• Atmosphere
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• v.30 no.2
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• pp.195-207
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• 2020

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3 → LD1) and the performance (r_s) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (r_s = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.

• Journal of Korean Society for Quality Management
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• v.31 no.2
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• pp.183-193
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• 2003

A special cause producing temporary deviation in the underlying process can influence on process adjustment in responsive feedback control system. In this paper, the impact of special causes on the EWMA(Exponentially Weighted Moving Average) forecasts and the process adjustment that is based on the EWMA forecasts are derived. For some special causes with patterned type of contamination, the influence of the causes on the output process are explicitly investigated. A data set, contaminated by a special cause of level shift, is analyzed to evaluate the impact numerically.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.19-32
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• 2000

Assignable causes producing temporary deviation from the underlying system can influence on process adjustment and process monitoring in dynamic feedback control system. In this paper, the impact of assignable causes on EWMA forecasts and process adjustment which is based on the EWMA forecasts are derived for optimum control methods.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.49-55
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• 2007

A special cause producing temporary deviation in the underlying process can influence on process adjustment in First-Order System feedback control system. In this paper, the impact of special causes on the forecasts and the process adjustment that is based on the EWMA forecasts are derived for a first-order system. For some special causes with patterned type of contamination, the influence of the causes on the output process are explicitly investigated. A data set, contaminated by a special cause of level shift, is analyzed to confirm the impact numerically.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.42-51
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• 2017

Snowfall is an important natural hazard in Korea. In recent years, the socioeconomic importance of impact-based forecasts of meteorological phenomena have been highlighted. To further develop forecasts, we first need to analyze the climatic characteristics of each region. In this study, homogeneous regions for snowfall analysis were classified using a self-organizing map for impact-based forecast and warning services. Homogeneous regions of snowfall were analyzed into seven clusters and the characteristics of each group were investigated using snowfall, observation days, and maximum snowfall. Daegwallyeong, Gangneung-si, and Jeongeup-si were classified as areas with high snowfall and Gyeongsangdo was classified as an area with low snowfall. Comparison with previous studies showed that representative areas were well distinguished, but snowfall characteristics were found to be different. The results of this study are of relevance to future policy decisions that use impact-based forecasting in each region.

• Atmosphere
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• v.30 no.3
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• pp.277-291
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• 2020

Low-level wind shear (LLWS) events on glide path at Jeju International Airport (CJU) are evaluated using the Aircraft Meteorological Data Relay (AMDAR) and Korea Meteorological Administration Post-Processing (KMAPP) with 100 m spatial resolution. LLWS that occurs in the complex terrains such as Mt. Halla on the Jeju Island affects directly aircraft approaching to and/or departing from the CJU. For this reason, accurate prediction of LLWS events is important in the CJU. Therefore, the use of high-resolution Numerical Weather Prediction (NWP)-based forecasts is necessary to cover and resolve these small-scale LLWS events. The LLWS forecasts based on the KMAPP along the glide paths heading to the CJU is developed and evaluated using the AMDAR observation data. The KMAPP-LLWS developed in this paper successfully detected the moderate-or-greater wind shear (strong than 5 knots per 100 feet) occurred on the glide paths at CJU. In particular, this wind shear prediction system showed better performance than conventional 1-D column-based wind shear forecast.

• Atmosphere
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• v.23 no.2
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• pp.171-186
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• 2013

Recently, the number of observations used in a data assimilation system is increasing due to the enormous amount of observations, including satellite data. However, it is not clear that all of these observations are always beneficial to the performance of the numerical weather prediction (NWP). Therefore, it is important to evaluate the effect of observations on these forecasts so that the observations can be used more usefully in NWP process. In this study, the adjoint-based Forecast Sensitivity to Observation (FSO) method with the KMA Unified Model (UM) is applied to two high-impact weather events which occurred in summer and winter in Korea in an effort to investigate the effects of observations on the forecasts of these events. The total dry energy norm is used as a response function to calculate the adjoint sensitivity. For the summer case, TEMP observations have the greatest total impact while BOGUS shows the greatest impact per observation for all of the 24-, 36-, and 48-hour forecasts. For the winter case, aircraft, ATOVS, and ESA have the greatest total impact for the 24-, 36-, and 48-hour forecasts respectively, while ESA has the greatest impact per observation. Most of the observation effects are horizontally located upwind or in the vicinity of the Korean peninsula. The fraction of beneficial observations is less than 50%, which is less than the results in previous studies. As an additional experiment, the total moist energy norm is used as a response function to measure the sensitivity of 24-hour forecast error to observations. The characteristics of the observation impact with the moist energy response function are generally similar to those with the dry energy response function. However, the ATOVS observations were found to be sensitive to the response function, showing a positive (a negative) effect on the forecast when using the dry (moist) norm for the summer case. For the winter case, the dry and moist energy norm experiments show very similar results because the adjoint of KMA UM does not calculate the specific humidity of ice properly such that the dry and moist energy norms are very similar except for the humidity in air that is very low in winter.

• 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 Environmental Science International
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• v.32 no.8
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• pp.543-553
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• 2023

Weather forecasts and advisories provided by the national organizations in Korea that are used to identify and prevent disaster associated damage are often ineffective in reducing disasters as they only focus on predicting weather events (World Meteorological Organization(WMO ), 2015). In particular, typhoons are not a single weather disaster, but a complex weather disaster that requires advance preparation and assessment, and the WMO has established guidelines for the impact forecasting and recommends typhoon impact forecasting. In this study, we introduced the Typhoon-Ready System, which is a system that produces pre-disaster prevention information(risk level) of typhoon-related disasters across Korea and in detail for each region in advance, to be used for reducing and preventingtyphoon-related damage in Korea.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.