• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.473-482
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• 2015

Doppler weather radar is an important tool for meteorological research. Through several decades of development, Doppler weather radar has enormous progress in understanding, detection and warning of meso and micro scale weather system. It makes a significant contribution to weather forecast and weather disaster warning. But the large amount of data process limits the application of Doppler weather radar. This paper proposed for fast weather radar data processing based on CUDA. CDUA is a powerful platform for highly parallel programming developed by NVIDIA. Through running plenty of threads, radar data can be calculated at same time. In experiment, CUDA parallel program can significantly improve weather data processing time.

• Journal of Environmental Science International
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• v.12 no.4
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• pp.399-417
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• 2003

The seasonal variation and frequency of rainfalls of Korea peninsula in Changma period show strong local weather phenomenon because of it's topographical and geographical factors in Northeast side of Asia. Based on weather entropy(statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical and geographical factors and seasonal variation. The data used for this study are the daily precipitation and cloudiness during the recent ten years(1990-1999) at the 73 stations in Korea. To synthesize weather Entropy, information ratio of decaying tendency and half$.$decay distance, Seoul's weather representativeness has the smallest in Summer Changma period. And Puyo has the largest value in September.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.117-129
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• 2018

This study examines the impact of severe weather on lean supply chains. First, this paper reviewed the literature on the disruptions and damages that severe weather events cause on supply chain. Then, several recent examples of lean supply chain disruptions due to severe weather were discussed. The results of the study indicated that the frequency of weather related disasters is increasing and extreme weather events will increase potential risks to supply chains. First, building organizational resilience will help firms look beyond efficiency and profits in managing lean supply chains. Second, the concept of sole sourcing may need rethinking to maintain a supply chain that is lean and resilient. Third, organizations must plan ahead for supply chains in unpredictable weather. Fifth, communication is a key for anticipating and avoiding the impact of severe weather. This study proposes of a set of strategies, both theoretical and practical, that business firms should develop to effectively prevent and respond to severe weather related disruptions in lean supply chains.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.229-237
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• 2011

Today they use a weather radar with spatially high resolution in predicting rainfall intensity and utilizing the information for super short-range forecast in order to make predictions of such severe meteorological phenomena as heavy rainfall and snow. For a weather radar, they use the Z-R relation between the reflectivity factor(Z) and rainfall intensity(R) by rainfall particles in the atmosphere in order to estimate intensity. Most used among the various Z-R relation is $Z=200R^{1.6}$ applied to stratiform rain. It's also used to estimate basic rainfall intensity of a weather radar run by the weather center. This study set out to compare rainfall intensity between the reflectivity of a weather radar and the ground rainfall of ASOS(Automatic Surface Observation System) by analyzing many different cases of heavy rain, analyze the errors of different weather radars and identify their problems, and investigate their applicability to nowcasting in case of severe weather.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.1-12
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• 2016

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.365-374
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• 2012

Weather derivatives designed to manage casual changes of weather, as opposed to catastrophic risks of weather, are relatively a new class of financial instruments. There are still many theoretical and practical challenges to the effective use of these instruments. The objective of this paper is to develop a pricing approach for valuing weather derivatives and presents a case study that is practical enough to be used by the risk managers of electrical utility firms. Utilizing daily average temperature data of Guangzhou, China from $1^{st}$ January 1978 to $31^{st}$ December 2010, this paper adopted a univariate time series model to describe weather behavior dynamics and calculates equilibrium prices for weather futures and options for an electrical utility firm in the region. The results imply that the risk premium is an important part of derivatives prices and the market price of risk affects option values much more than forward prices. It also demonstrates that weather innovation as well as weather risk management significantly affect the utility's financial outcomes.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.93-116
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• 2017

Purpose: The purpose of this study is to suggest a key quality factor affecting user satisfaction based on quality attribute of weather information system and additionally, a managerial implication for activating weather information service market by analyzing causal relevance between weather information system and business performance. Methods: As its method, a causal relevance of quality factor of weather information system affecting satisfaction and business performance was analyzed by using structural equation model. An enterprise utilized in analysis sample performed a survey by targeting 186 distribution/logistics and general manufacturing company. Results: It was revealed that information, service quality of weather information system exerted a significant influence on user satisfaction and it did not take a significant influence on business performance in terms of quality of information, system and service. A significant relevance between user satisfaction and business performance was strong supported. Finally, it was revealed that utilization level of weather information system had a moderating effect on business performance. Conclusion: Domestic weather information system mainly focused on information utilization based weather information system and additional service level utilizing weather information is very low in practical terms. Therefore, in order to expand domestic weather related service market, systematic market expansion strategy of providing corporate customized service and case enlargement would be required.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.153-162
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• 2017

Today, weather radar is used as a key tool for modern high-tech weather observations and forecasts, along with a wide variety of ground gauges and weather satellites. In this paper, we propose a frequency transform based weather radar image processing technique to improve the weather radar image damaged by beam blocking and clutter removal in order to minimize the uncertainty of the weather radar observation. In the proposed method, DCT based mean energy correction is performed to improve damage caused by beam shielding, and DWT based morphological image processing and high frequency cancellation are performed to improve damage caused by clutter removal. Experimental results show that the application of the proposed method to the damaged original weather radar image improves the quality of weather radar image adaptively to the weather echo feature around the damaged area. In addition, radar QPE calculated from the improved weather radar image was also qualitatively confirmed to be improved by the damage. In the future, we will develop quantitative evaluation scales through continuous research and develop an improved algorithm of the proposed method through numerical comparison.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.490-494
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• 2008

We simulated a forest fire which was occurred in Yangyang area on 2005 and compared a results between two different weather conditions(real weather condition and mean weather condition since 1968) using FARSITE, which is a forest fire spread simulator for preventing and predicting fire in USDA. And, we researched a problem in the transition for introducing, so we serve the basic method for prevention and attacking fire. In the result, severe weather condition on 2005 effected a forest fire behavior. The rate of spread under real weather condition was about 4 times faster than mean weather condition. Damaged area was about 10 time than mean weather condition. Therefore, Climate change will make a more sever fire season. As we will encounter to need for accurate prediction in near future, it will be necessary to predict a forest fire linked with future wether and fuel condition.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.243-246
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• 2009

To evaluate the building energy performance, many building simulation programs are used and its capabilities are developed. Despite of its increased capabilities the weather data used In the Building Energy performance evaluation, are still using the same limited set of data. This often forces users to find or calculate weather data such as illuminance, solar radiation, and ground temperature from other sources to calculate it. Also, proper selection of a right weather data set has been considered as one of important factors for a successful building energy simulation. In this paper, we describe TMY2 data, a generalized weather data format developed for use, and applied to Seoul region and examine the differences comparing to existing weather data. A set of 23 years raw weather data base has been developed to provide the weather data file for building energy analysis in Seoul.