• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.475-485
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• 2014

A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.413-420
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• 2013

Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.1
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• pp.57-67
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• 1998

A stochastic weather generator which simulate daily precipitation, maximum and minimum daily temperature, relative humidity was developed. The model parameters were estimated using stochastic characteristics analysis of historical data of 71 weather stations. Spatial variations of the parameters for the country were also analyzed. Model parameters of ungauged Sites were determined from parameters of adjacent weather stations using inverse distance method. The model was verified on Suwon and Ulsan weather stations and showed good agreement between simulated and observed data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.345-363
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• 2003

From the General Circulation Models(GCMs), it is known that the increases of concentrations of greenhouse gases will have significant implications for climate change in global and regional scales. The GCM has an uncertainty in analyzing the meteorologic processes at individual sites and so the 'downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what, at present, climate modellers can provide and what impact assessors require. This paper describes a method for assessing local climate change impacts using a robust statistical downscaling technique. The method facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. The construction of climate change scenarios based on spatial regression(transfer function) downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translates the GCM grid-box predictions with coarse resolution of climate change to site-specific values and the values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather values. In this study, the global climate change scenarios are constructed using the YONU GCM control run and transient experiments.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1425-1428
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• 2010

추계학적 기상모형(Stochastic weather generator)은 기상자료의 결측치 보완, 장기간의 기상 시계열 자료 생성, 지역적 기후변화 시나리오의 통계학적 다운스케일링에 적용되어 왔다. 이러한 추계학적 기상모형은 수자원, 농업, 환경, 생태 등의 분야에 적용되어, 수자원 설계, 점/비점오염 거동, 생태 및 수문학적 영향 평가의 중요한 도구로 이용되어 오고 있다. 또한, 최근 가장 큰 이슈가 되고 있는 기후변화의 영향을 평가하는데 필수불가결한 분야이다. 이 분야의 중요한 변화는 과거에는 지점별로 각각 기상자료를 생성하였으나, 최근에는 지점간의 상관성을 고려한 다지점 해석이 계속적으로 연구되어지고 있다. 본 연구에서는 유역규모에 적용하기 타당한 기상자료생성을 위하여 관측지점간의 상관성, 강수장(rainfall field)의 생성, 호우이동(storm movement)을 고려한 추계학적 기상모형을 제안하고, 충주댐 유역을 대상으로 그 적용성을 평가하였다.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.273-273
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• 2012

최근 기후변화에 따른 수자원 평가를 위해 장기유출모형이 적극 활용되고 있다. 또한, 장기유출모형의 입력자료인 기후변화 시나리오를 적용한 수문학적 기상자료 생성을 위해 추계학적 기상모형(Stochastic weather generator)이 이용되고 있다. 이러한 장기유출모형의 수문학적 프로세스, 기상모형의 결과는 일단위에 기초하고 있다. 특히, 입력자료로 이용되는 강우량은 대상유역 단위의 면적강우량 산정, 즉 실제 강우의 공간적 특성이 반영된 강우생성이 무엇보다 중요한 것으로 판단된다. 따라서, 추계학적 기상모형을 이용하여 강우생성시, 강우의 공간적 특성이 고려된 호우(Storm) 생성이 중요하다. 이러한 호우생성을 위해 호우규모에 따른 면적감소계수에 대한 분석이 요구된다. 따라서, 본 연구에서는 충주댐 유역을 대상으로 일단위의 동시강우 자료를 수집하여, 호우규모에 따른 면적감소계수의 특성을 파악하여 논의하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.143-148
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• 2005

The main purpose of this study is to suggest and evaluate an operational method for assessing the potential impact of climate change on hydrologic components and water resources of regional scale river basins. The method, which uses large scale climate change information provided by a state of the art general circulation model(GCM) comprises a statistical downscaling approach and a spatially distributed hydrological model applied to a river basin located in Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The values are used to modify the parameters of the stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of $2CO_2$. This approach is applied to the Yongdam dam basin in southern part of Korea. The results show that under the condition of $2CO_2$, about $7.6\% of annual mean streamflow is reduced when it is compared with the observed one. And while Seasonal streamflows in the winter and autumn are increased, a streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern and the analysis of the duration cure shows the mean of averaged low flow is increased while the averaged wet and normal flow are decreased for the climate change.