Fast data assimilation (FDA): Data assimilation by machine learning for faster optimize model state

Pin Wu*, Xuting Chang, Wenyan Yuan, Junwu Sun, Wenjie Zhang, Rossella Arcucci, Yi-Ke GUO

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    12 Citations (Scopus)

    Abstract

    Data assimilation (DA) can provide the more accurate initial state for numerical forecasting models. But traditional DA algorithms has the problem of long calculation time. This paper proposes fast data assimilation (FDA) based on machine learning. For training model, FDA uses 4DVAR, iForest, MLP, and also includes a modified model that does not require observations. This paper applies FDA in the Lorenz63 dynamical system. The experimental results show that the single analysis time of FDA is almost 524 times faster than 4DVAR. FDA greatly reduces the time of the DA process.

    Original languageEnglish
    Article number101323
    JournalJournal of Computational Science
    Volume51
    Early online date10 Feb 2021
    DOIs
    Publication statusPublished - Apr 2021

    Scopus Subject Areas

    • Theoretical Computer Science
    • Computer Science(all)
    • Modelling and Simulation

    User-Defined Keywords

    • Data assimilation
    • Four-Dimensional Variational Assimilation
    • Machine learning
    • Multi-layer perceptron
    • Numerical forecast

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