• 한국수자원학회논문집
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• 제44권12호
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• pp.967-974
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• 2011

RDAPS 수치예보로부터 생산된 일단위 강우시계열을 바탕으로 유량 예측을 모의하고, 정성적인 중장기 예보를 고려한 ESP 분석을 수행하여 결과를 비교하고 적용성을 검토하였다. 금강유역을 대상으로 ESP, 정성적 기상예보를 고려한 ESP, RDAPS 기상수치예보에의한유량예측결과를평균유출량과비교 분석을 통해각기법별 결과의 개선효과를 평가하였다. 예측 모의 결과 기상정보를 고려한 ESP 방법의 결과가상대적으로 양호한 것으로 분석되었다. 확률예측의 정확도를 평가하기 위한 불일치율(Discrepancy Ratio) 분석 결과에서도 같은 결과를 얻었다. RDAPS 수치예보의 경우 3시간 단위의 누적강수라는 특성이 감안된 시간분해능을 갖는 일단위 시나리오로 개선되거나 장기간 동안 지속적인 모의 평가가 이루어진다면 더욱 정밀한 유량예측을 모의 할 수 있을 것으로 예상된다.

• The Plant Pathology Journal
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• 제36권1호
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• 한국항공운항학회지
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• 제15권3호
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• pp.57-62
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• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• 한국태양에너지학회 논문집
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• 제33권6호
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

• 한국농공학회지
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• 제41권6호
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• pp.25-32
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• 1999

The purpose of this study is to analyse the accuracy of weather forecasts of temperature, precipitation probability , and sky condition and to evaluate the applicability of weather forecasts for the estimation of potential evapotranspiration for irrigation scheduling. Five weather station s were selected to compare forecasted and measured climatcal data. The error between forecasted and measured temperature was calculated and discussed. The accuracy of temperature forecast using relative frequency of the error was calculated . The temperature forecasting showed considerably high accuracy. Average sunshine hours for forecasted sky conditions were calculated and showed reasonable quality. From the reliability graphs, the forecasting precipation probabililty was reliable. Potential evapotranspirations were calculated and compared using forecast and measured temperatures. The weather forecast is considered usable for irrigation scheculing.

• 대기
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• 제32권4호
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• pp.307-322
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• 2022

A drone has recently got attention as an instrument for weather observation in lower atmosphere because it can produce the high spatiotemporal resolution weather data even though the weather phenomenon is inaccessible. Sea fog is a weather phenomenon occurred in lower atmosphere, and has observational limitations because it occurs on the sea. Therefore, goal of this study is to analyze the vertical structures about inflow, development and dispersion of sea fog using the high-resolution weather data with the meteorological sensor-equipped drone. This study observed sea fogs in the west coast of the Korean peninsula from March to October 2021 and investigated one sea fog inflowed into the coast on June 8th 2021. θ_e - q_v diagrams (θ_e: equivalent potential temperature, q_v: water vapor ratio) and vertical wind structures were analyzed. At inflow of sea fog, moist adiabatically stable layer was formed in 0-300 m and prevailing wind was switched from south-southwesterly to west-southwesterly under 120 m. Both changes are favorable for sea fog on the location. θ_e and q_v plummeted in a layer 0-183 m. The inflowed sea fog developed from 183 m to 327 m by mixing with ambient atmosphere on top of sea fog. Also, strong mechanical turbulence near ground drove a vertical mixing under stable layer. At dispersion of sea fog, as θ_e on ground gradually increased, air condition was changed to neutral. Evaporation occurred on both bottom and top in sea fog. These results induced dissipation of sea fog.

• 한국의류산업학회지
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• 제19권3호
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• pp.289-295
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• 2017

This study aims to develop clothing sales forecast system using weather information. As the annual temperature variation affects changes in daily sales of seasonal clothes, sales period can be predicted growth, peak and decline period by changes of temperature. From this perspective, we analyzed the correlation between temperature and sales. Moving average method was applied in order to indicate long-term trend of temperature and sales changes. 7-day moving average temperature at the start/end points of the growth, peak, and decline period of S/S clothing sales was calculated as a reference temperature for sales forecast. According to the 2013 data analysis results, when 7-day moving average temperature value becomes $4^{\circ}C$ or higher, the growth period of S/S clothing sales starts. The peak period of S/S clothing sales starts at $17^{\circ}C$, up to the highest temperature. When temperature drops below $21^{\circ}C$ after the peak temperature, the decline period of S/S clothing sales is over. The reference temperature was applied to 2014 temperature data to forecast sales period. Through comparing the forecasted sales periods with the actual sales data, validity of the sales forecast system has been verified. Finally this study proposes 'clothing sales forecast system using weather information' as the method of clothing sales forecast.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• 한국환경과학회지
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• 제5권4호
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• pp.399-410
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• 1996

Pusan is the largest coastal city with a population of about four mi18ion in Korea. Because of increased and confused traffic, photochemical air pollution become a major urban environmental problem recently. The photo-chemical air pollution weather forecasting method preciser than existing air pollution forecast method has been developed to forecast ozone episode days with meteorological conditions using the data measured at 7 air quality continuous monitoring stations from lune to September using 2 years (1994, 1995). The method developed in present study showed higher percentage correct and skill score than existing air pollution forecasting in KMA ( Korea Meteorological Administration).

• 대기
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• 제14권3호
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• pp.41-54
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• 2004

In this study, a brief overview on a WMO/WWRP program - The Observing System Research and Predictability Experiment (THORPEX) and discussions on perspectives and potential benefits of Asian countries are provided. THORPEX is aimed at accelerating improvements in the accuracy of 1 to 14-day high-impact weather forecasts with research objectives of: 1) predictability and dynamical processes; 2) observing systems; 3) data assimilation and observing strategies; and 4) societal and economic applications. Direct benefits of Asian countries from THORPEX include improvement of: 1) forecast skills in global models, which exerts positive impact on mesoscale forecasts; 2) typhoon forecasts through dropwindsonde observations; and 3) forecast skills for high-impact weather systems via increased observations in neighboring countries. Various indirect benefits for scientific researches are also discussed. Extensive adaptive observation studies are recommended for all high-impact weather systems coming into the Korean peninsula, and enhancement of observations in the highly sensitive regions for the forecast error growth is required to improve forecast skills in the peninsula, possibly through international collaborations with neighboring countries.