A robust surrogate data assimilation approach to real-time forecasting using polynomial chaos expansion

被引:12
作者
Vinh Ngoc Tran [1 ]
Kim, Jongho [1 ]
机构
[1] Univ Ulsan, Sch Civil & Environm Engn, Ulsan, South Korea
关键词
Surrogate filter; Data assimilation; Polynomial chaos expansion; Ensemble Kalman filter; Real-time forecasting; Sequential experimental design-polynomial degree; ENSEMBLE KALMAN FILTER; SEQUENTIAL DATA ASSIMILATION; CHAIN MONTE-CARLO; UNCERTAINTY QUANTIFICATION; INTEGRATED SURFACE; NEURAL NETWORKS; MODELS; PREDICTIONS; SENSITIVITY; CHALLENGES;
D O I
10.1016/j.jhydrol.2021.126367
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Data assimilation plays an essential role in real-time forecasting but demands repetitive model evaluations given ensembles. To address this computational challenge, a novel, robust and efficient approach to surrogate data assimilation is presented. It replaces the internal processes of the ensemble Kalman filter (EnKF) with polynomial chaos expansion (PCE) theory. Eight types of surrogate filters, which can be characterized according to their different surrogate structures, building systems, and assimilating targets, are proposed and validated. To compensate for the potential shortcomings of the existing sequential experimental design (SED), an advanced optimization scheme, named sequential experimental design-polynomial degree (SED-PD), is also advised. Its dual optimization system resolves the issue of SED by which the value of the polynomial degree had to be selected ad-hoc or by trial and error; its multiple stopping criteria ensure convergence even when an accuracy metric does not monotonically decrease over iterations. A comprehensive investigation into how to configure a surrogate filter indicates that the new partial (replacing part of original filters) and invariant (valid for entire time periods) approaches are preferred in terms of accuracy and efficiency, which helps directly reduce the number of dimensions and bridge the gap between hindcasting and real-time forecasting. Of the eight filters, the Dual Invariant Partial filter performs best, with equivalent accuracy to Dual EnKF and about 500 times greater computational efficiency. Ultimately, this proposed surrogate filter will be a promising alternative tool for performing computationally-intensive data assimilation in high-dimensional problems.
引用
收藏
页数:22
相关论文
共 87 条
[81]   Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation [J].
Wang, S. ;
Ancell, B. C. ;
Huang, G. H. ;
Baetz, B. W. .
WATER RESOURCES RESEARCH, 2018, 54 (03) :2129-2151
[82]   Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion [J].
Wang, S. ;
Huang, G. H. ;
Baetz, B. W. ;
Ancell, B. C. .
JOURNAL OF HYDROLOGY, 2017, 548 :484-497
[83]   A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment [J].
Wang, S. ;
Huang, G. H. ;
Baetz, B. W. ;
Huang, W. .
JOURNAL OF HYDROLOGY, 2015, 530 :716-733
[84]   Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models [J].
Weerts, Albrecht H. ;
El Serafy, Ghada Y. H. .
WATER RESOURCES RESEARCH, 2006, 42 (09)
[85]   The homogeneous chaos [J].
Wiener, N .
AMERICAN JOURNAL OF MATHEMATICS, 1938, 60 :897-936
[86]   Systematic assessment of the uncertainty in integrated surface water-groundwater modeling based on the probabilistic collocation method [J].
Wu, Bin ;
Zheng, Yi ;
Tian, Yong ;
Wu, Xin ;
Yao, Yingying ;
Han, Feng ;
Liu, Jie ;
Zheng, Chunmiao .
WATER RESOURCES RESEARCH, 2014, 50 (07) :5848-5865
[87]   Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error [J].
Zhang, Jiangjiang ;
Zheng, Qiang ;
Chen, Dingjiang ;
Wu, Laosheng ;
Zeng, Lingzao .
WATER RESOURCES RESEARCH, 2020, 56 (01)