Journal Articles Proceedings of the National Academy of Sciences of the United States of America Year : 2024

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

Eviatar Bach
  • Function : Author
V. Krishnamurthy
  • Function : Author
Safa Mote
  • Function : Author
Jagadish Shukla
  • Function : Author
A. Surjalal Sharma
  • Function : Author
Eugenia Kalnay
  • Function : Author

Abstract

The South Asian monsoon affects more than a billion people in the Indian subcontinent. The monsoon intraseasonal oscillation (MISO) determines the spatial structure of the monsoon rainfall on subseasonal timescales, and its accurate prediction is therefore key for agricultural and hydrological planning. Here, we combine data-driven forecasts of MISO with an ensemble of dynamical forecasts of the full system, leveraging the predictability of MISO to improve monsoon forecasts. Our results show significant improvement compared to state-of-the-art dynamical model forecasts, demonstrating the potential of data-driven forecasts to improve subseasonal monsoon prediction.
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insu-04729892 , version 1 (16-01-2025)

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Eviatar Bach, V. Krishnamurthy, Safa Mote, Jagadish Shukla, A. Surjalal Sharma, et al.. Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes. Proceedings of the National Academy of Sciences of the United States of America, 2024, 121, 5, pp. 439-461. ⟨10.1073/pnas.2312573121⟩. ⟨insu-04729892⟩
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