• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.1-4
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• 2002

• Atmosphere
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• v.16 no.3
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• pp.247-257
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• 2006

The Yeong-dong heavy snowfall forecast supporting system has been developed during the last several years. In order to construct the conceptual model, we have examined the characteristics of heavy snowfalls in the Yeong-dong region classified into three precipitation patterns. This system is divided into two parts: forecast and observation. The main purpose of the forecast part is to produce value-added data and to display the geography based features reprocessing the numerical model results associated with a heavy snowfall. The forecast part consists of four submenus: synoptic fields, regional fields, precipitation and snowfall, and verification. Each offers guidance tips and data related with the prediction of heavy snowfalls, which helps weather forecasters understand better their meteorological conditions. The observation portion shows data of wind profiler and snow monitoring for application to nowcasting. The heavy snowfall forecast supporting system was applied and tested to the heavy snowfall event on 28 February 2006. In the beginning stage, this event showed the characteristics of warm precipitation pattern in the wind and surface pressure fields. However, we expected later on the weak warm precipitation pattern because the center of low pressure passing through the Straits of Korea was becoming weak. It was appeared that Gangwon Short Range Prediction System simulated a small amount of precipitation in the Yeong-dong region and this result generally agrees with the observations.

• Atmosphere
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• v.18 no.3
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• Current Optics and Photonics
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• v.4 no.3
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• pp.174-185
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• 2020

Multiscattering occurs when a laser transmits into dense atmosphere targets (e.g. fogs, smoke or clouds), which can cause depolarization effects even though the scattering particles are spherical. In addition, multiscattering effects have additional information about microphysical properties of scatterers. Thus, multiscattering can be utilized to study the microphysical properties of the liquid water cloud. In this paper, a Monte Carlo method was used to simulate multi-scattering transmission properties of Lidar signals in the cloud. The results showed the slope of the degree of linear polarization (SLDLP) can be used to invert the extinction coefficient, and then the cloud effective size (CES) and the liquid water content (LWC) may be easily obtained by using the extinction coefficient and saturation of the degree of linear polarization (SADLP). Based on calculation results, a microphysical properties inversion method for a liquid cloud was presented. An innovative multiscattering polarization Lidar (MSPL) system was constructed to measure the LWC and CES of the liquid cloud, and a new method based on the polarization splitting ratio of the Polarization Beam Splitter (PBS) was developed to calibrate the polarization channels of MSPL. By analyzing the typical observation data of MSPL observation in the northern suburbs of Nanjing, China, the LWC and CES of the liquid water cloud were obtained. Comparisons between the results from the MSPL, MODIS and the Microwave radar data showed that, the microphysical properties of liquid cloud could be retrieved by combining our MSPL and the inversion method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.89-98
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• 2007

In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user's decision under the specific payoff structure, which is represented by a profit/loss ratio model combined with a decision function and a value score (VS). The forecast user determines a business-related decision based on the probabilistic forecast, the user's subjective reliability of the forecasts, and the payoff structure specific to the user's business environment. The VS curve for a meteorological forecast is specified by a function of the various profit/loss ratios, providing the scaled economic value relative to the value of a perfect forecast. The proposed valuation method based on the profit/loss ratio model and the VS is adapted for hypothetical sets of forecasts and verified for site-specific probability of precipitation forecast of 12 hour and 24 hour-lead time, which is generated from Korea meteorological administration (KMA). The application results show that forecast information with shorter lead time can provide the decision-makers with great benefits and there are ranges of profit/loss ratios in which high subjective reliability of the given forecast is preferred.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.382-395
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• 2013

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the $2{\times}2$ contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group's satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

• 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.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.139-158
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• 2007

Information sharing is key to effective supply chain management. In reality, however, it is impossible to get perfect information. Accordingly, only uncertain information can be accessed in business environment, and thus it is important to deal with the uncertainties of information in managing supply chains. This study adopts meteorological forecast as a typical uncertain information. The meteorological events may affect the demands for various weather-sensitive goods, such as beer, ices, clothes, electricity etc. In this study, a beer distribution game is modified by introducing meterological forecast information provided in a probabilistic format. The behavior patterns of the modified beer supply chains are investigated. for two conditions using the weather forecast with or without an information sharing. A value score is introduced to generalize the well-known performance measures employed in the study of supply chains, i.e.. inventory, backlog, and deviation of orders. The simulation result showed that meterological forecast information used in an information sharing environment was more effective than without information sharing, which emphasizes the synergy of uncertain information added to the information sharing environment.

• Atmosphere
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• v.20 no.1
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• pp.13-26
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• 2010

The Urban Canopy Model (UCM) implemented in WRF model is applied to improve urban meteorological forecast for fine-scale (about 1-km horizontal grid spacing) simulations over the city of Seoul. The results of the surface air temperature and wind speed predicted by WRF-UCM model is compared with those of the standard WRF model. The 2-m air temperature and wind speed of the standard WRF are found to be lower than observation, while the nocturnal urban canopy temperature from the WRF-UCM is superior to the surface air temperature from the standard WRF. Although urban canopy temperature (TC) is found to be lower at industrial sites, TC in high-intensity residential areas compares better with surface observation than 2-m temperature. 10-m wind speed is overestimated in urban area, while urban canopy wind (UC) is weaker than observation by the drag effect of the building. The coupled WRF-UCM represents the increase of urban heat from urban effects such as anthropogenic heat and buildings, etc. The study indicates that the WRF-UCM contributes for the improvement of urban weather forecast such nocturnal heat island, especially when an accurate urban information dataset is provided.

• Atmosphere
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• v.30 no.2
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• pp.141-154
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• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.