• Atmosphere
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• v.20 no.4
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• pp.505-518
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• 2010

We investigated the impacts of the diabatic heating location, vertical profile and basic state on the Rossby wave propagation. To examine the dynamical process of individual responses on the regional heat source, a dry version of the linear baroclinic model was used with climatological summertime (JJA) mean basic state and vertical structure of the diabatic heating for 1979-2008. Two sets of diabatic heating were constructed of those positioned in the mid-latitudes (Tibetan Plateau, eastern Mediterranean Sea, and the west-central Asia) and the tropics (the southern India, Bay of Bengal, and western Pacific). It was found that using the principal component analysis, atmospheric response to diabatic heating reaches to the steady state in 19th days in time. The prescribed mid-latitude forcing forms equivalent barotropic Rossby wave propagation along the westerly Asia jets, whereas the tropical forcing generates the Rossby wave train extending from the tropics to mid-latitudes. In relation to the maximum vertical profile, the mid-level forcing reveals a stronger response than the lower-level forcing, which may be caused by more effective Rossby wave response by the upper-level divergent flow. Under the different sub-seasonal mean state, both of the tropical and mid-latitude forcing induce the different sub-seasonal response intensity, due to the different basic-state wind.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.15-31
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• 2003

In this study, we will construct a statistical model which considered the irregularity of observed time sequence in order to analyze sets of bioindex data gathered from stations in Lake Ontario for a number of years. We fit a linear model to account for the trend and seasonal component in an exploratory way and draw variogram and correlogram for further confirmatory studies.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.865-877
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• 2009

While characterized initially as an urban-scale pollutant, ozone has increasingly been recognized as a regional and even global-scale phenomenon. The complexity of environmental data dynamics often requires models covering non-linearity. This study deals with modeling ozone with meteorology in Seoul area. The relationships are used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology.

• Ocean and Polar Research
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• v.34 no.3
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• pp.287-296
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• 2012

In the theory of source-driven abyssal circulation, the forcing is usually assumed to be steady source (deep-water formation). In many cases, however, the deep-water formation occurs instantaneously and it is not clear whether the theory can be applied well in this case. An attempt is made to resolve this problem by using a simple reduced gravity model. The model basin has large depth change compared for its size, like the East Sea, such that isobaths nearly coincide with geostrophic contours. Deep-water is formed every year impulsively and flows into the model basin through the boundary. It is found that the circulation driven by the impulsive source is generally the same as that driven by a steady source except that the former has a seasonal fluctuation associated with unsteadiness of forcing. The magnitudes of both the annual average and seasonal fluctuations increase with the rate of deep-water formation. The problem can be approximated to that of linear diffusion of momentum with boundary flux, which well demonstrates the essential feature of abyssal circulation spun-up by periodic impulsive source. Although the model greatly idealizes the real situation, it suggests that abyssal circulation can be driven by a periodic impulsive source in the East Sea.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.9-15
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• 2002

This paper presents the seasonal forecasting of the occurrence of tropical cyclone (TC) over Western North Pacific (WNP) using the generalized linear model (GLM) and dynamic linear model (DLM) based on 51-year-data (1951-2001) in TC season (June to November). The numbers of TC and TY are predictands and 16 indices (the E1 Nino/Southern Oscillation, the synoptic factors over East asia and WNP) are considered as potential predictors. With 30-year moving windowing, the estimation and prediction of TC and TY are performed using GLM. If GLM forecasts have some systematic error like a bias, DLM is applied to remove the systematic error in order to improve the accuracy of prediction.

• 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.

• 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.