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 journalArticlepeer-review

1 Citation (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
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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