• Title/Summary/Keyword: numerical weather forecast

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Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea

  • Kim, Hyo-suk;Do, Ki Seok;Park, Joo Hyeon;Kang, Wee Soo;Lee, Yong Hwan;Park, Eun Woo
    • The Plant Pathology Journal
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    • v.36 no.1
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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 (Ci) and the 20-day and 7-day moving averages of Ci for the inoculum build-up phase (Cinc) prior to the panicle emergence of rice plants and the infection phase (Cinf) during the heading stage of rice plants, respectively. Based on Cinc and Cinf, 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.

Application of Vertical Grid-nesting to the Tropical Cyclone Track and Intensity Forecast

  • Kim, Hyeon-Ju;Cheong, Hyeong-Bin;Lee, Chung-Hui
    • Journal of the Korean earth science society
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    • v.40 no.4
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    • pp.382-391
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    • 2019
  • The impact of vertical grid-nesting on the tropical cyclone intensity and track forecast was investigated using the Weather Research and Forecast (WRF) version 3.8 and the initialization method of the Structure Adjustable Balanced Bogus Vortex (SABV). For a better resolution in the central part of the numerical domain, where the tropical cyclone of interest is located, a horizontal and vertical nesting technique was employed. Simulations of the tropical cyclone Sanba (16th in 2012) indicated that the vertical nesting had a weak impact on the cyclone intensity and little impact on the track forecast. Further experiments revealed that the performance of forecast was quite sensitive to the horizontal resolution, which is in agreement with previous studies. The improvement is due to the fact that horizontal resolution can improve forecasts not only on the tropical cyclone-scale but also for large-scale disturbances.

Numerical Weather Prediction and Forecast Application (수치모델링과 예보)

  • Woo-Jin Lee;Rae-Seol Park;In-Hyuk Kwon;Junghan Kim
    • Atmosphere
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    • v.33 no.2
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    • pp.73-104
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    • 2023
  • Over the past 60 years, Korean numerical weather prediction (NWP) has advanced rapidly with the collaborative effort between the science community and the operational modelling center. With an improved scientific understanding and the growth of information technology infrastructure, Korea is able to provide reliable and seamless weather forecast service, which can predict beyond a 10 days period. The application of NWP has expanded to support decision making in weather-sensitive sectors of society, exploiting both storm-scale high-impact weather forecasts in a very short range, and sub-seasonal climate predictions in an extended range. This article gives an approximate chronological account of the NWP over three periods separated by breakpoints in 1990 and 2005, in terms of dynamical core, physics, data assimilation, operational system, and forecast application. Challenges for future development of NWP are briefly discussed.

Long-term Streamflow Prediction Using ESP and RDAPS Model (ESP와 RDAPS 수치예보를 이용한 장기유량예측)

  • Lee, Sang-Jin;Jeong, Chang-Sam;Kim, Joo-Cheol;Hwang, Man-Ha
    • Journal of Korea Water Resources Association
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    • v.44 no.12
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    • pp.967-974
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    • 2011
  • Based on daily time series from RDAPS numerical weather forecast, Streamflow prediction was simulated and the result of ESP analysis was implemented considering quantitative mid- and long-term forecast to compare the results and review applicability. The result of ESP, ESP considering quantitative weather forecast, and flow forecast from RDAPS numerical weather forecast were compared and analyzed with average observed streamflow in Guem River Basin. Through this process, the improvement effect per method was estimated. The result of ESP considering weather information was satisfactory relatively based on long-term flow forecast simulation result. Discrepancy ratio analysis for estimating accuracy of probability forecast had similar result. It is expected to simulate more accurate flow forecast for RDAPS numerical weather forecast with improved daily scenario including time resolution, which is able to accumulate 3 hours rainfall or continuous simulation estimation.

Cloud Forecast using Numerical Weather Prediction (수치 예보를 이용한 구름 예보)

  • Kim, Young-Chul
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.15 no.3
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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.

