• Journal of Korean Society of Archives and Records Management
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• v.19 no.2
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• pp.109-145
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• 2019

Citizens are becoming providers of weather information through crowdsourcing on the Internet. In Korea and abroad, national weather service organizations and companies are using weather information provided by citizens for weather forecasting. Recently, it is necessary to pay attention to the changes and the current status of the producers of meteorological information in the meteorological field as they are aware of the importance of information management including data in academia. In this paper, first, the present status and problems of the weather observation network constructed by each weather information producer were identified. Second, to confirm the crowdsourcing in the meteorological area, the researchers directly participated in the weather forecasting process through crowdsourcing and analyzed the collection, utilization, and the possibility of weather record information. Third, prospects for the utilization of weather information through crowdsourcing were presented.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• 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.

• Journal of Industrial Technology
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• v.15
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• pp.187-193
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• 1995

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.300-301
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• 2015

Accurate and robust load forecasting model plays very important role in power system operation. In case of short-term electric load forecasting, its results offer standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve accuracy of load forecasting. This paper proposes a newly forecasting model for weather sensitive season including temperature and Cooling Degree Hour(C.D.H) data as an input. This Forecasting model consists of previous electric load and preprocessed temperature, constant, parameter. It optimizes load forecasting model to fit actual load by PSO and results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows better performance than comparison groups.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.121-126
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• 2006

Modern Ocean wave forecasting systems predict the mean sea state, as characterized by the wave spectrum, in a box of size ${\Delta}x{\Delta}y$ surrounding a grid point at location x. It is shown that this approach also allows the determination of deviations from the mean sea state, i.e. the probability distribution function of the surface elevation. Hence, ocean wave forecasting may provide valuable information on extreme sea states.

• Journal of Biosystems Engineering
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• v.17 no.2
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• pp.177-191
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• 1992

The MAFSAP(Monitoring and Forecasting System of Air Pollution) was developed to measure the weather and air pollution data automatically, then make them input to microcomputer and analyze them for monitoring and forecasting air pollution at all times. And the air pollution telemetering systems installed at Young-Dong Thermal Power Plant was analyzed and an ideal telemetering system utilizing MAFSAP was suggested.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.360-365
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• 2014

There are many types of advanced devices for weather prediction process such as weather radar, satellite, radiosonde, and other weather observation devices. Among them, the weather radar is an essential device for weather forecasting because the radar has many advantages like wide observation area, high spatial and time resolution, and so on. In order to analyze the weather radar observation result, we should know the inside structure and data. Some non-precipitation echoes exist inside of the observed radar data. And these echoes affect decreased accuracy of weather forecasting. Therefore, this paper suggests a method that could remove line-shaped non-precipitation echo from raw radar data. The line-shaped echoes are distinguished from the raw radar data and extracted their own features. These extracted data pairs are used as learning data for naive bayesian classifier. After the learning process, the constructed naive bayesian classifier is applied to real case that includes not only line-shaped echo but also other precipitation echoes. From the experiments, we confirm that the conclusion that suggested naive bayesian classifier could distinguish line-shaped echo effectively.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.44-55
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• 1994

This paper describes the development of real-time irrigation reservoir operation models that adequately allocate available water resources for paddy rice irrigation. Water requirement deficiency index(WRDI) was proposed as a guide to evaluate the operational performance of release schemes by comparing accumulated differences between daily release requirements for irrigated areas and actual release amounts. Seven reservoir release rules were developed, which are constant release rate method (CRR), mean storage curve method(MSC), frequency analysis method of reservoir storage rate(FAS), storage requirement curve method(SRC), constant optimal storage rate method (COS), ten-day optimal storage rate method(TOS), and release optimization method(ROM). Long-term forecasting reservoir operation model(LFROM) was formulated to find an optimal release scheme which minimizes WRDIs with long-term weather generation. Rainfall sequences, rainfall amount, and evaporation amount throughout the growing season were to be forecasted and the results used as an input for the model. And short-term forecasting reservoir operation model(SFROM) was developed to find an optimal release scheme which minimizes WRDIs with short-term weather forecasts. The model uses rainfall sequences forecasted by the weather service, and uses rainfall and evaporation amounts generated according to rainfall sequences.

• 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.