• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.20-27
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• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• KIEAE Journal
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• v.3 no.3
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• pp.67-74
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• 2003

The repair and maintenance planning is the principal transaction to prevent from the degradation of apartment buildings to prolong their lives. Various building components as part of living area should be maintained properly and timely before critical defects are realized, then the building life might be extended until the limit of its usability. In other words, various building components such as utilities, finishes and structural members are needed to be repaired and replaced in different time interval after completion. To do this systematically, a maintenance management system for Long-Range Planning (LRP) needs developing. The LRP should be figured out based on the forecast of repairing and replacing cycle of building components according to work trades. And the precise forecast of repairing and replacing time of the components helps to enhance the usability of the developed system. The purpose of this paper is to suggest a system with which apartment building managers carry out the tasks of periodical check, diagnosis and replacement of building components based on the maintenance calendar. By using the system, they can easily forecast repairing and replacing time of the components with the consideration of life cycle of building materials and build the LRP.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.209-218
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• 2024

The National Institute of Agricultural Sciences (NAS) operates in-house long-range climate forecasting system to support the agricultural use of climate forecast data. This system, developed through collaborative research with Pusan National University, is based on the PNU/RDA Coupled General Circulation Model (CGCM) and includes the regional climate model WRF (Weather Research and Forecasting). It generates detailed climate forecast data for periods ranging from 1 to 6 months, covering 20 key variables such as daily maximum, minimum, and average temperatures, precipitation, and agricultural meteorological elements like solar radiation, soil moisture, and ground temperature-factors essential for agricultural forecasting. The data are provided at a daily temporal resolution with a spatial resolution of a 5km grid, which can be used in point form (interpolated) or averaged across administrative regions. The system's seasonal temperature and precipitation forecasts align closely with observed climatological data, accurately reflecting spatial and topographical influences, confirming its reliability. These long-range forecasts from NAS are expected to offer valuable insights for agricultural planning and decision-making. The detailed forecast data can be accessed through the Climate Change Assessment Division of NAS.

• Journal of Technology Innovation
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• v.5 no.1
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• pp.223-248
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• 1997

In this study the comparative analysis of four nations' Delphi technological forecasts in information and communication industry was carried out. The nations were Korea, Japan, Germany and France. The reliability test of realization time forecast was also conducted. There were some technologies of which four nations forecast almost same realization time whereas other technologies which four nations showed large variance in forecasting realization time. This means that experts from different nations had different views and prospects on the same technology. It is expected that prospecting international technological trend from this study will contribute to formulating long-range plan for technological development in information and communication in Korea. The result of the comparative analysis of four nations' forecasts in information and communication technology can be summarized as follows. a)As for the realization time, most of the technologies were forecast by four nations to be similar, the differences ranging from one to three years. It was found that on the whole, the longer the time of the forecast, the bigger the variance. The German forecast showed the biggest variance. b)In reliability test Korean was found to be the most reliable and Japan, France and Germany were in descending order. The response of all panel members was found to be more reliable than that of the panel members with high expertise, which means that there were substantially different views among panel members with high expertise on the realization time of the same technology.

• Korea journal of population studies
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• v.8 no.1
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• pp.87-117
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• 1985

In Principle, the distriction should be understood between projections and forecasts. When the author or user of a projection is willing to describe it as indicating the most likely population at a give date, then he has made a forecast Population change since 1 960 has been reviewed briefly in order to forecast the population of Korea in the year 2,000 which is a leading factor in long term national development plan for which Korea Institute for Population and Health (KIPH) has been participated since 1983. The author of this paper introduced the population forecast prepared for the long term national development plan and an attempt of comparisons with other forecasts such as D.P. Smith's, T. Frejka's, Economic Planning Board's (EPB), UN's and S.B. Lee's was made. Those six forecasts of Korean future population in year 2,000 varried from 48.5 million to 50.0 million due to the base population and assumption of fertility and mortality however the range of total population size is not large enough. Taking four forecasts such as KIPH, EPB, UN, and Lee based on 1980 population census results and latest data of fertility and mortality, KIPH and UN forecast are close in total population size even though there was a slight difference in fertility and mortality assumptions. The smallest size of total population was shown by S.B. Lee (see Table 13) although the difference between KIPH and Lee was approximately one million which is two percent of total population in year 2,000. As a summary of conclusion the author pointed out that one can take anyone of forecasts prepared by different body because size and proportion wise of the Korean population until early I 990s can not be different much and new population projections must be provided by using 1985 population census data and other latest fertility and mortality information coflected by Korea Institute for Population and Health and Economic Planning Board in forth comming year.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1758-1761
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• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.111-117
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• 1992

