• Journal of the Korean earth science society
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• v.34 no.1
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• pp.51-58
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• 2013

An appropriate determination of time step is one of the important problems in atmospheric modeling. In this study, we investigate the sensitivity of numerical solutions to time step in a nonlinear atmospheric model. For this purpose, a simple nondimensional dynamical model is employed, and numerical experiments are performed with various time steps and nonlinearity factors. Results show that numerical solutions are not sensitive to time step when the nonlinearity factor is not influentially large and truncation error is negligible. On the other hand, when the nonlinearity factor is large (i.e., in a highly nonlinear regime), numerical solutions are found to be sensitive to time step. In this situation, smaller time step increases the intensity of the spatial filter, which makes small-scale phenomena weaken. This conflicts with the fact that smaller time step generally results in more accurate numerical solutions owing to reduced truncation error. This conflict is inevitable because the spatial filter is necessary to stabilize the numerical solutions of the nonlinear model.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.155-156
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• 2000

대기오염물질 중 오존은 대기성분 간의 화학반응에 의하여 광화학스모그를 형성하는 주요한 가스로서 지금까지 오존의 생성과 대기오염물질 및 기상과의 상관성을 이용한 오존 예측 연구가 다양하게 이루어져 왔다. 국내에서는 회귀모형을 이용한 오존농도 예측(허정숙등, 1993), 신경회로망을 이용한 오존농도 예측(김용국 등, 1994), Wavelet Transform을 이용한 단기오존농도 예측(김신도, 1998)등이 있고, 국외에서는 단기 오존예측(Feister ＆ Balzer; 1991), 선형모델을 이용한 오존예측(Cox, Chu, 1992), 비선형모델을 이용한 오존예측(Peter et, 1995)등이 있다. (중략)

• Environmental and Resource Economics Review
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• v.15 no.3
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• pp.405-423
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• 2006

This paper develops theoretical model between economic growth and pollution as follows: First, emissions are generated from final good production process and technology accumulation. Second, pollution is directly connected with increase in final good production or in consumption, Third, no pollution abatement activity would be undertaken. Fourth, reproducible factors associated with labor and capital input are used in production function. We also test the existence of nonlinear Dynamics between economic growth and pollution using an exponential smooth transition autoregressive model(ESTAR). We find the presence of nonlinear dynamics between economic growth and pollution with a time series data for Seoul. This result shows indirectly that an inverted U relationship between air pollution and economic growth exists.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.533-542
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• 2001

In order to establish drought policy, the estimation of drought period for each drought situation should be preceded. Non-linear Water Balance Model(NWBM) and palmer Drought Severity Index (PDSI) can be used for analysis of drought period. As a water balance method considering moisture transfer between land surface and atmosphere, NWBM can be used to estimate transition time between dry and wet period induced by stochastic fluctuations. PDSI is also water balance method to show drought severity comparing actual precipitation with climatically appropriate precipitation based on precipitation and potential evapotranspiration. In this study, the drought periods are estimated using NWBM and PDSI for the Han River Basin. The drought periods according to the soil moisture estimated by NWBS and the drought periods according to drought severity index estimated by PDSI show similar trend. The estimated drought period from extreme drought to wet condition for the Han River Basin is about 3years.

• Journal of the Korean earth science society
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• v.36 no.7
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• pp.611-616
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• 2015

El Nino and Southern Oscillation (ENSO) presents a broad band (2-8 year) variability and slowly changing amplitude and period, which are respectively referred to as ENSO irregularity and ENSO modulation. In this study, we developed a nonlinear low-order climate model by combining the Lorenz-63 model of nonlinear atmospheric variability and a simple ENSO model with recharge oscillator characteristics. The model successfully reproduced the ENSO-like variations in the sea surface temperature of eastern Pacific, such as the peak period, wide periodicity, and decadal modulations. The results show that the chaotic atmospheric forcing can lead to ENSO irregularity and ENSO modulation. It is also suggested the high probability of La Nina development could be associated with strong convection of the western warm pool. Although it is simple, this model is expected to be used in research on long-term climate change because it well captures the nonlinear air-sea interactions in the equatorial Pacific.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.473-486
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• 2001

