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Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS: evaluation for 2015–2018 seasons

  • Hong LiEmail author
  • Jingyao Luo
  • Mengting Xu
Research Article

Abstract

The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running realtime since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015–2018, and compared against the NCEP GEFS.

TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.

Keywords

ensemble data assimilation ensemble forecasting tropical cyclones 

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Notes

Acknowledgements

The authors would like to thank Dr. Lina Bai in STI for providing the best-track data. This research was primarily supported by National Key R&D Program of China (Grant No. 2018YFC1506404), the National Basic Research Program of China (Grant No. 2015CB452806), National Natural Science Foundation of China (Grant No. 41575107), and in part by Shanghai Sailing Program (Grant No. 19YF1458700), Scientific Research Program of Shanghai Science & Technology Commission (Grant No. 19dz1200101), National Programme on Global Change and Air-Sea Interaction (Grant No. GASI-IPOVAI-04), and Shanghai Typhoon Innovation Team grants to Shanghai Typhoon Institute.

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Typhoon InstituteChina Meteorological AdministrationShanghaiChina
  2. 2.Key Laboratory of Numerical Modeling for Tropical CyclonesChina Meteorological AdministrationShanghaiChina

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