• 제목/요약/키워드: weather research and forecasting

검색결과 319건 처리시간 0.026초

Advanced Forecasting Approach to Improve Uncertainty of Solar Irradiance Associated with Aerosol Direct Effects

  • Kim, Dong Hyeok;Yoo, Jung Woo;Lee, Hwa Woon;Park, Soon Young;Kim, Hyun Goo
    • Journal of Environmental Science International
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    • 제26권10호
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    • pp.1167-1180
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    • 2017
  • Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

KASI's contributions to Space Weather over the past 10 years

  • Cho, Kyungsuk;Park, Young-Deuk
    • The Bulletin of The Korean Astronomical Society
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    • 제40권1호
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    • pp.64.4-65
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    • 2015
  • For the past decade, supported by the Korean government, the solar and space weather group of Korea Astronomy and Space Science Institute (KASI) has been researching towards the prevention of hazardous effects on Korean satellites, the stability of wireless telecommunications, and the safety of polar route aviation. So far, we have expanded the ground observation system, made space data more accessible, developed more advanced models for space weather forecasting, from which we have been providing forecasting services to a satisfied domestic clientele. Alongside that, we have continued our research on solar activities and the Sun-Earth connection. In this talk, I will summarize our contributions to space weather over the past 10 years and discuss future plans for next decade.

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Developing and Evaluating Damage Information Classifier of High Impact Weather by Using News Big Data (재해기상 언론기사 빅데이터를 활용한 피해정보 자동 분류기 개발)

  • Su-Ji, Cho;Ki-Kwang Lee
    • Journal of Korean Society of Industrial and Systems Engineering
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    • 제46권3호
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    • pp.7-14
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    • 2023
  • Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

Comparative Study on the Accuracy of Surface Air Temperature Prediction based on selection of land use and initial meteorological data (토지이용도와 초기 기상 입력 자료의 선택에 따른 지상 기온 예측 정확도 비교 연구)

  • Hae-Dong Kim;Ha-Young Kim
    • Journal of Environmental Science International
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    • 제33권6호
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    • pp.435-442
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    • 2024
  • We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

The generation of cloud drift winds and inter comparison with radiosonde data

  • Lee, Yong-Seob;Chung, Hyo-Sang;Ahn, Myeung-Hwan;Park, Eun-Jung
    • Proceedings of the KSRS Conference
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    • 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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    • pp.135-139
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    • 1999
  • Wind velocity is one of the primary variables for describing atmospheric state from GMS-5. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP(Numerical Weather Prediction) models. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images. Multi spectral imagery from GMS-5 was used for this purpose and applied to Korean region with together BoM(Bureau of Meteorology). The derivation of wind velocity estimates from low and high resolution visible, split window infrared, and water vapor images, resulted in improvements in the amount and quality of wind data available for forecasting.

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A study on the Conceptual Design for the Real-time wind Power Prediction System in Jeju (제주 실시간 풍력발전 출력 예측시스템 개발을 위한 개념설계 연구)

  • Lee, Young-Mi;Yoo, Myoung-Suk;Choi, Hong-Seok;Kim, Yong-Jun;Seo, Young-Jun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • 제59권12호
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    • pp.2202-2211
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    • 2010
  • The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

Development of Surface Weather Forecast Model by using LSTM Machine Learning Method (기계학습의 LSTM을 적용한 지상 기상변수 예측모델 개발)

  • Hong, Sungjae;Kim, Jae Hwan;Choi, Dae Sung;Baek, Kanghyun
    • Atmosphere
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    • 제31권1호
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    • pp.73-83
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    • 2021
  • Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

PV Power Prediction Models for City Energy Management System based on Weather Forecast Information (기상정보를 활용한 도시규모-EMS용 태양광 발전량 예측모델)

  • Eum, Ji-Young;Choi, Hyeong-Jin;Cho, Soo-Hwan
    • The Transactions of The Korean Institute of Electrical Engineers
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    • 제64권3호
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    • pp.393-398
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    • 2015
  • City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.

Predictability Experiments of Fog and Visibility in Local Airports over Korea using the WRF Model

  • Bang, Cheol-Han;Lee, Ji-Woo;Hong, Song-You
    • Journal of Korean Society for Atmospheric Environment
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    • 제24권E2호
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    • pp.92-101
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    • 2008
  • The objective of this study is to evaluate and improve the capability of the Weather Research and Forecasting (WRF) model in simulating fog and visibility in local airports over Korea. The WRF model system is statistically evaluated for the 48-fog cases over Korea from 2003 to 2006. Based on the 4-yr evaluations, attempts are made to improve the simulation skill of fog and visibility over Korea by revising the statistical coefficients in the visibility algorithms of the WRF model. A comparison of four existing visibility algorithms in the WRF model shows that uncertainties in the visibility algorithms include additional degree of freedom in accuracy of numerical fog forecasts over Korea. A revised statistical algorithm using a linear-regression between the observed visibility and simulated hydrometeors and humidity near the surface exhibits overall improvement in the visibility forecasts.