• 대기
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• 제25권4호
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• pp.623-637
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• 2015

The accurate analysis of water vapor in initial of numerical weather prediction (NWP) model is required as one of the necessary conditions for the improvement of heavy rainfall prediction and reduction of spin-up time on a very-short-range forecast. To study this effect, the impact of a ground-based Global Positioning System (GPS)-Precipitable Water Vapor (PWV) on very-short-range forecast are examined. Data assimilation experiments of GPS-PWV data from 19 sites over the Korean Peninsula were conducted with Advanced Storm-scale Analysis and Prediction System (ASAPS) based on the Korea Meteorological Administration's Korea Local Analysis and Prediction System (KLAPS) included "Hot Start" as very-short-range forecast system. The GPS total water vapor was used as constraint for integrated water vapor in a variational humidity analysis in KLAPS. Two simulations of heavy rainfall events show that the precipitation forecast have improved in terms of ETS score compared to the simulation without GPS-PWV data. In the first case, the ETS for 0.5 mm of rainfall accumulated during 3 hrs over the Seoul-Gyeonggi area shows an improvement of 0.059 for initial forecast time. In other cases, the ETS improved 0.082 for late forecast time. According to a qualitative analysis, the assimilation of GPS-PWV improved on the intensity of precipitation in the strong rain band, and reduced overestimated small amounts of precipitation on the out of rain band. In the case of heavy rainfall during the rainy season in Gyeonggi province, 8 mm accompanied by the typhoon in the case was shown to increase to 15 mm of precipitation in the southern metropolitan area. The GPS-PWV assimilation was extremely beneficial to improving the initial moisture analysis and heavy rainfall forecast within 3 hrs. The GPS-PWV data on variational data assimilation have provided more useful information to improve the predictability of precipitation for very short range forecasts.

• 대기
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• 제21권1호
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• pp.1-16
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• 2011

Numerical prediction capability has been improved over the decades, but progress of prediction for high-impact weather (HIW) was unsatisfactory. One reason of low predictability for HIW is lack of observation data. The National Institute of Meteorological Research (NIMR) has been performed observation program for improvement of predictability, and reduction in social and economical cost for HIW. As part of this observation program, summer intensive observation program (ProbeX-2009) was performed at the observation-gap areas from 25 August to 6 September 2009. Sounding observations using radiosonde were conducted in the Gisang2000 research vessel (R/V) from the Korea Meteorological Administration (KMA) over the West Sea and the Eardo R/V from the Korea Ocean Research and Development Institute (KORDI) over the South Sea. Observation System Experiment (OSE) is carried out to examine the effect of ProbeX-2009 data. OSEs using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) Model are conducted to investigate the predictability for a short time forecast. And, OSEs using WRF/3DVAR system and WRF forecast model are conducted to study the predictability for an extended time. Control experiment (K_CTL and CNTL) used only GTS observation and experiment (K_EXP and SWEXP) used ProbeX-2009 data from two system are performed. ETS for 3hr accumulated rainfall simulated by KLAPS-WRF shows that K_EXP is higher than K_CTL. Also, ETS for 12hr accumulated rainfall of SWEXP from 3DVAR-WRF is higher than CNTL. The results indicate that observation over the ocean has positive impact on HIW prediction.

• 대기
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• 제31권3호
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• pp.311-326
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• 2021

To improve the predictability of high-impact weather phenomena around Seoul, where a larger number of people are densely populated, KMA conducted the intensive observation from 22 June to 20 September in 2020 over the Seoul area. During the intensive observation period (IOP), the dropsonde from NIMS Atmospheric Research Aircraft (NARA) and the radiosonde from KMA research vessel Gisang1 were observed in the Yellow Sea, while, in the land, the radiosonde observation data were collected from Icheon and Incheon. Therefore, in this study, the effects of radiosonde and dropsonde data during the IOP were investigated by Observing System Experiment (OSE) based on Korean Integrated Model (KIM). We conducted two experiments: CTL assimilated the operational fifteen kinds of observations, and EXP assimilated not only operational observation data but also intensive observation data. Verifications over the Korean Peninsula area of two experiments were performed against analysis and observation data. The results showed that the predictability of short-range forecast (1~2 day) was improved for geopotential height at middle level and temperature at lower level. In three precipitation cases, EXP improved the distribution of precipitation against CTL. In typhoon cases, the predictability of EXP for typhoon track was better than CTL, although both experiments simulated weaker intensity as compared with the observed data.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(I)
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• 한국수자원학회논문집
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• 제51권7호
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• pp.575-584
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• 2018

