Abstract
Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 8-18 |
| Number of pages | 11 |
| Journal | Advances in Water Resources |
| Volume | 66 |
| DOIs | |
| State | Published - Apr 2014 |
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Keywords
- Data assimilation
- Ensemble Kalman Filter
- Filter inbreeding
- Moment equations
- Random hydraulic conductivity field
- Transient groundwater flow
ASJC Scopus subject areas
- Water Science and Technology
Cite this
Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation. / Panzeri, M.; Riva, M.; Guadagnini, A.; Neuman, Shlomo P.
In: Advances in Water Resources, Vol. 66, 04.2014, p. 8-18.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation
AU - Panzeri, M.
AU - Riva, M.
AU - Guadagnini, A.
AU - Neuman, Shlomo P
PY - 2014/4
Y1 - 2014/4
N2 - Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.
AB - Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.
KW - Data assimilation
KW - Ensemble Kalman Filter
KW - Filter inbreeding
KW - Moment equations
KW - Random hydraulic conductivity field
KW - Transient groundwater flow
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UR - http://www.scopus.com/inward/citedby.url?scp=84896881733&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2014.01.007
DO - 10.1016/j.advwatres.2014.01.007
M3 - Article
AN - SCOPUS:84896881733
VL - 66
SP - 8
EP - 18
JO - Advances in Water Resources
JF - Advances in Water Resources
SN - 0309-1708
ER -