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Deep learning for bias correction of MJO prediction

Author

Listed:
  • H. Kim

    (Stony Brook University)

  • Y. G. Ham

    (Chonnam National University)

  • Y. S. Joo

    (Chonnam National University)

  • S. W. Son

    (Seoul National University)

Abstract

Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

Suggested Citation

  • H. Kim & Y. G. Ham & Y. S. Joo & S. W. Son, 2021. "Deep learning for bias correction of MJO prediction," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23406-3
    DOI: 10.1038/s41467-021-23406-3
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    Cited by:

    1. Fenghua Ling & Jing-Jia Luo & Yue Li & Tao Tang & Lei Bai & Wanli Ouyang & Toshio Yamagata, 2022. "Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Soukayna Mouatadid & Paulo Orenstein & Genevieve Flaspohler & Judah Cohen & Miruna Oprescu & Ernest Fraenkel & Lester Mackey, 2023. "Adaptive bias correction for improved subseasonal forecasting," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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