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An evaluation and improvement of tropical cyclone prediction in the western North Pacific basin from global ensemble forecasts

  • Lili Lei
  • Yangjinxi Ge
  • Zhemin TanEmail author
  • Xuwei Bao
Research Paper Special Topic: Weather characteristics and climate anomalies of the TC track, heavy rainfall and tornadoes in 2018
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Abstract

Forecasts of tropical cyclones (TCs) of the western North Pacific basin during the period of July to August 2018, especially of Rumbia (2018), Ampil (2018) and Jongdari (2018) that made landfall over Shanghai, have opposed great challenges for numerical models and forecasters. The predictive skill of these TCs are analyzed based on ensemble forecasts of ECMWF and NCEP. Results of the overall performance show that ensemble forecasts of ECMWF generally have higher predictive skill of track and intensity forecasts than those of NCEP. Specifically, ensemble forecasts of ECMWF have higher predictive skill of intensity forecasts for Rumbia (2018) and Ampil (2018) than those of NCEP, and both have low predictive skill of intensity forecasts for Jongdari (2018) at peak intensity. To improve the predictive skill of ensemble forecasts for TCs, a method that estimates adaptive weights for members of an ensemble forecast is proposed. The adaptive weights are estimated based on the fit of ensemble priors and posteriors to observations. The performances of ensemble forecasts of ECMWF and NCEP using the adaptive weights are generally improved for track and intensity forecasts. The advantages of the adaptive weights are more prominent for ensemble forecasts of ECMWF than for those of NCEP.

Keywords

Tropical cyclone Ensemble forecast Adaptive weight 

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Notes

Acknowledgements

This work was supported by the National Key R & D Program of China (Grant No. 2017YFC1501603), the National Natural Science Foundation of China (Grant Nos. 41675052, 41775057 & 41775064).

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Mesoscale Severe Weather/Ministry of Education, School of Atmospheric SciencesNanjing UniversityNanjingChina
  2. 2.Shanghai Typhoon Institute, China Meteorological Administration School of Atmospheric SciencesNanjing UniversityNanjingChina

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