Ensemble Kalman methods

Data Assimilation; Data fusion; Sloppy filters for over-ambitious models

June 22, 2015 — May 27, 2024

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\[\renewcommand{\var}{\operatorname{Var}} \renewcommand{\cov}{\operatorname{Cov}} \renewcommand{\dd}{\mathrm{d}} \renewcommand{\bb}[1]{\mathbb{#1}} \renewcommand{\vv}[1]{\boldsymbol{#1}} \renewcommand{\rv}[1]{\mathsf{#1}} \renewcommand{\vrv}[1]{\vv{\rv{#1}}} \renewcommand{\disteq}{\stackrel{d}{=}} \renewcommand{\gvn}{\mid} \renewcommand{\Ex}{\mathbb{E}} \renewcommand{\Pr}{\mathbb{P}} \renewcommand{\one}{\unicode{x1D7D9}}\]

A random-sampling variant/generalisation of the Kalman-Bucy filter. That also describes particle filters, but the randomisation in ensemble methods is different than those. We can do both types of randomisation. This sent has a few tweaks that make it more tenable in tricky situations with high dimensional state spaces or nonlinearities in inconvenient places. A popular data assimilation method for spatiotemporal models.

Figure 1: Ensemble Kalman filters make it somewhat easier to wring estimates out of data.

1 Tutorial introductions

Katzfuss, Stroud, and Wikle (2016);Roth et al. (2017);Fearnhead and Künsch (2018), are all pretty good. Schillings and Stuart (2017) has been recommended by Haber, Lucka, and Ruthotto (2018) as the canonical modern version. Wikle and Berliner (2007) present a broad data assimilation context on these methods, although it is too curt to be helpful for me. Mandel (2009) is helpfully longer. The inventor of the method explains it in Evensen (2003), but I found that hard going, since it uses too much oceanography terminology. Roth et al. (2017) is probably the best for my background. Let us copy their notation.

We start from the discrete-time state-space models; the basic one is the linear system \[ \begin{aligned} x_{k+1} &=F x_{k}+G v_{k}, \\ y_{k} &=H x_{k}+e_{k}, \end{aligned} \] with state \(x\in\mathbb{R}^n\) and the measurement \(y\in\mathbb{R}^m\). The initial state \(x_{0}\), the process noise \(v_{k}\), and the measurement noise \(e_{k}\) are mutually independent such that \[\begin{aligned} \Ex x_{0}&=\hat{x}_{0}\\ \Ex v_{k}&=0\\ \Ex e_{k}&=0\\ \cov x_{0} &=P_{0}\\ \cov v_{k} & =Q\\ \cov e_{k}&=R \end{aligned}\] and all are Gaussian.

The Kalman filter propagates state estimates \(\hat{x}_{k \mid k}\) and covariance matrices \(P_{k \mid k}\) for this model. The KF update or prediction or forecast is given by the step \[ \begin{aligned} &\hat{x}_{k+1 \mid k}=F \hat{x}_{k \mid k} \\ &P_{k+1 \mid k}=F P_{k \mid k} F^{\top}+G Q G^{\top} \end{aligned} \] We predict the observations forward using these state estimates via \[ \begin{aligned} \hat{y}_{k \mid k-1} &=H \hat{x}_{k \mid k-1}, \\ S_{k} &=H P_{k \mid k-1} H^{\top}+R . \end{aligned} \] Given these and an actual observation, we update the state estimates using a gain matrix, \(K_{k}\) \[ \begin{aligned} \hat{x}_{k \mid k} &=\hat{x}_{k \mid k-1}+K_{k}\left(y_{k}-\hat{y}_{k \mid k-1}\right) \\ &=\left(I-K_{k} H\right) \hat{x}_{k \mid k-1}+K_{k} y_{k}, \\ P_{k \mid k} &=\left(I-K_{k} H\right) P_{k \mid k-1}\left(I-K_{k} H\right)^{\top}+K_{k} R K_{k}^{\top}. \end{aligned} \] in what geoscience types refer to as an analysis update. The variance-minimising gain is given \[ K_{k}=P_{k \mid k-1} H^{\top} S_{k}^{-1}=M_{k} S_{k}^{-1}, \] where \(M_{k}\) is the cross-covariance between the state and output predictions.

In the Ensemble Kalman filter, we approximate some of these quantities of interest using samples; this allows us to relax the assumption of Gaussianity and gets us computational savings in certain problems of interest. That does sound very similar to particle filters, and indeed there is a relation.

Figure 2: Various extensions of Kalman filters as per Katzfuss, Stroud, and Wikle (2016).

