• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.125-137
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• 1996

Time series data are often contaminated with outliers due to influence of unusal and non-responsitive events. The effect of the outliers is larger in the time series analysis than in the other statistical analysis, because the time series data have dependent structure over time. This paper focuses on the effect of patchy outliers on forecasting. Especially, the increase of the mean square of the l-step-ahead forecast error is derived and used to evaluate the impact of those outliers on the forecast. We fine, in general, that this increase is rather small, provided that the patchy outliers does not occur too close to the forecast origin.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.49-56
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• 2023

Impact based forecast refers to providing information on potential socioeconomic risks according to weather conditions, away from the existing weather factor-oriented forecast. Developed weather countries are investing manpower and finances in technology development to provide and spread impact information, but awareness of impact based forecasts has not spread in Korea. In addition, the focus is on disasters such as floods and typhoons, which cause a lot of damage to impact based forecasts, and research on evaluating the impact of vehicle risks due to strong winds in the transportation sector with relatively low damage is insufficient. In Korea, there are not many cases of damage to vehicle conduction caused by strong winds, but there are cases of damage and the need for research is increasing. Road vulnerability is required to evaluate the risk of vehicles caused by strong winds, and the purpose of this study was to calculate the criteria for road vulnerability. The road vulnerability evaluation was evaluated by the altitude of the road, the number of lanes, the type of road. As a result of the analysis, it was found that the vulnerable area was well reproduced. It is judged that the results of this study can be used as a criterion for preparing an objective evaluation of potential risks for vehicle drivers.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.41-53
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• 2014

The special observation using Radiosonde was performed to investigate precipitation events over the east coast of Korea during the winter season from 5 January to 29 February 2012. This analysis focused on the various indices to describe the characteristics of the atmospheric instability. Equivalent Potential Temperature (EPT) from surface (1000 hPa) to middle level (near 750 hPa) was increased when the precipitation occurred and these levels (1000~750 hPa) had moisture enough to cause the instability of atmosphere. The temporal evolution of Convective Available Potential Energy (CAPE) appeared to be enhanced when the precipitation fell. Similar behavior was also observed for the temporal evolution of Storm Relative Helicity (SRH), indicating that it had a higher value during the precipitation events. To understand a detailed structure of atmospheric condition for the formation of precipitation, the surface remote sensing data and Automatic Weather System (AWS) data were analyzed. We calculated the Total Precipitable Water FLUX (TPWFLUX) using TPW and wind vector. TPWFLUX and precipitation amount showed a statistically significant relationship in the north easterly winds. The result suggested that understanding of the dynamical processes such as wind direction be important to comprehend precipitation phenomenon in the east coast of Korea.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.855-862
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• 1998

This paper investigates the impact of the forecast error on performance of a reservoir system for hydropower production. Forecast error is measured as th Root Mean Square Error (RMSE) and parametrically varied within a Generalized Maintenance Of Variance Extension (GMOVE) procedure. A set of transition probabilities are calculated as a function of the RMSE of the GMOVE procedure and then incorporated into a Bayesian Stochastic Dynamic Programming model which derives monthly operating policies and assesses their performance. As a case study, the proposed methodology is applied to the Skagit Hydropower System (SHS) in Washington state. The results show that the system performance is a nonlinear function of RMSE and therefor suggested that continued improvements in the current forecast accuracy correspond to gradually greater increase in performance of the SHS.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.183-188
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• 2002

The forecast of a container traffic has been very important for port and development. Generally, Statistic methods, such as moving average method, exponential smoothing, and regression analysis have been much used for traffic forecasting. But, considering various factors related to the port affect the forecasting of container volume, neural network of parallel processing system can be effective to forecast container volume based on various factors. This study discusses the forecasting of volume by using the neural, network with back propagation learning algorithm. Affected factors are selected based on impact vector on neural network, and these selected factors are used to forecast container volume. The proposed the forecasting algorithm using neural network was compared to the statistic methods.

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.853-861
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• 2011

The objective of this study is to assess Sejong University River Forecast (SURF) model which consists of a continuous rainfall-runoff model and measured streamflow assimilation using ensemble Kalman filter technique for streamflow forecast on Nakdong river basin. The study area is divided into 43 subbasins. The forecasted streamflows are evaluated at 12 measurement sites during flood season from 2006 to 2007. The forecasted ones are improved due to the impact of the measured streamflows assimilation. In effectiveness indices corresponding to 1~5 h forecast lead times, the accuracy of the forecasted streamflows with the assimilation approach is improved by 46.2~30.1% compared with that using only the rainfall-runoff model. The mean normalized absolute error of forecasted peak flow without and with data assimilation approach in entering 50% of the measured rainfall, respectively, the accuracy of the latter is improved about 40% than that of the former. From these results, SURF model is able to be used as a real-time river forecast model.

• Atmosphere
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• v.29 no.4
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• pp.439-450
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• 2019

Present weather plays an important role not only for atmospheric sciences but also for public welfare and road safety. While the widely used state-of-the-art visibility and present weather sensor yields present weather, a single type of measurement is far from perfect to replace long history of human-eye based observation. Truly automatic present weather observation enables us to increase spatial resolution by an order of magnitude with existing facilities in Korea. 8 years of human-eyed present weather records in 19 sites over Korea are compared with visibility sensors and auxiliary measurements, such as humidity of AWS. As clear condition agrees with high probability, next best categories follow fog, rain, snow, mist, haze and drizzle in comparison with human-eyed observation. Fog, mist and haze are often confused due to nature of machine sensing visibility. Such ambiguous weather conditions are improved with empirically induced criteria in combination with visibility and humidity. Differences between instrument manufacturers are also found indicating nonstandard present weather decision. Analysis shows manufacturer dependent present weather differences are induced by manufacturer's own algorithms, not by visibility measurement. Accuracies of present weather for haze, mist, and fog are all improved by 61.5%, 44.9%, and 26.9% respectively. The result shows that automatic present weather sensing is feasible for operational purpose with minimal human interactions if appropriate algorithm is applied. Further study is ongoing for impact of different sensing types between manufacturers for both visibility and present weather data.

• The Journal of Industrial Distribution & Business
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• v.9 no.10
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• pp.83-89
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• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• Atmosphere
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• v.21 no.1
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• pp.85-93
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• 2011

UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.

• Atmosphere
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• v.26 no.1
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.