Data-driven Data Assimilation Method based on Support Vector Machine Algorithm
Data-driven modeling is to discover the spatio-temporal evolution of state variables from data.Data-driven data assimilation is a scientific method to optimize the fusion of observation information and model by us-ing data-driven model instead of traditional(physics-based)model.In this work,a data-driven support vector machine regression prediction model is applied to the ensemble Kalman filtering process,and the dynamic sys-tem is reconstructed from the sample set by non-parametric sampling of the dynamic system trajectory using the simulation prediction method.A data driven data assimilation method based on support vector machine regres-sion machine learning simulation prediction strategy is proposed and applied to classical pattern driven data as-similation system.The Lorenz-63 and Lorenz-96 model are used for numerical experiments.The data assimila-tion performance is compared by changing the sensitivity parameters such as sample sizes,noise variance and ob-servation step sizes.The results show that the proposed method is superior to the general sequential data assimi-lation method for large sample sets,which proves the effectiveness of the new method.
Data-driven data assimilationSupport vector machineEnsemble Kalman filterLorenz model
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