• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.95-105
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• 1996

Meteorological Joint Frequency Function required indispensably in long-term air quality prediction models were discussed for practical application in Korea. The algorithm, proposed by Turner(l964), is processed with daily solar insolation and cloudiness and height basically using Pasquill's atmospheric stability classification method. In spite of its necessity and applicability, the computer program, called STAR(STability ARray), had some significant difficulties caused from the difference in meteorological data format between that of original U.S. version and Korean's. To cope with the problems, revised STAR program for Korean users were composed of followings; applicability in any site of Korea with regard to local solar angle modification; feasibility with both of data which observed by two classes of weather service centers; and examination on output format associated with prediction models which should be used.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• Journal of Korean Society for Atmospheric Environment
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• v.3 no.1
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• pp.34-40
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• 1987

The Texas Climatological Model (TCM) Predicts long-term pollutant concentrations for a rectilinear array or receptors defined by the user. This paper describes the TCM and compares predictions from TCM with predictions from the Climatological Dispersion Model (CDMQC). A number of model runs have been made with the TCM and CDMQC using the same source inventories and sets of climatology. The concentrations predicted by these two models are compared and the result of several types of statistical analyses are reported. In most cases, the TCM predicts concentrations that are equivalent to those predicted by the CDMQC. However, in certain cases, the CDMQC tends to predict concentrations that are unrealistically high. In the computer time, the TCM requires about one-eights of the computer time used by the CDMQC.