TY - JOUR
T1 - Fast data assimilation (FDA)
T2 - Data assimilation by machine learning for faster optimize model state
AU - Wu, Pin
AU - Chang, Xuting
AU - Yuan, Wenyan
AU - Sun, Junwu
AU - Zhang, Wenjie
AU - Arcucci, Rossella
AU - GUO, Yi-Ke
N1 - Funding Information:
This work is supported by State Key Laboratory of Aerodynamics, China , China Aerodynamics Research and Development Center under Grant SKLA20180303 , Natural Science Foundation of Shanghai, China under Grant 19ZR1417700 , and Transforming Systems through Partnership, Newton Fund under Grant TSPC1086 .
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Data assimilation
KW - Four-Dimensional Variational Assimilation
KW - Machine learning
KW - Multi-layer perceptron
KW - Numerical forecast
UR - http://www.scopus.com/inward/record.url?scp=85101903229&partnerID=8YFLogxK
U2 - 10.1016/j.jocs.2021.101323
DO - 10.1016/j.jocs.2021.101323
M3 - Article
AN - SCOPUS:85101903229
SN - 1877-7503
VL - 51
JO - Journal of Computational Science
JF - Journal of Computational Science
M1 - 101323
ER -