Journal ArticleDOI
Ensemble smoother with multiple data assimilation
Reads0
Chats0
TLDR
This paper proposes to assimilate the same data multiple times with an inflated measurement error covariance matrix in order to improve the results obtained by the ensemble smoother, motivated by the equivalence between single and multiple data assimilation for the linear-Gaussian case.About:
This article is published in Computers & Geosciences.The article was published on 2013-06-01. It has received 644 citations till now. The article focuses on the topics: Ensemble Kalman filter & Data assimilation.read more
Citations
More filters
MonographDOI
Probabilistic forecasting and Bayesian data assimilation
Sebastian Reich,Colin J. Cotter +1 more
TL;DR: This book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas with a general dynamical systems approach.
Journal ArticleDOI
Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
Yan Chen,Dean S. Oliver +1 more
TL;DR: An efficient, iterative ensemble smoother algorithm based on the Levenberg–Marquardt (LM) method of regularizing the update direction and choosing the step length is developed and the incorporation of the LM damping parameter reduces the tendency to add model roughness at early iterations when the update step is highly nonlinear.
Journal ArticleDOI
An iterative ensemble Kalman smoother
TL;DR: The iterative ensemble Kalman filter (IEnKF) was proposed in this article to improve the performance of Ensemble Kalman filtering with strongly nonlinear geophysical models.
Journal ArticleDOI
Analysis of iterative ensemble smoothers for solving inverse problems
TL;DR: The iterative methods are compared with the standard Ensemble Smoother to improve the understanding of the similarities and differences between them, and the three smoothers from Bayes’ theorem for a scalar case are derived to compare the equations solved by the three methods.
Journal ArticleDOI
Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
TL;DR: This paper uses a small but highly nonlinear reservoir model so that it can generate the reference posterior distribution of reservoir properties using a very long chain generated by a Markov chain Monte Carlo sampling algorithm.
References
More filters
Book
Inverse Problem Theory and Methods for Model Parameter Estimation
TL;DR: This chapter discusses Monte Carol methods, the least-absolute values criterion and the minimax criterion, and their applications to functional inverse problems.
Journal ArticleDOI
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Book
Data Assimilation: The Ensemble Kalman Filter
TL;DR: In this paper, the authors define a statistical analysis scheme for estimating an oil reservoir simulator and an ocean prediction system based on the En-KF model, and propose a sampling strategy for the EnKF and square root analysis schemes.
Journal ArticleDOI
Hydrologic Data Assimilation with the Ensemble Kalman Filter
TL;DR: In this paper, the performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model.