In the stock marketing. investor needs speedy and accurate decision making for the investment. A stock exchange index provides the important index of the early of 1993 in Korea using Fuzzy Delphi Method(F. D. M) which is widely used to a mid and long range forecasting in decision making problem. In the Fuzzy Delphi method, considerably qualified experts an first requested to give their opinion seperately and without intercommunication. The forecasting data of experts consist of Triangular Fuzzy Number (T.F.N) which represents the pessimistic, moderate, and optimistic forecast of a stock exchange index. A statistical analysis and dissemblance index are then made of these subject data. These new information are then transmitted to the experts once again, and the process of reestimation is continued until the process converges to a reasonable stable forecast of stock exchange index. The goal of this research is to forecast the stock exchange index using F.D.M. in which subjective data of experts are transformed into quasi -objective data index by some statistical analysis and fuzzy operations. (a) A long range forecasting problem must be considered as an uncertain but not random problem. The direct use of fuzzy numbers and fuzzy methods seems to be more compatible and well suited. (b) The experts use their individual competency and subjectivity and this is the very reason why we propose the use of fuzzy concepts. (c) If you ask an expert the following question: Consider the forecasting of the price index of stocks in the near future. This experts wi11 certainly be more comfortable giving an answer to this question using three types of values: the maximum value, the proper value, and the minimum value rather than an answer in terms of the probability.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Atmosphere
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• v.27 no.3
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• pp.359-370
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• 2017

In this study, the oceanic Total Precipitable Water (TPW) retrieval algorithm at 16 km altitude of High Altitude Long Endurance Unmanned Aerial Vehicle (HALE UAV) is described. Empirical equation based on Wentz method (1995) that uses the 18.7 and 22.235 GHz channels is developed using the simulated brightness temperature and SeeBor training dataset. To do radiative simulation, Satellite Data Simulator Unit (SDSU) Radiative Transfer Model (RTM) is used. The data of 60% (523) and 40% (349) in the SeeBor training dataset are used to develop and validate the TPW retrieval algorithm, respectively. The range of coefficients for the TPW retrieval at the altitude of 3~18 km with 3 km interval were 153.69~199.87 (${\alpha}$), 54.330~58.468 (${\beta}$), and 84.519~93.484 (${\gamma}$). The bias and RMSE at each altitude were found to be about $-0.81kg\;m^{-2}$ and $2.17kg\;m^{-2}$, respectively. Correlation coefficients were more than 0.9. Radiosonde observation has been generally operated over land. To validate the accuracy of the oceanic TPW retrieval algorithm, observation data from the Korea Meteorological Administration (KMA) Gisang 1 research vessel about six clear sky cases representing spring, autumn, and summer season is used. Difference between retrieved and observed TPW at 16 km altitude were in the range of $0.53{\sim}1.87kg\;m^{-2}$, which is reasonable for most applications. Difference in TPW between retrieval and observation at each altitude (3~15 km) is also presented. Differences of TPW at altitudes more than 6 km were $0.3{\sim}1.9kg\;m^{-2}$. Retrieved TPW at 3 km altitude was smaller than upper level with a difference of $-0.25{\sim}0.75kg\;m^{-2}$ compared to the observed TPW.