최근 이상기후로 인한 집중호우 발생빈도와 이로 인한 국지적인 홍수 피해가 증가하고 있다. 이러한 점에서 홍수피해 예방측면에서 수치예보 정보 활용이 요구되고 있다. 그러나 수치예보모델은 초기 조건 및 지형적 요인으로 인해 시공간적 편의가 존재하며 실시간 예측정보로 활용하기 전에 모형결과에 대한 편의보정이 요구된다. 본 연구에서는 관측지점 기준으로 편의 보정계수를 산정하는 과정에서 모든 관측소간의 상관성을 거리의 함수로 고려하여 미계측지점의 편의 보정계수를 공간적으로 확장할 수 있는 Bayesian Kriging 기반 MFBC 기법을 개발하였다. 본 연구에서 개발한 방법은 미계측 유역에 대해서도 보정계수를 효과적으로 추정하는 것이 확인되었으며, 비교적 고해상도로 72시간(3일) 정도까지 예측강우 정보를 활용하는 것이 가능할 것으로 판단된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1142-1146
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• 2009

A rainfall simulation and forecasting technique that can generate daily rainfall sequences conditional on multi-model ensemble GCMs is developed and applied to data in Korea for the major rainy season. The GCM forecasts are provided by APEC climate center. A Weather State Based Downscaling Model (WSDM) is used to map teleconnections from ocean-atmosphere data or key state variables from numerical integrations of Ocean-Atmosphere General Circulation Models to simulate daily sequences at multiple rain gauges. The method presented is general and is applied to the wet season which is JJA(June-July-August) data in Korea. The sequences of weather states identified by the EM algorithm are shown to correspond to dominant synoptic-scale features of rainfall generating mechanisms. Application of the methodology to seasonal rainfall forecasts using empirical teleconnections and GCM derived climate forecast are discussed.

• 대기
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• 제14권3호
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• pp.19-28
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• 2004

In this study, two years Automatic Weather Station (AWS) data observed near the coast and islands are used to evaluate gust factors only when time averaged wind speed is higher than 5 ms. The gust factors are quite different in spatial and temporal domain according to analysis method. As the averaged time is increased, the gust factors are also increased. But the gust factors are decreased when wind speed is increased. It is because each wind speed is averaged one and a maximum wind is the greatest one for each time interval. The result from t-test is shown that all data are included within the 99% significance level. A sample standard deviation of ten minutes and one minute are 0.137~0.197, 0.067~0.142, respectively. Recently, the gust factor provided at the Korea Meteorological Administration (KMA) Homepage is calculated with one-hour averaged method. All though this method is hard to use directly for forecasting the strong wind over sea and coast, the result will be a great help to express Ocean Storm Flash in the Regional Meteorological Offices and the Meteorological Stations.

• 대기
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• 제14권3호
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• pp.3-18
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• 2004

A heavy snowfall event occurred over the middle part of the Korean peninsula on March 4, 2004. The numerical models of KMA failed to forecast this heavy snowfall event because this event was due to small scale disturbance by low lever convergence and atmospheric instability. The analyses for this heavy snowfall have been performed to give forecasters useful suggestions for forecasting heavy snowfall events in the future. The analyses for the snowfall event were recounted by the Hourly Korean Peninsula Analysis Weather Chart (HKPAWC) presenting on the KMA intranet system. We confirmed that warm air flows of low level into south central Korea in conjunction with strong southwesterly winds played important role in the heavy snowfall event. We suggested several check points to improve the forecast of heavy snowfall events in the future through the results of the analyses.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.724-736
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• 2019

Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines. However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes a new dynamic thermal rating technology based on global/regional assimilation and prediction system (GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves a lot of investment.