A Study of the Method for Estimating the Missing Data from Weather Measurement Instruments

인공신경망을 이용한 기상관측장비 결측 보완 기술에 관한 연구

  • Min, Jae-Sik (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Lee, Moo-Hun (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Jee, Joon-Bum (Weather Information Service Engine Institute, Hankuk University of Foreign Studies) ;
  • Jang, Min (Weather Information Service Engine Institute, Hankuk University of Foreign Studies)
  • 민재식 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 이무훈 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 지준범 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 장민 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Received : 2016.06.28
  • Accepted : 2016.08.20
  • Published : 2016.08.28


The purpose of this study is to make up for missing of weather informations from ASOS and AWS using artificial neural networks. We collected temperature, relative humidity and wind velocity for August during 5-yr (2011-2015) and sample designed artificial neural networks, assuming the Seoul weather station was missing. The result of sensitivity study on number of epoch shows that early stopping appeared at 2,000 epochs. Correlation between observation and prediction was higher than 0.6, especially temperature and humidity was higher than 0.9, 0.8 respectively. RMSE decreased gradually and training time increased exponentially with respect to increase of number of epochs. The predictability at 40 epoch was more than 80% effect on of improved results by the time the early stopping. It is expected to make it possible to use more detailed weather information via the rapid missing complemented by quick learning time within 2 seconds.


Supported by : 기상청


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