Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter

Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng

doi:10.1016/j.advwatres.2016.03.016

Title: Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter

Full Record
Other Related Research

Authors:

;

; Zeng, Lingzao; Wu, Laosheng

Publication Date:: 2016-06-01

Sponsoring Org.:: USDOE

OSTI Identifier:: 1424548

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Advances in Water Resources

Additional Journal Information:: Journal Name: Advances in Water Resources Journal Volume: 92 Journal Issue: C; Journal ID: ISSN 0309-1708

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Man, Jun, Li, Weixuan, Zeng, Lingzao, and Wu, Laosheng. Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter.  United Kingdom: N. p., 2016. 
Web.  doi:10.1016/j.advwatres.2016.03.016.

Copy to clipboard


                    Man, Jun, Li, Weixuan, Zeng, Lingzao, & Wu, Laosheng. Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter.  United Kingdom.  https://doi.org/10.1016/j.advwatres.2016.03.016

Copy to clipboard


                    Man, Jun, Li, Weixuan, Zeng, Lingzao, and Wu, Laosheng. Wed .  
"Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter".  United Kingdom.  https://doi.org/10.1016/j.advwatres.2016.03.016.

Copy to clipboard


                    
@article{osti_1424548,

  title        = {Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter},

  author       = {Man, Jun and Li, Weixuan and Zeng, Lingzao and Wu, Laosheng},

  abstractNote = {},

  doi          = {10.1016/j.advwatres.2016.03.016},

  journal      = {Advances in Water Resources},

  number       = C,

  volume       = 92,

  place        = {United Kingdom},

  year         = {2016},

  month        = {6}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1016/j.advwatres.2016.03.016

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 20 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter

Journal Article Man, Jun ; Li, Weixuan ; Zeng, Lingzao ; ... - Advances in Water Resources

The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we proposemore »« less
https://doi.org/10.1016/j.advwatres.2016.03.016
Data assimilation in a solar dynamo model using Ensemble Kalman Filters: sensitivity and robustness in reconstruction of meridional flow speed

Journal Article Dikpati, Mausumi ; Anderson, Jeffrey L. ; Mitra, Dhrubaditya - Astrophysical Journal

We implement an Ensemble Kalman Filter procedure using the Data Assimilation Research Testbed for assimilating “synthetic” meridional flow-speed data in a Babcock–Leighton-type flux-transport solar dynamo model. By performing several “observing system simulation experiments,” we reconstruct time variation in meridional flow speed and analyze sensitivity and robustness of reconstruction. Using 192 ensemble members including 10 observations, each with 4% error, we find that flow speed is reconstructed best if observations of near-surface poloidal fields from low latitudes and tachocline toroidal fields from midlatitudes are assimilated. If observations include a mixture of poloidal and toroidal fields from different latitude locations, reconstruction is reasonably good for ⩽40% errormore »« less
https://doi.org/10.3847/0004-637X/828/2/91
Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

Journal Article Tarrahi, Mohammadali ; Elahi, Siavash Hakim ; Jafarpour, Behnam - Computational Geosciences

Not provided.
https://doi.org/10.1007/s10596-016-9570-7
Ensemble Kalman Filter for Assimilating Experimental Data into Large-Eddy Simulations of Turbulent Flows

Journal Article Labahn, Jeffrey W. ; Wu, Hao ; Harris, Shaun R. ; ... - Flow, Turbulence and Combustion

Data assimilation techniques are investigated for integrating high-speed high-resolution experimental data into large-eddy simulations. To this end, an ensemble Kalman filter is employed to assimilate velocity measurements of a turbulent jet at a Reynolds number of 13,500 into simulations. The goal of the current work is to examine the behavior of the assimilation algorithm for state estimation of turbulent flows that are of relevance to engineering applications. This is accomplished by investigating the impact that localization, measurement uncertainties, assimilation frequency, data sparsity and ensemble size have on the estimated state vector. For the flow configuration and computational setup considered inmore »« less
https://doi.org/10.1007/s10494-019-00093-1

Full Text Available
EAKF-CMAQ : introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter.

Journal Article Zubrow, A ; Chen, L ; Kotamarthi, V R ; ... - J. Geophys. Res.

A new approach is presented for data assimilation using the ensemble adjustment Kalman filter (EAKF) technique for surface measurements of carbon monoxide in a single tracer version of the community air quality model. An implementation of the EAKF known as the Data Assimilation Research Testbed at the National Center for Atmospheric Research was used for developing the model. Three different sets of numerical experiments were performed to test the effectiveness of the procedure and the range of key parameters used in implementing the procedure. The model domain includes much of the northeastern United States. The first two numerical experiments usemore »« less
https://doi.org/10.1029/2007JD009267

Similar Records