Instead of maintaining the \(n\)-dimensional estimate \(\hat{x}_{k \mid k}\) and the \(n \times n\) covariance \(P_{k \mid k}\) as such, we maintain an ensemble of \(N<n\) sampled state realizations \[X_{k}:=\left[x_{k}^{(i)}\right]_{i=1}^{N}.\] This notation is intended to imply that we are treating these realisations as an \(n \times N\) matrix \(X_{k \mid k}\) with columns \(x_{k}^{(i)}\). We introduce the following notation for ensemble moments: \[ \begin{aligned} &\bar{x}_{k \mid k}=\frac{1}{N} X_{k \mid k} \one \\ &\bar{P}_{k \mid k}=\frac{1}{N-1} \widetilde{X}_{k \mid k} \widetilde{X}_{k \mid k}^{\top}, \end{aligned} \] where \(\one=[1, \ldots, 1]^{\top}\) is an \(N\)-dimensional vector and \[ \widetilde{X}_{k \mid k}=X_{k \mid k}-\bar{x}_{k \mid k} \one^{\top}=X_{k \mid k}\left(I_{N}-\frac{1}{N} \one \one^{\top}\right) \] is an ensemble of anomalies/deviations from \(\bar{x}_{k \mid k}\), which I would call it the centred version. We attempt to match the moments of the ensemble with those realised by a true Kalman filter, in the sense that \[ \begin{aligned} &\bar{x}_{k \mid k}:=\frac{1}{N} \sum_{i=1}^{N} x_{k}^{(i)} \approx \hat{x}_{k \mid k}, \\ &\bar{P}_{k \mid k}:=\frac{1}{N-1} \sum_{i=1}^{N}\left(x_{k}^{(i)}-\bar{x}_{k \mid k}\right)\left(x_{k}^{(i)}-\bar{x}_{k \mid k}\right)^{\top} \approx P_{k \mid k} . \end{aligned} \] The forecast step computes \(X_{k+1 \mid k}\) such that its moments are close to \(\hat{x}_{k+1 \mid k}\) and \(P_{k+1 \mid k}\). An ensemble of \(N\) independent process noise realizations \(V_{k}:=\left[v_{k}^{(i)}\right]_{i=1}^{N}\) with zero mean and covariance \(Q\), is used in \[ X_{k+1 \mid k}=F X_{k \mid k}+G V_{k}. \]

Next the \(X_{k \mid k-1}\) is adjusted to obtain the filtering ensemble \(X_{k \mid k}\) by applying an update to each ensemble member: With some gain matrix \(\bar{K}_{k}\) the KF update is applied to the ensemble by the update \[ X_{k \mid k}=\left(I-\bar{K}_{k} H\right) X_{k \mid k-1}+\bar{K}_{k} y_{k} \one^{\top} . \] This does not yet approximate the update of the full Kalman observation — there is no term \(\bar{K}_{k} R \bar{K}_{k}^{\top}\); We have a choice how to implement that.

1.1 Stochastic EnKF update

In the stochastic method, we use artificial zero-mean measurement noise realizations \(E_{k}:=\left[e_{k}^{(i)}\right]_{i=1}^{N}\) with covariance \(R\). \[ X_{k \mid k}=\left(I-\bar{K}_{k} H\right) X_{k \mid k-1}+\bar{K}_{k} y_{k} \one^{\top}-\bar{K}_{k} E_{k} . \] The resulting \(X_{k \mid k}\) has the correct ensemble mean and covariance, \(\hat{x}_{k \mid k}\) and \(P_{k \mid k}\).

If we define a predicted output ensemble \[ Y_{k \mid k-1}=H X_{k \mid k-1}+E_{k} \] that evokes the classic Kalman update (and encapsulates information about) \(\hat{y}_{k \mid k-1}\) and \(S_{k}\), we can rewrite this update into one that resembles the Kalman update: \[ X_{k \mid k}=X_{k \mid k-1}+\bar{K}_{k}\left(y_{k} \one^{\top}-Y_{k \mid k-1}\right) . \]

