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Korean Meteorological Society (한국기상학회)
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A Consensus Technique for Tropical Cyclone Intensity Prediction over the Western North Pacific

북서태평양 태풍 강도 예측 컨센서스 기법

  • Oh, Youjung (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Moon, Il-Ju (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Lee, Woojeong (National Typhoon Center, Korea Meteorological Administration)
  • 오유정 (제주대학교 태풍연구센터/해양기상학협동과정) ;
  • 문일주 (제주대학교 태풍연구센터/해양기상학협동과정) ;
  • 이우정 (기상청 국가태풍센터)
  • Received : 2018.06.02
  • Accepted : 2018.07.31
  • Published : 2018.09.30
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Abstract

In this study, a new consensus technique for predicting tropical cyclone (TC) intensity in the western North Pacific was developed. The most important feature of the present consensus model is to select and combine the guidance numerical models with the best performance in the previous years based on various evaluation criteria and averaging methods. Specifically, the performance of the guidance models was evaluated using both the mean absolute error and the correlation coefficient for each forecast lead time, and the number of the numerical models used for the consensus model was not fixed. In averaging multiple models, both simple and weighted methods are used. These approaches are important because that the performance of the available guidance models differs according to forecast lead time and is changing every year. In particular, this study develops both a multi-consensus model (M-CON), which constructs the best consensus models with the lowest error for each forecast lead time, and a single best consensus model (S-CON) having the lowest 72-hour cumulative mean error, through on training process. The evaluation results of the selected consensus models for the training and forecast periods reveal that the M-CON and S-CON outperform the individual best-performance guidance models. In particular, the M-CON showed the best overall performance, having advantages in the early stages of prediction. This study finally suggests that forecaster needs to use the latest evaluation results of the guidance models every year rather than rely on the well-known accuracy of models for a long time to reduce prediction error.

Keywords

References

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