Particle network EnKF for large-scale data assimilation

被引:2
|
作者
Li, Xinjia [1 ]
Lu, Wenlian [1 ,2 ,3 ,4 ,5 ]
机构
[1] Fudan Univ, Sch Math Sci, Shanghai, Peoples R China
[2] Fudan Univ, Shanghai Ctr Math Sci, Shanghai, Peoples R China
[3] Fudan Univ, Shanghai Key Lab Contemporary Appl Math, Shanghai, Peoples R China
[4] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China
[5] Fudan Univ, Key Lab Computat Neurosci & Brain Inspired Intelli, Minist Educ, Shanghai, Peoples R China
来源
FRONTIERS IN PHYSICS | 2022年 / 10卷
基金
国家重点研发计划;
关键词
EnKF; data assimilation; gossip algorithms; decentralized sampling; large-scale problem; ENSEMBLE KALMAN FILTER; MODEL;
D O I
10.3389/fphy.2022.998503
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The Ensemble Kalman filter (EnKF) is a classic method of data assimilation. For distributed sampling, the conventional EnKF usually requires a centralized server to integrate the predictions of all particles or a fully-connected communication network, causing traffic jams and low bandwidth utilization in high-performance computing. In this paper, we propose a novel distributed scheme of EnKF based on network setting of sampling, called Particle Network EnKF. Without a central server, every sampling particle communicates with its neighbors over a sparsely connected network. Unlike the existing work, this method focuses on the distribution of sampling particles instead of sensors and has been proved effective and robust on numerous tasks. The numerical experiments on the Lorenz-63 and Lorenz-96 systems indicate that, with proper communication rounds, even on a sparse particle network, this method achieves a comparable performance to the standard EnKF. A detailed analysis of effects of the network topology and communication rounds is performed. Another experiment demonstrating a trade-off between the particle homogeneity and performance is also provided. The experiments on the whole-brain neuronal network model show promises for applications in large-scale assimilation problems.
引用
收藏
页数:15
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