북서태평양 태풍 강도 예측 컨센서스 기법
A Consensus Technique for Tropical Cyclone Intensity Prediction over the Western North Pacific
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (15)
- 2012 National hurricane center forecast verification report, 79, "Cangialosi, J. P.", National Hurricane Center (NHC), ~ (2013)
- Davis, C. E., "Modern Statistical Methods in Chronic Disease Epidemiology", pp.140~147 (1986), Wiley
- Mark DeMaria, "Further Improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS)", Weather and Forecasting, 20(4), pp.531~543 (2005), American Meteorological Society[DOI]
- Goerss, J. S., "Tropical cyclone track forecast using an ensemble of dynamical models", Mon. Wea. Rev., 128, pp.1187~1193 (2000)[DOI]
- Russell L. Elsberry, "Advances in Research and Forecasting of Tropical Cyclones from 1963-2013", Asia-Pacific Journal of Atmospheric Sciences, 50(1), pp.3~16 (2014), 한국기상학회
- Sanghee Jun, "Applicability of the Superensemble to the Tropical Cyclone Track Forecasts in the Western North Pacific", Asia-Pacific Journal of Atmospheric Sciences, 51(1), pp.39~48 (2015), 한국기상학회
- Korea Meteorological Administration (KMA), "2016 Yearbook", pp.342~343 (2017), KMA
- John A. Knaff, "Statistical, 5-Day Tropical Cyclone Intensity Forecasts Derived from Climatology and Persistence", Weather and Forecasting, 18(1), pp.80~92 (2003), American Meteorological Society[DOI]
- Kotal, S. D., "A multimodel ensemble (MME) technique for cyclone track prediction over the North Indian Sea", GEOFIZIKA, 28, pp.275~291 (2011)
- T. S. V. Vijaya Kumar, "Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific", Monthly Weather Review, 131(3), pp.574~583 (2003), American Meteorological Society[DOI]
- Hyeong-Seog Kim, "Track-Pattern-Based Model for Seasonal Prediction of Tropical Cyclone Activity in the Western North Pacific", Journal of Climate, 25(13), pp.4660~4678 (2012), American Meteorological Society[DOI]
- Robert Rogers, "The Intensity Forecasting Experiment: A NOAA Multiyear Field Program for Improving Tropical Cyclone Intensity Forecasts", Bulletin of the American Meteorological Society, 87(11), pp.1523~1538 (2006), American Meteorological Society[DOI]
- Ueno, M., "Tropical Cyclogenesis Monitoring at RSMC Tokyo", JMA, 2014, ~
- Gabriele Villarini, "Is the recorded increase in short-duration North Atlantic tropical storms spurious?", Journal of Geophysical Research, 116(D10) (2011), American Geophysical Union (AGU)[DOI]
- WMO (World Meteorological Organization), , Sixth WMO International Workshop on Tropical Cyclone (IWTC-VI), pp.92~ (2007), WMO