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 language | English |
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Article number | 101323 |
Journal | Journal of Computational Science |
Volume | 51 |
Early online date | 10 Feb 2021 |
DOIs | |
Publication status | Published - Apr 2021 |
User-Defined Keywords
- Data assimilation
- Four-Dimensional Variational Assimilation
- Machine learning
- Multi-layer perceptron
- Numerical forecast