• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.415-417
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• 2008

기상자료는 매순간 방대한 양으로 쏟아져 나온다. 본 논문은 이 방대한 양의 자료를 토대로 신경망을 학습시켜 정보(예보)를 도출시키는 데 얼마나 적합한지 확인하고자 함에 있다. 과거 의사결정나무를 통해서 위와 같은 연구가 진행된 바 있으나, 현재 우리나라에서 신경망을 통한 분석은 전무한 상태이다. 따라서 우리나라 3개지역을 선정 96년도부터 05년까지의 10년간의 9, 10월 지상자료를 토대로 안개예보에 신경망이 적합한지에 대해 연구하였다.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.190-194
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• 2018

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.

• 설비공학논문집
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• 제25권6호
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• 한국지구과학회:학술대회논문집
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• 한국지구과학회 2005년도 춘계학술발표회 논문집
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• pp.182-187
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• 2005

호남지방의 집중호우 예측 가능성을 향상시키기 위하여 레이더 자료동화를 이용한 예측가능성 제고, 광주지방의 고층자료를 분석하여 집중호우 발생시의 종관장을 해석하였다. 자료동화 자료로는 진도 S-band 레이더 원시자료를 이용한 고도별 수평 바람장을 산출하여 사용하였다. 또한, PC-cluster를 platform으로 사용하는 호남지방의 고해상도 기상예측시스템을 이용하여, 레이더 수평 바람장 자료의 동화가 집중호우 및 중규모 순환장 예측정확도에 미치는 영향을 살펴보았다.

• 대기
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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.

• 한국항공운항학회지
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• 제28권1호
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• pp.45-58
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• 2020

According to the Ministry of Land, Infrastructure and Transport press release ('18.12.21.) The amendment of the Aviation Business Act will reduce the capital requirements for aviation leisure operators and make it easier to enter aviation leisure businesses by improving regulations on small air transportation business. In addition, as the scale of the UAV(Unmanned Aerial Vehicle) sector is expected to increase globally, the dramatic increase in low altitude operating aircraft, including this, must be taken into account. The low altitude aircraft category is divided into small airplanes, helicopters, light aircrafts and ultra-light aircrafts, and instructors include school instructor pilots and student pilots, military and national helicopter pilots, and aviation leisure operators. In case of low altitude aircraft, there are cases of canceling operations due to low visibility and low clouds, and aircraft accidents due to excessive operation and sudden weather phenomenon. Therefore, in order to prevent low-altitude aircraft accidents, a safe flight plan based on weather conditions and weather forecasts and more accurate and local weather forecasts and weather forecast data are needed to prepare for the rapidly changing weather conditions.

• 대한토목학회논문집
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• 제29권6B호
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• pp.543-550
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• 2009

선형외삽법에 기초한 전형적인 단시간 강우예측모형은 호우를 발생하는 강우시스템의 발달과정을 모의하지 못하는 한계를 내포하고 있다. 본 연구에서는 이러한 한계를 극복하기 위하여 기상레이더로 획득할 수 있는 여러 시간대의 반사도 정보로부터 획득한 정보변화 과정과 다항 회귀식을 이용하여 x방향과 y방향의 전파속도의 성장과정과 레이더 반사도의 성장과정 모의에 기초한 단시간 강우예측 모형을 개발하였다. 검정통계량이 제시한 결과는 2-CAPPI를 이용한 기존의 단시간 강우예측모형보다 개선된 결과를 보여주었다. 그럼에도 불구하고 본 모형이 완전한 물리적 모형이 아니라 자료사이의 상관성과 다항 회귀식을 이용한 통계적인 방법에 기초하였으므로 강우의 성장과 소멸과정을 구현과 예측성 개선에도 한계가 있음을 보였다.

• 대기
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• 제29권4호
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• pp.463-480
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• 2019

This study classified heavy rain types from K-means clustering for the hourly relationship between rainfall intensity and cloud top height over the Korean peninsula, and then examined their statistical characteristics for the period of June~August 2013~2018. Total rainfall amount of warm-type events was 2.65 times larger than that of the cold-type, whereas the lightning frequency divided by total rainfall for the warm-type was only 46% of the cold-type. Typical cold-type cases exhibited high cloud top height around 16 km, large reflectivity in the upper layer, and frequent lightning flashes under convectively unstable condition. Phenomenally, the cold-type cases corresponded to cloud cluster or multi-cell thunderstorms. However, two warm-type cases related to Changma and typhoon were characterized by heavy rainfall due to long duration, relatively low cloud top height and upper-level reflectivity, and the absence of lightning under the convectively neutral and extremely humid conditions. This study further confirmed that the forecast skill of rainfall could be improved by applying correction factor with the overestimation for cold-type and underestimation for warm-type cases in the Local Data Assimilation and Prediction System (LDAPS) operational model (e.g., BIAS score was improved by 5%).

• 한국태양에너지학회 논문집
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• 제40권5호
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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.

• 대기
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• 제31권4호
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• pp.433-443
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• 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.