• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• 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.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1057-1066
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• 2018

Climate change has been influenced on extreme precipitation events, which are major driving causes of flooding. Especially, most of extreme water-related disasters in Korea occur from floods induced by extreme precipitation events. However, future climate change scenarios simulated with Global Circulation Models (GCMs) or Reigonal Climate Models (RCMs) are limited to the application on medium and small size rivers and urban watersheds due to coarse spatial and temporal resolutions. Therefore, the current study introduces the state-of-the-art approaches and procedures of statistical downscaling techniques to resolve this limitation It is expected that the temporally downscaled data allows frequency analysis for the future precipitation and estimating the design precipitation for disaster prevention.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.213-225
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• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of Climate Change Research
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• v.2 no.2
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• pp.79-91
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• 2011

In order to study interactions between climate change and air quality, a modeling system including the downscaling scheme has been developed in the integrated manner. This research focuses on the development of a downscaling method to utilize CCSM3 outputs as the initial and boundary conditions for the regional climate model, MM5. Horizontal/vertical interpolation was performed to convert from the latitude/longitude and hybrid-vertical coordinate for the CCSM3 model to the Lambert-Conformal Arakawa-B and sigma-vertical coordinate for the MM5 model. A variable diagnosis was made to link between different variables and their units of CCSM and MM5. To evaluate the dynamic downscaling performance of this study, spatial distributions were compared between outputs of CCSM/MM5 and NRA/MM5 and statistic analysis was conducted. Temperature and precipitation patterns of CCSM/MM5 in summer and winter showed a similar pattern with those of observation data in East Asia and the Korean Peninsula. In addition, statistical analysis presented that the agreement index (AI) is more than 0.9 and correlation coefficient about 0.9. Those results indicate that the dynamic downscaling system built in this study can be used for the research of interaction between climate change and air quality.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.4
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• pp.83-91
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• 2010

Generally, the GCM (General Circulation Model) data by IPCC climate change scenarios are used for future weather prediction. IPCC GCM models predict well for the continental scale, but is not good for the regional scale. This paper tried to generate future temperature and precipitation of 8 scattered meteorological stations in South Korea by using the MIROC3.2 hires GCM data and applying LARS-WG downscaling method. The MIROC3.2 A1B scenario data were adopted because it has the similar pattern comparing with the observed data (1977-2006) among the scenarios. The results showed that both the future precipitation and temperature increased. The 2080s annual temperature increased $3.8{\sim}5.0^{\circ}C$. Especially the future temperature increased up to $4.5{\sim}7.8^{\circ}C$ in winter period (December-February). The future annual precipitation of 2020s, 2050s, and 2080s increased 17.5 %, 27.5 %, and 39.0 % respectively. From the trend analysis for the future projected results, the above middle region of South Korea showed a statistical significance for winter precipitation and south region for summer rainfall.

• Atmosphere
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• v.18 no.1
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• pp.71-81
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• 2008

In this study, in order to estimate high resolution precipitation with monthly time scales, Parameter-elevation Regressions on Independent Slopes Model (PRISM) was modified and configured for Korean precipitation based on elevation, distance, topographic facet, and coastal proximity. Applying this statistical downscaling model to Korean precipitation for 5 years from 2001 to 2005, we have compiled monthly grid data with a horizontal resolution of 5-km and evaluated the model using bias, root mean square error (RMSE), and correlation coefficient between the observed and the estimated. Results show that bias, RMSE, and correlation coefficient of the estimated value have a range from 0.2% to 1.0%, 19.6% (June) to 43.9% (January), and 0.73 to 0.84, respectively, indicating that the modified Korean PRISM (K-PRISM) is reasonably worked by weighting factors, i.e., topographic effect and rain shadow effect.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.135-143
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• 2020

Shortwave radiation and sunshine hours (SHOUR) are important variables having many applications, including crop growth. However, observational data for these variables have low horizontal resolution, rendering its application to related research and decision making on f arming practices challenging. In the present study, hourly solar radiation data were physically generated using the Land-Atmosphere Modeling Package (LAMP) at the National Center f or Agro-Meteorology, and then daily SHOUR fields were calculated through statistical downscaling. After data quality evaluation, including case studies, the SHOUR data were added to the existing publically accessible LAMP daily database. The LAMP daily dataset, newly updated with SHOUR, has been provided operationally as input data to the "Gyeonggi-do Agricultural Drought Prediction System," which predicts agricultural weather disasters and field crop growth status.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.563-573
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• 2018

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts' knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.