• 한국위성정보통신학회논문지
• /
• 제8권1호
• /
• pp.23-30
• /
• 2013

본 연구는 "극항로 우주방사선 예보 모델 개발을 위한 사전연구"로서 2013년부터 본격적으로 개발하게 될 기상청의 극항로 우주방사선 예보 모델의 개발 방안 마련을 위한 사전 조사에 초점을 맞추고 있다. 자료 조사는 주로 항공기 운항과 우주기상 관련 문헌 및 법령 조사, 국내 항공사들의 우주기상 관련 운영지침 및 실태 조사를 통해서 이루어졌다. 또한 주요 선진국들이 현재 사용하고 있는 우주 방사선 계산 프로그램들의 장단점을 파악하고 개선할 수 있는 가능성을 찾는데 주력하였다. 조사 결과 국내에서는 아직 극항로 우주방사선을 예보하는 독자적인 모델이 전무한 상황으로 극항로 우주방사선 예보 모델의 국내 개발의 필요성이 절실함을 파악하였다. 현재 주요 선진국에서 사용하고 있는 대부분의 우주방사선 계산 프로그램들이 태양활동 및 우주기상의 변화를 제대로 반영하지 못하고 있다는 사실도 파악하였다. 본 연구에서는 현재 일반적으로 널리 사용되고 있는 우주방사선 계산 프로그램들의 장단점을 비교 분석하였다. 최종적으로 현재의 우주방사선 계산 모델들이 반영하지 못한 실시간 우주기상 효과를 반영하고, 보다 정밀한 우주방사선 예보 모델을 개발하고자 하는 목적으로 다음의 4가지 방안을 최종 제시하였다. (1) 우주방사선 예보 모델의 기반이 될 지상 방사선량 계산 프로그램의 후보 선정, (2) 항공기 고도에서 적용 가능한 정밀한 대기 모델 개선 및 결정, (3) 지상 방사선량 계산 프로그램과 항공기 고도에서의 대기 모델과 결합, (4) 최종적으로 결합된 우주방사사선 모델에 우주기상 예보 정보 반영.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -1
• /
• pp.262-264
• /
• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
• /
• pp.35-38
• /
• 2005

This paper suggests the method that forecast Dynamic Line Rating (DLR). Thermal Overload Risk (TOR) of next time is forecasted based on current weather condition and DLR value by Monte Carlo Simulation (MCS). To model weather element of transmission line for MCS, we will propose the use of weather forecast system and statistical models that time series law is applied. Also, through case study, forecasted TOR probability confirmed can utilize by standard that decide DLR of next time. In short, proposed method may be used usefully to keep safety of transmission line and reliability of supply of electric Power by forecasting transmission capacity of next time.

• 천문학회보
• /
• 제34권1호
• /
• pp.81.1-81.1
• /
• 2009

• 한국우주과학회:학술대회논문집(한국우주과학회보)
• /
• 한국우주과학회 2009년도 한국우주과학회보 제18권1호
• /
• pp.45.1-45.1
• /
• 2009

• 한국수자원학회논문집
• /
• 제45권7호
• /
• pp.631-641
• /
• 2012

본 연구에서는 지상의 관측 자료와 광역의 정보를 제공하는 수치 예보 모형 자료 및 인공위성 자료를 이용하고 자료와 강수예측치의 물리적 상관 특성을 나타내기 위하여 자료 사이의 비선형 거동을 잘 나타내는 신경망 모형에 적용시켜 단시간 강수 예측을 수행하였다. 이를 위하여 서울지점에 대하여 현재로부터 3시간, 6시간, 9시간, 12시간의 선행시간을 가지는 인공위성자료(MTSAT-1R) 및 수치 예보 모형 자료(RDAPS, Regional Data Assimilation and Prediction System)와 실시간 전송되는 자동 기상 관측 시스템(AWS, Automatic Weather System)의 관측치를 신경망 모형의 입력 자료로 하여 3시간, 6시간, 9시간, 12시간의 선행시간을 가지는 자료로 강수를 예측 할 수 있는 강수 예측 모형을 개발하였다. 장마와 태풍과 같이 전선형강수와 선풍형강수 등 강수 양상의 차이를 고려하기 위하여 6월, 7월과 8월, 9월 자료를 구분하여 신경망을 구축하였으며, 자료가용성에 기초하여 2006년에서 2008년 기간 동안에 대하여 모형을 학습하고 2009년에 대하여 모형의 적용성을 검증한 결과, 단시간 강수예측에 대한 모형의 적용 가능성을 보여주었으나 다양한 광역 자료와 인공신경망을 사용함에도 불구하고 단시간 강수예측의 정량적 정도향상을 위한 여지가 많음을 보여준다.

• Journal of Electrical Engineering and Technology
• /
• 제12권6호
• /
• pp.2177-2186
• /
• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• 전기학회논문지
• /
• 제64권3호
• /
• pp.393-398
• /
• 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.

• 대기
• /
• 제26권1호
• /
• pp.1-18
• /
• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• 대기
• /
• 제31권4호
• /
• pp.433-443
• /
• 2021

The wind speed and wind direction in Yeongdong are one of the crucial meteorological factors for forecasting snowfall in this area. To improve the snowfall forecast in Yeongdong region, Yeongdong Extreme Snowfall-Windstorm Experiment, YES-WEX was designed. We examined the wind field variation simulated with Local Data Assimilation and Prediction System (LDAPS) using observed wind field during YES-WEX period. The simulated wind speed was overestimated over the East Sea and especially 2 to 4 times in the coastal line. The vertical wind in Yeongdong region, which is a crucial factor in the snowfall forecast, was not well simulated at the low level (850 hPa~1000 hPa) until 12 hours before the forecast. The snowfall distribution was also not accurately simulated. Three hours after the snowfall on the East Sea coast was observed, the snowfall was simulated. To improve the forecast accuracy of snowfall in Yeongdong region, it is important to understand the weather conditions using the observed and simulated data. In the future, data in the northern part of the East Sea and the mountain slope of Taebaek observed from the meteorological aircraft, ship, and drone would help in understanding the snowfall phenomenon and improving forecasts.