The WGR model was developed to represent meso-scale precipitation. As a conceptual model, this model shows a good link between atmospheric dynamics and statistical description of meso-scale precipitation(Waymire et al., 1984). However, as it has maximum 18 parameters along with its non-linear structure, its parameter estimation has been remained a difficult problem. There have been several cases of its parameter estimation for different fields using non-linear programming techniques(NLP), which were also difficult tasks to hamper its wide applications. In this study, we estimated the WGR model parameters of the Han river basin using the genetic algorithm(GA) and compared them to the NLP results(Yoo and Kwon, 2000). As a result of the study, we can find that the sum of square error from the GA provide more consistent parameters to the seasonal variation of rainfall. Also, we can find that the higher rainfall amount during summer season is closely related with the arrival rate of rain bands, not the rain cell intensity.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1027-1031
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• 2008

본 연구는 한반도 영역을 대상으로 2001년 7, 8월과 2002년 6월로 홍수기를 대상으로 RDAPS 모형, AWS, 상층기상관측(upper-air sounding)의 자료를 이용하였다. 또한 수치예보자료를 범주적 예측확률로 변환하고 인공신경망기법(ANN)을 이용하여 강수발생확률의 예측정확성을 향상시키는데 있다. 신경망의 예측인자로 사용된 대기변수는 500/ 750/ 1000hpa에서의 지위고도, 500-1000hpa에서의 층후(thickness), 500hpa에서의 X와 Y의 바람성분, 750hpa에서의 X와 Y의 바람성분, 표면풍속, 500/ 750hpa/ 표면에서의 온도, 평균해면기압, 3시간 누적 강수, AWS관측소에서 관측된 RDAPS모형 실행전의 6시간과 12시간동안의 누적강수, 가강수량, 상대습도이며, 예측변수로는 강수발생확률로 선택하였다. 강우는 다양한 대기변수들의 비선형 조합으로 발생되기 때문에 예측인자와 예측변수 사이의 복잡한 비선형성을 고려하는데 유용한 인공신경망을 사용하였다. 신경망의 구조는 전방향 다층퍼셉트론으로 구성하였으며 역전파알고리즘을 학습방법으로 사용하였다. 강수예측성과의 질을 평가하기 위해서 $2{\times}2$ 분할표를 이용하여 Hit rate, Threat score, Probability of detection, Kuipers Skill Score를 사용하였으며, 신경망 학습후의 강수발생확률은 학습전의 강수발생확률에 비하여 한반도영역에서 평균적으로 Kuipers Skill Score가 0.2231에서 0.4293로 92.39% 상승하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.904-907
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• 2005

본 연구에서는 강우량의 실측치인 자동기상관측소(AWS) 자료와 현재의 대기상태인 인공위성(GMS-5호) 자료를 입력자료로 하여 현재부터 3시간 선행시간까지의 면적평균강우량을 예측할 수 있도록 강우예측 신경망 모형을 개발하였으며, 2002년 8월 집중호우시 남강댐 유역에 적용하였다. 신경망 모형의 학습을 위해서 $1998\~2001$년 $6\~9$월과 2002년 6, 7월의 강우사상과 적외선 자료가 사용되었고, 학습이 종료되면 예측기간(2002년 8월 $6\~16$일)동안의 강우예측이 수행되었다. 신경망 모형의 학습단계에서는 자료들간의 비선형 상관관계를 나타내는데 적합한 역전파 알고리즘 학습방법 중 모멘텀법을 사용하였으며, 신경망 모형의 출력값은 현재부터 3시간 후까지의 면적평균강우량을 예측할 수 있도록 구성하였다. 예측된 면적평균강우량은 실제 관측된 강우량의 패턴은 잘 따르고 있었지만 첨두치를 과소평가하는 경향이 나타났다. 본 연구에서 개발된 신경망 모형은 관측된 강우자료의 품질과 패턴이 모형의 정확성에 미치는 영향이 절대적인 기존의 신경망 모형과 차별화하여, 현재의 대기상태를 나타내는 인공위성 자료를 추가함으로써 보다 정확한 강우량 예측이 가능하도록 하였다.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.375-382
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• 2000

The surface hydrology of large watershed is susceptible to several preferred stable states with transitions between stable states induced by stochastic fluctuations. This comes about due to the close coupling of land surface and atmospheric interaction. An interesting and important issue is the duration of residence in each mode. In this study, mean transition tunes between the stable modes are analyzed for the Han River Basin. On the basis of historical data, the nonlinear water balance model is calibrated for the Han River Basin. The transition times between the stable modes in the model are studied based on the stochastic representation of the physical processes and on the calibrated model parameters. This study has implications for prediction of the transition time between stable modes or residence times, that is, the time the system spends in a given stable modes, since this would be equivalent to predicting the duration of drought or wet conditions.