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Improving Wind Speed Forecasts Using Deep Neural Network

  • Hong, Seokmin;Ku, SungKwan
    • International Journal of Advanced Culture Technology
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    • v.7 no.4
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    • pp.327-333
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    • 2019
  • Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

Evaluation of UM-LDAPS Prediction Model for Solar Irradiance by using Ground Observation at Fine Temporal Resolution (고해상도 일사량 관측 자료를 이용한 UM-LDAPS 예보 모형 성능평가)

  • Kim, Chang Ki;Kim, Hyun-Goo;Kang, Yong-Heack;Kim, Jin-Young
    • Journal of the Korean Solar Energy Society
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    • v.40 no.5
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    • pp.13-22
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    • 2020
  • Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

Analysis of Forecast Performance by Altered Conventional Observation Set (종관 관측 자료 변화에 따른 예보 성능 분석)

  • Han, Hyun-Jun;Kwon, In-Hyuk;Kang, Jeon-Ho;Chun, Hyoung-Wook;Lee, Sihye;Lim, Sujeong;Kim, Taehun
    • Atmosphere
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    • v.29 no.1
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    • pp.21-39
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    • 2019
  • The conventional observations of the Korea Meteorological Administration (KMA) and National Centers for Environmental Prediction (NCEP) are compared in the numerical weather forecast system at the Korea Institute of Atmospheric Prediction Systems (KIAPS). The weather forecasting system used in this study is consists of Korea Integrated Model (KIM) as a global numerical weather prediction model, three-dimensional variational method as a data assimilation system, and KIAPS Package for Observation Processing (KPOP) as an observation pre-processing system. As a result, the forecast performance of NCEP observation was better while the number of observation is similar to the KMA observation. In addition, the sensitivity of forecast performance was investigated for each SONDE, SURFACE and AIRCRAFT observations. The differences in AIRCRAFT observation were not sensitive to forecast, but the use of NCEP SONDE and SURFACE observations have shown better forecast performance. It is found that the NCEP observations have more wind observations of the SONDE in the upper atmosphere and more surface pressure observations of the SURFACE in the ocean. The results suggest that evenly distributed observations can lead to improved forecast performance.

Evaluation of UM-LDAPS Prediction Model for Daily Ahead Forecast of Solar Power Generation (태양광 발전 예보를 위한 UM-LDAPS 예보 모형 성능평가)

  • Kim, Chang Ki;Kim, Hyun-Goo;Kang, Yong-Heack;Yun, Chang-Yeol
    • Journal of the Korean Solar Energy Society
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    • v.39 no.2
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    • pp.71-80
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    • 2019
  • Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

A Study on Improvement of the Use and Quality Control for New GNSS RO Satellite Data in Korean Integrated Model (한국형모델의 신규 GNSS RO 자료 활용과 품질검사 개선에 관한 연구)

  • Kim, Eun-Hee;Jo, Youngsoon;Lee, Eunhee;Lee, Yong Hee
    • Atmosphere
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    • v.31 no.3
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    • pp.251-265
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    • 2021
  • This study examined the impact of assimilating the bending angle (BA) obtained via the global navigation satellite system radio occultation (GNSS RO) of the three new satellites (KOMPSAT-5, FY-3C, and FY-3D) on analyses and forecasts of a numerical weather prediction model. Numerical data assimilation experiments were performed using a three-dimensional variational data assimilation system in the Korean Integrated Model (KIM) at a 25-km horizontal resolution for August 2019. Three experiments were designed to select the height and quality control thresholds using the data. A comparison of the data with an analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system showed a clear positive impact of BA assimilation in the Southern Hemisphere tropospheric temperature and stratospheric wind compared with that without the assimilation of the three new satellites. The impact of new data in the upper atmosphere was compared with observations using the infrared atmospheric sounding interferometer (IASI). Overall, high volume GNSS RO data helps reduce the RMSE quantitatively in analytical and predictive fields. The analysis and forecasting performance of the upper temperature and wind were improved in the Southern and Northern Hemispheres.