• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.523-534
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• 1994

The purpose of this study is to develop computer simulation model that produce precipitation patterns from stochastic model. In the paper(I) of this study, the alternate renewal process(ARP) is used for the daily precipitation series. In this paper(Il), stochastic simulation models for the daily precipitation series are developed by combining Markov chain for the precipitation occurrence process and continuous probability distribution for the precipitation amounts on the wet days. The precipitation occurrence is determined by first order Markov chain with two states(dry and wet). The amounts of precipitation, given that precipitation has occurred, are described by a Gamma, Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Since the daily precipitation series shows seasonal variation, models are identified for each month of the year separately. To illustrate the application of the simulation models, daily precipitation data were taken from records at the seven locations of the Nakdong and Seomjin river basin. Simulated data were similar to actual data in terms of distribution for wet and dry spells, seasonal variability, and precipitation amounts.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Water for future
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• v.7 no.1
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• pp.65-70
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• 1974

According to the simplified Gringorten's method of extreme values from data samples, daily maximum precipitation and return period in Seoul was estimated. And also, it was known that the distribution of daily maximum precipitation of Seoul area belong th an exponential type of distribution.

• Atmosphere
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• v.18 no.4
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• pp.397-416
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• 2008

This study investigates a long-term variation of the annual extreme value for the instantaneous wind speed and the daily precipitation during 56 years (1951-2006) in Korea. Results show that there is a uptrend for both wind and precipitation extreme records, although regional trends are different from overall pattern in some places, particularly for wind speed. The estimated linear trends are 230 mm/56 yr in the daily precipitation and $15ms^{-1}$/56 yr in the maximum instantaneous wind speed. For precipitation, other indexes such as total annual precipitation, the number of extreme precipitation event, and precipitation intensity have dramatically increased as well, while there has been a clear downtrend for the number of strong wind events (> $14ms^{-1}$). It is found that the minimum surface pressure recorded during typhoon attacks in Korea tends to be decreasing, about 10 hPa/56 yr. This partly explains why the extreme values in the precipitation are increasing in Korea.

• The Korean Journal of Quaternary Research
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• v.13 no.1
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• pp.45-52
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• 1999

This study examined the spatial distribution of precipitation in Donghae-Shi. The daily, monthly precipitaion on the 2 stations, 3 AWS(Automatic Weather Station) were analyzed by altitudinal distribution, the air pressure type and days of daily precipitation. The results of the study are as follows. 1 Hour greatest precipitation is 62.4mm(1994. 10. 12), Daily greatest precipitation, 200mm(1994. 10. 12), Monthly greatest precipitation, 355.5mm(1994. 10), Maximum depth of snow fall, 35.5cm(1994. 1. 29) in Donghae-Shi, 1993∼1997. Altitudinal distribution of precipitation in Summer tends to have more precipitation at higher altitude, in Winter, high mountains and coast have more precipitation than other sites do. The heavy rainfall in Donghae-Shi is mainly formed by a Typhoon, next is Jangma front. The number of consecutive days of daily precipitation $\geq$20mm is 81days, 44days of those appeared in Summer season. The synoptic environment causes the difference in observed the heavy snowfall amount between high mountains and coast.

• Water for future
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• v.29 no.6
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• pp.179-188
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• 1996

The chronical sequences of daily precipitation are of great practical importance in the planning and operational processes of water resources system. A sequence of days with alternate dry day and wet day can be generated by two state Markov chain model that establish the subsequent daily state as wet or dry by previously calculated vconditional probabilities depending on the state of previous day. In this study, a synthetic generation model for obtaining the daily precipitation series is presented by classifying the precipitation amount in wet days into multi-states. To apply multi-state Markov chain model, the daily precipitation amounts for wet day are rearranged by grouping into thirty states with intervals for each state. Conditional probabilities as transition probability matrix are estimated from the computational scheme for stepping from the precipitation on one day to that on the following day. Statistical comparisons were made between the historical and synthesized chracteristics of daily precipitation series. From the results, it is shown that the proposed method is available to generate and simulate the daily precipitation series with fair accuracy and conserve the general statistical properties of historical precipitation series.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.303-314
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• 2024

Precipitation data are an integral part of water management planning, especially the design of hydroelectric structures and the study of floods and droughts. However, it is difficult to obtain accurate data due to space-time constraints. The recent increase in hydrological variability due to climate change has further emphasized the importance of precipitation simulation techniques. Therefore, in this study, the Modified Bartlett-Lewis Rectangular Pulse model was utilized to apply the parameters necessary to predict daily precipitation. The effect of this parameter on the daily precipitation prediction was analyzed by applying exponential distribution, Gamma distribution, and Weibull distribution to evaluate the suitability of daily precipitation prediction according to each distribution type. As a result, it is judged that parameters should be selected in consideration of regional and seasonal characteristics when simulating precipitation using the MBLRP model.

• 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 of Transportation
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• v.32 no.2
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• pp.83-92
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• 2014

Weather condition is one of the crucial factors affecting travelers' mode choice. Nevertheless, there are numerous indefinite traffic phenomena under various weather conditions. This study was conducted to verify the hypothesis that transit riderships decrease as precipitation increases. To clarify the relationship between precipitation and transit ridership, a seemingly unrelated regression model was employed with data such as daily precipitation and daily transit riderships of 3 transit modes (bus, metro, and shuttle bus) collected in Busan for recent 24 months. The estimation results show that transit riderships decreased as the daily precipitation increased when the daily precipitation is greater or equal to 10mm/day (0.169%, 0.101%, and 0.172% reduction in bus, metro, and shuttle bus riderships, respectively, when the daily precipitation increased by 1mm). When comparing the impact of precipitation on transit riderships by modes using a cross-equation parameter restriction test, the decrease in metro ridership is relatively insensitive to the change in precipitation. However, the negative coefficient of precipitation in the metro ridership estimation model indicates that the transit users in Busan may alter their mode to taxi or automobile and/or may give up the trip itself in bad weather condition.

• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.595-604
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• 2005

Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.