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Impact assessment of Indian Ocean Dipole on the North Indian Ocean tropical cyclone prediction using a Statistical model

Abstract

This study examined the predictive skill of a statistical Generalised Additive Model (GAM) by considering the impact of the Indian Ocean Dipole (IOD). The proposed technique is powerful but simple and considers both the linear and non-linear relations hidden in the data. The model considers tropical cyclogenesis through kernel density estimation, trajectories by velocity field, and landfall through a country mask approach. A lead–lag analysis for TC forecast potential confirms that the IOD is a good predictor for 2-month lead forecast. Result shows that TC occurrence increase (decrease) during the negative (positive) IOD events and that the landfall probability vary for each IOD phase. Altering convection, steering flow, and low-level vorticity influence the NIO TC activity. Result also highlights the importance and potential of the GAM approach (approximately 72% skill) in matching predicted landfall with the observations.

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Data availability

Available from IMAS repository (https://www.imas/utas.edu.au/cyclone).

Code availability

Code will be available upon request.

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Acknowledgements

This work is supported by National Key R&D Program of China (Grant No. 2020YFA0608004) and National Natural Science Foundation of China (Grant Nos. 42088101 and 42030605). M. Wahiduzzaman was supported by Special Grant from China Postdoctoral Science Foundation (No. 2020T130311) and NUIST start up fund. Authors are grateful to Md Abdus Sattar for the code of GPI budget analysis. Authors are also grateful to three anonymous reviewers for their input that improved the manuscript.

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Contributions

MW initiated the project and conducted the data management and analysis. MW drafted the manuscript and all authors contributed to editing the manuscript.

Corresponding authors

Correspondence to Md Wahiduzzaman or Jing-Jia Luo.

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The authors declare no competing interests.

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Appendix

Appendix

See Figs.

Fig. 16
figure16

GPI RH anomaly during the a pre-monsoon (AMJ) IOD positive, b pre-monsoon IOD negative, c post-monsoon (OND) IOD positive, d post monsoon IOD negative years

16,

Fig. 17
figure17

Same as Appendix Fig. 16 but for vorticity

17,

Fig. 18
figure18

Same as Appendix Fig. 16 but for VWS

18 and

Fig. 19
figure19

Same as Appendix Fig. 16 but for MPI

19.

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Wahiduzzaman, M., Cheung, K., Luo, JJ. et al. Impact assessment of Indian Ocean Dipole on the North Indian Ocean tropical cyclone prediction using a Statistical model. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-05960-0

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Keywords

  • Tropical cyclones
  • Indian Ocean Dipole
  • North Indian Ocean
  • Prediction
  • Statistical model