• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1260-1264
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• 2009

Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1620-1624
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• 2010

The NLCCA has been applied to analyze the East Asia sea surface temperature (SST) and Korea monthly precipitation, where the eight leading PCs of the SST and the eight PCs of the precipitation during 1973-2007 were inputs to an NLCCA model. The first NLCCA mode is plotted in the PC spaces of the Korea precipitation and the world SST present a curve linking the nonlinear relationship between the first three leading PCs of Korea precipitation and world SST forthright. The correlation coefficient between canonical variate time series u and v is 0.8538 for the first NLCCA mode. And there are some areas' climate variability have higher relationship with Korea precipitation, especially focus on the north of East Sea' climate variability have represented the higher canonical correlation with Korea precipitation, with the correlation coefficient is 0.871 and 0.838. Likewise in Korea, most stations display similarly uniform distributing characteristic and less difference, in particular the inshore stations have display identical distributing characteristic. In correlation variables' scores, the fluctuation and variation trend are also seasonal oscillation with high frequency.

• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.337-345
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• 2020

In order to provide information for proper management of groundwater resources, an analysis of the effects of precipitation and groundwater withdrawal on groundwater levels is needed. In this study, we analyzed the correlation of precipitation-groundwater level and groundwater withdrawal-groundwater level using time series data converted by normalized standard deviation (Nor.St.Dev) and cross correlation coefficient (CCC) for nine groundwater monitoring wells in the middle mountainous area in the southeastern Jeju Island. First, the CCCs of precipitation-groundwater level were estimated using daily time series data, and the low CCCs of up to 0.3 were obtained. However, the result of using the Nor.St.Dev showed a clearer correlation by obtaining a CCC of up to 0.8. In addition, in most cases, precipitation variability and groundwater level variability had positive CCCs, whereas groundwater withdrawal variability and groundwater level variability had negative CCCs. Therefore, the groundwater level in this study area was largely influenced by precipitation with little effect of groundwater withdrawal. Lastly, as a result of analyzing the relative effects of Seongpanak and Gyorae rainfall station on the groundwater level, the rainfall at the relatively downstream Gyorae rainfall station has more influence. The analysis method used in this study can be easily used for analyzing the effects of precipitation and groundwater withdrawal on groundwater level variability in other regions in the future.

• East Asian Economic Review
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• v.27 no.4
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• pp.273-301
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• 2023

This study investigates the impacts of long-term climate variability on household consumption in Indonesia, a country highly vulnerable to climate change. The analysis combines household survey data from nearly 5,998 families with satellite-derived weather data from NASA POWER spanning 30 years. We use the long-term variability in temperature and precipitation as a proxy for climate change. This study examines the impact of climate change which proceeds over the long term, unlike previous studies concerning one-off or short-term climate events. In addition, using satellite data enhances the accuracy of households' exposure to climate variability. The analysis finds that households in a village with higher temperature and precipitation variability significantly consume less food. This implies that households more exposed to climate change are at higher risk of malnutrition in developing countries. This study has a limitation that it cannot rule out the potential endogeneity of choosing a climate-vulnerable residential location due to economic poorness.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.24-24
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• 2019

This study investigates variations in the Western Pacific Subtropical High (WPSH) and its impact on South Korean precipitation in late summer during the period between 1958 and 2017. Composite analysis reveals that precipitation occurrence is directly linked to the displacement of the WPSH western ridge, a single, large-scale feature of the atmosphere in the Pacific Ocean. When WPSH ridging is located northwest (NW) of its climatological mean position, excessive precipitation is expected in late summer due to enhanced moisture transport. On the other hand, a precipitation deficit is frequently observed when the western ridge is located in the southeast (SE). Different phases of the WPSH are associated with lagged patterns of Pacific and Atlantic atmospheric and oceanic variability, introducing the potential to predict variability in the WPSH western ridge and its climate over northern East Asia by one month. Based on the identified SST patterns, a simple statistical model is developed and improvement in the ability to predict is confirmed through a cross-validation framework. Finally, the potential for further improvements in WPSH-based predictions is addressed.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Spatial Information Research
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• v.16 no.4
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• pp.467-478
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• 2008

This study analyzes the spatial and temporal variability of temperature ($^{\circ}C$) and precipitation (mm) in Seoul, Korea. The temperature and precipitation data were measured at 31 automatic weather stations (AWSs) in Seoul for 10 years from 1997 to 2006. In this study, inverse distance squared weighting (IDSW) was applied to interpolate the non-measured spaces. To estimate the temperature and precipitation variability, the mean values and frequencies of hot and cold days were examined. The maximum and minimum temperatures were $32.80^{\circ}C$ in 1999 and $-19.94^{\circ}C$ in 2001, respectively. The year 2006 showed the highest frequency of hot temperatures with 79 hot days, closely followed by 2004 and 2005. The coldest year was in 2001 with 105 cold days. The annual mean temperature and precipitation increased by about $1^{\circ}C$ and 483mm during the 10-year period, respectively. The temperature variability differed between high-elevation forested areas and low-elevation residential areas. However, the precipitation variability showed little relation with the topography and land use patterns.

• Atmosphere
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• v.27 no.4
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• pp.435-444
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• 2017

By analyzing Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) from May to September for 1951~2007, this study investigates impacts of two dominant boreal summer intraseasonal oscillation (BSISO) modes on precipitation over Monsoon Asia including Korea and long-term change of 10~20-day and 30~60-day ISO over Korea. It is shown that BSISO strongly modulates rainfall variability over the many part of Monsoon Asia including Korea. Korea tends to have more (less) rainfall during the phases 3~5 (7~8) of BSISO1 representing the canonical northward/northeastward propagating 30~60-day ISO and during the phases 6~8 (3~5) of BSISO2 representing the northward/northwestward propagating 10~20-day ISO. It is found that the 10~20-day ISO variability contributes to summer mean rainfall variability more than 30~60-day ISO over Korea. For the 57 years of 1951~2007, the correlation coefficient between the May to September mean precipitation anomaly and standard deviation of 10~20-day (30~60-day) ISO is 0.71 (0.46). It is further noted that there is a significant increasing trend in the 10~20-day and 30~60-day ISO variability in the rainy season during the period of 1951 to 2007.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.331-337
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• 2016

Water vapor is main absorption factor of outgoing longwave radiation. So, it is essential to monitoring the changes in the amount of water vapor and to understanding the causes of such changes. In this study, we monitor temporal variability of Total Precipitable Water (TPW) which observed by satellite. Among climate variables, precipitation play an important part to analyze temporal variability of water vapor because it is produced by water vapor. And El $Ni{\tilde{n}}o$ is one of climate variables which appear regularly in comparison with the others. Through them, we analyze relationship between temporal variability of TPW and climate variable. In this study, we analyzed long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) data and change of precipitation in middle area of Korea peninsula quantitatively. After these analysis, we compared relation of TPW and precipitation with El $Ni{\tilde{n}}o$. The aim of study is to research El $Ni{\tilde{n}}o$ has an impact on TPW and precipitation change in middle area of Korea peninsula. First of all, we calculated TPW and precipitation from time series analysis quantitatively, and anomaly analysis is performed to analyze their correlation. As a result, TPW and precipitation has correlation mostly but the part had inverse correlation was found. This was compared with El $Ni{\tilde{n}}o$ of anomaly results. As a result, TPW and precipitation had inverse correlation after El $Ni{\tilde{n}}o$ occurred. It was found that El $Ni{\tilde{n}}o$ have a decisive effect on change of TPW and precipitation.