A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

被引：1

作者：

Gharamti, Mohamad E. ^{[1
]}

Ait-El-Fquih, Boujemaa ^{[2
]}

Hoteit, Ibrahim ^{[1
,2
]}

机构：

[1] King Abdullah Univ Sci & Technol, Earth Sci & Engn, Thuwal 23955, Saudi Arabia

[2] King Abdullah Univ Sci & Technol, Appl Math & Computat Sci, Thuwal 23955, Saudi Arabia

来源：

DYNAMIC DATA-DRIVEN ENVIRONMENTAL SYSTEMS SCIENCE, DYDESS 2014 | 2015年 / 8964卷

关键词：

DATA ASSIMILATION;

D O I：

10.1007/978-3-319-25138-7_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

引用

页码：207 / 214

页数：8

共 17 条

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