• Water Engineering Research
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• v.2 no.1
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• pp.1-10
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• 2001

The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

• Atmosphere
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• v.22 no.3
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• pp.287-298
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• 2012

The predictability of heavy precipitation over the Korean Peninsula is studied using THORPEX Interactive Grand Global Ensemble (TIGGE) data. The performance of the six ensemble models is compared through the inconsistency (or jumpiness) and Root Mean Square Error (RMSE) for MSLP, T850 and H500. Grand Ensemble (GE) of the three best ensemble models (ECMWF, UKMO and CMA) with equal weight and without bias correction is consisted. The jumpiness calculated in this study indicates that the GE is more consistent than each single ensemble model. Brier Score (BS) of precipitation also shows that the GE outperforms. The GE is used for a case study of a heavy rainfall event in Korean Peninsula on 9 July 2009. The probability forecast of precipitation using 90 members of the GE and the percentage of 90 members exceeding 90 percentile in climatological Probability Density Function (PDF) of observed precipitation are calculated. As the GE is excellent in possibility of potential detection of heavy rainfall, GE is more skillful than the single ensemble model and can lead to a heavy rainfall warning in medium-range. If the performance of each single ensemble model is also improved, GE can provide better performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.441-452
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• 2009

This paper describes the evaluation technique for climate change effect on daily precipitation frequency using daily precipitation generator that can use outputs of the climate model offered by IPCC DDC. Seoul station of KMA was selected as a study site. This study developed daily precipitation generation model based on two-state markov chain model which have transition probability, scale parameter, and shape parameter of Gamma-2 distribution. Each parameters were estimated from regression analysis between mentioned parameters and monthly total precipitation. Then the regression equations were applied for computing 4 parameters equal to monthly total precipitation downscaled by K-NN to generate daily precipitation considering climate change. A2 scenario of the BCM2 model was projected based on 20c3m(20th Century climate) scenario and difference of daily rainfall frequency was added to the observed rainfall frequency. Gumbel distribution function was used as a probability density function and parameters were estimated using probability weighted moments method for frequency analysis. As a result, there is a small decrease in 2020s and rainfall frequencies of 2050s, 2080s are little bit increased.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.725-740
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• 2008

In this study a multi-site daily precipitation generator which generates the precipitation with similar spatial correlation, and at the same time, with conserving statistical properties of the observed data is developed. The proposed generator is intended to be a tool for down-scaling the data obtained from GCMs or RCMs into local scales. The occurrences of precipitation are simultaneously modeled in multi-sites by 2-parameter first-order Markov chain using random variables of spatially correlated while temporally independent, and then, the amount of precipitation is simulated by 3-parameter mixed exponential probability density function that resolves the issue of maintaining intermittence of precipitation field. This approach is applied to the Nakdong river basin and the observed data are daily precipitation data of 19 locations. The results show that spatial correlations of precipitation series are relatively well simulated and statistical properties of observed precipitation series are simulated properly.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.413-427
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• 2010

Urbanization means the sudden increment of a population and the industrialization. The hydrologic water cycle causes many changes due to urbanization. Therefore, the affects that urbanization influences on the precipitation events were analyzed. But the precipitation events are very much influenced many meteorological and climatologically indices besides the effect of an urbanization. So, an analysis was performed by using precipitation data observed in many spots of the Korean peninsula. The analysis data are annual precipitation, the duration 1 daily maximum amount of precipitation, the rainy days, and 10 mm over the rainy days, and 80 mm. seasonal precipitation and seasonal rainy days. The analytical method classified 4 clusters in which the precipitation characteristic is similar through the cluster analysis. It compared and analyzed precipitation events of the urban and rural stations. Moreover, the representative rainfall stations were selected and the urban stations and rural stations were compared. In the analyzed result, the increment of the rainy days was conspicuous over 80mm in which it can cause the heavy rainfall. By using time precipitation data, the design precipitation was calculated. Rainfall events over probability precipitation on duration and return period were analyzed. The times in which it exceeds the probability precipitation in which the urban area is used for the hydrologic structure design in comparison with the rural area more was very much exposed to increase.

• Journal of Environmental Science International
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• v.6 no.2
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• pp.95-106
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• 1997

The characteristics on the transtion probabilities and periodicity for the daily precipitation occurrence in Korean peninsula are investigated by applying the Markov chain properties to daily precipitation occurrence. In order to examine the responses of Markov Chain properties to the applied period and their magnitudes, three cases (Case A: 1956~ 1985 at 14 stations, Case B: 1965~ 1994 at 14 stations, and Case C: 1985~ 1994 at 63 stations) are considered In this study. The transition probabilities from wet day to wet day for all cases are about 0.50 and in summer, especially July, are higher. In addition, considering them in each station we can find that they are the highest at Ullung-do and lowest at Inchon for all cases. The annual equilibrium probabilities of a wet day appear 0.31 In Case A, 0.30 Case B, and 0. 29 Case C, respectively. This may explain that as the data-period used becomes shorter, the higher the equilibrium probability is. The seasonal distributions of equilibrium probabilities are appeared the lowest(0.23~0.28) in winter and the highest(more than 0.39) in spring and monthly in .truly and in October, repectively. The annual mean wet duration for all cases is 2.04 days in Case A, 1.99 Case B, and 1.89 Case C, repectively. The weather cycle obtained from the annual mean wet and dry duration is 6.54~6.59 days, which are closely associated with the movement of synoptic systems. And the statistical tests show that the transitions of daily precipitation occurrence for all cases may have two-state first Markov chain property, being the stationarity in time and heterogeneity in space.

• 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.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• 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.