Above all, the concentration of non-seasalt sulfate was $84 \mu eq/L$, which was the highest compared to that measured in other regions of the world. The major acidifying ions in the precipitation at Seoul were identified as sulfate and nitrate except for chloride, because the Cl/Na ratio in the precipitation was close to the ratio in seawater. If all of the non-seasalt sulfate and nitrate existed in the form of sulfuric and nitric acids, respectively, the average pH in the precipitation was calculated as 3.7, lower than the measured value. Consequently, the difference between the calculated and measured pH suggest that the acidity of precipitation was neutralized by alkaline species, not due to the low contribution of an anthropogenic air pollutants to the precipitation. The equivalent concentration ratio of sulfate to nitrate was 3.5, which indicated that the contributions of sulfuric and nitric acids to the precipitation acidity were 78% and 22%, respectively.

• Journal of the Korean earth science society
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• v.43 no.4
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• pp.498-510
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• 2022

Cloud-aerosol interactions are one of the paramount but least understood forcing factors in climate systems. Generally, an increase in the concentration of aerosols increases the concentration of cloud droplet numbers, implying that clouds tend to persist for longer than usual, suppressing precipitation in the warm boundary layer. The cloud lifetime effect has been the center of discussion in the scientific community, partly because of the lack of cloud life cycle observations and partly because of cloud problems. In this study, the precipitation susceptibility (S_o) matrix was employed to estimate the aerosols' effect on precipitation, while the non-aerosol effect is minimized. The S_o was calculated for the typical coupled, well-mixed maritime stratocumulus decks and giant cloud condensation nucleus (GCCN) seeded clouds. The GCCN-artificially introduced to the marine stratocumulus cloud decks-is shown to initiate precipitation and reduces S_o to approximately zero, demonstrating the cloud lifetime hypothesis. The results suggest that the response of precipitation to changes in GCCN must be considered for accurate prediction of aerosol-cloud-precipitation interaction by model studies

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• 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.

• 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.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.366-372
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• 2019

The PRISM model has been used to estimate precipitation in South Korea where observation data are readily available at a large number of weather station. However, it is likely that the PRISM model would result in relatively low reliability of precipitation estimates in North Korea where weather data are available at a relatively small number of weather stations. Alternatively, a hybrid method has been developed to estimate the precipitation distribution in area where availability of climate data is relatively low. In the hybrid method, Regression coefficients between the precipitation-terrain relationships are applied to a low-resolution precipitation map produced using the PRISM. In the present study, a hybrid approach was applied to North Korea for estimation of precipitation distribution at a high spatial resolution. At first, the precipitation distribution map was produced at a low-resolution (2,430m) using the PRISM model. Secondly, a deviation map was prepared calculating difference between altitudes of synoptic stations and virtual terrains produced using 270m-resolution digital elevation map (DEM). Lastly, another deviation map of precipitation was obtained from the maps of virtual precipitation produced using observation data from the synoptic weather stations and both synoptic and automated weather station (AWS), respectively. The regression equation between precipitation and terrain was determined using these deviation maps. The high resolution map of precipitation distribution was obtained applying the regression equation to the low-resolution map. It was found that the hybrid approach resulted in better representation of the effects of the terrain. The precipitation distribution map for the hybrid approach had similar spatial pattern to that for the existing method. It was estimated that the mean annual cumulative precipitation of entire territory of North Korea was 1,195mm with a standard deviation of 253mm.

• Atmosphere
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• v.26 no.4
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• pp.553-563
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• 2016

Land evaporation contribution to precipitation over East Asia is studied to understand terrestrial moisture source of continental precipitation. Moisture recycling of precipitation relying on terrestrial evaporation is estimated based on the analysis method of Van der Ent et al. (2010). We utilize HadGEM2-AO simulations for the period of 1970~1999 and 2070~2099 from RCP8.5. Globally, 46% of terrestrial precipitation is depending from continental evaporation. 58% of terrestrial evaporation returns as continental precipitation. Over East Asia, precipitation has been affected by local evaporation and transported moisture. The advection of upwind continental evaporation results from the prevailing westerlies from the midwestern of Eurasian continent. For the present-day period, about 66% of the precipitation over the land of East Asia originates from land evaporation. Regionally, the ratios change and the ratios of precipitation terrestrial origin over the Northern inland and Southern coast of East Asia are 82% and 48%, respectively. Seasonally, the continental moisture recycling ratio is larger during summer (JJA) than winter (DJF). According to RCP8.5, moisture recycling ratio is expected to change. At the end of the 21st century, the impact of continental moisture sources for precipitation over East Asia is projected to be reduced by about 5% compared to at the end of 20th century. To understand the future changes, moisture residence time change is investigated using depletion and replenishment time.