Now, the gain matrix \(\bar{K}_{k}\) in the classic KF is computed from the covariance matrices of the predicted state and output. In the EnKF, the required \(M_{k}\) and \(S_{k}\) must be estimated from the prediction ensembles. The obvious way of doing that is to once again centre the ensemble, \[ \begin{aligned} &\widetilde{X}_{k \mid k-1}=X_{k \mid k-1}\left(I_{N}-\frac{1}{N} \one \one^{\top}\right) \\ &\widetilde{Y}_{k \mid k-1}=Y_{k \mid k-1}\left(I_{N}-\frac{1}{N} \one \one^{\top}\right) \end{aligned} \] and use the empirical ensemble covariances \[ \begin{aligned} \bar{M}_{k} &=\frac{1}{N-1} \widetilde{X}_{k \mid k-1} \widetilde{X}_{k \mid k-1}^{\top}, \\ \bar{S}_{k} &=\frac{1}{N-1} \widetilde{Y}_{k \mid k-1} \widetilde{Y}_{k \mid k-1}^{\top} . \end{aligned} \] The gain \(\bar{K}_{k}\) is then the solution to the system of linear equations, \[ \bar{K}_{k} \widetilde{Y}_{k \mid k-1} \widetilde{Y}_{k \mid k-1}^{\top}=\widetilde{X}_{k \mid k-1} \widetilde{Y}_{k \mid k-1}^{\top} \]

1.2 Deterministic update

Resemblance to unscented/sigma-point filtering also apparent. TBD.

The additive measurement noise model we have used the \(e_{k}\) for should not affect the cross covariance \(M_k\). Thus it is reasonable to make the substitution \[ \widetilde{Y}_{k \mid k-1}\longrightarrow \widetilde{Z}_{k \mid k-1}=H \widetilde{X}_{k \mid k-1} \] to get a less noisy update \[ \begin{aligned} \bar{M}_{k} &=\frac{1}{N-1} \widetilde{X}_{k \mid k-1} \widetilde{Z}_{k \mid k-1}^{\top} \\ \bar{S}_{k} &=\frac{1}{N-1} \widetilde{Z}_{k \mid k-1} \widetilde{Z}_{k \mid k-1}^{\top}+R \end{aligned} \] The Kalman gain \(\bar{K}_{k}\) is then computed as in the KF. Or we can interpret it as a matrix square-root \(R^{\frac{1}{2}}\) with \(R^{\frac{1}{2}} R^{\frac{\top}{2}}=R\) and then factorize \[ \bar{S}_{k}=\left[\begin{array}{cc}\frac{1}{\sqrt{N-1}} \widetilde{Z}_{k \mid k-1}\quad R^{\frac{1}{2}}\end{array}\right] \left[\begin{array}{c}\frac{1}{\sqrt{N-1}} \widetilde{Z}^{\top}_{k \mid k-1} \\ R^{\frac{\top}{2}}\end{array}\right]. \]

TBD: EAKF and ETKF (Tippett et al. 2003) which deterministically propagate an estimate \[ P_{k \mid k}^{\frac{1}{2}} P_{k \mid k}^{\frac{\top}{2}}=P_{k \mid k} \] which introduces less sampling noise. Roth et al. (2017) explain it as rewriting the measurement update to use a square root \(P_{k \mid k-1}^{\frac{1}{2}}\) and in particular the ensemble approximation \(\frac{1}{N-1} \widetilde{X}_{k \mid k-1}\) : \[ \begin{aligned} P_{k \mid k} &=\left(I-K_{k} H\right) P_{k \mid k-1} \\ &=P_{k \mid k-1}^{\frac{1}{2}}\left(I-P_{k \mid k-1}^{\frac{\top}{2}} H^{\top} S_{k}^{-1} H P_{k \mid k-1}^{\frac{1}{2}}\right) P_{k \mid k-1}^{\frac{\top}{2}} \\ & \approx \frac{1}{N-1} \widetilde{X}_{k \mid k-1}\left(I-\frac{1}{N-1} \widetilde{Z}_{k \mid k-1}^{\top} \bar{S}_{k}^{-1} \widetilde{Z}_{k \mid k-1}\right) \widetilde{X}_{k \mid k-1}^{\top}. \end{aligned} \] Factorising, \[ \left(I-\frac{1}{N-1} \widetilde{Z}_{k \mid k-1}^{\top} \bar{S}_{k}^{-1} \widetilde{Z}_{k \mid k-1}\right)=\Pi_{k}^{\frac{1}{2}} \Pi_{k}^{\frac{\top}{2}}, \] The \(\Pi_{k}^{\frac{1}{2}}\in\mathbb{R}^{N\times N}\) can be used to create a deviation ensemble \[ \tilde{X}_{k \mid k}=\tilde{X}_{k \mid k-1} \Pi_{k}^{\frac{1}{2}} \] that correctly encodes \(P_{k \mid k}\) without using random perturbations. The actual filtering is achieved by updating each sample according to \[ \bar{x}_{k \mid k}=\left(I-\bar{K}_{k} H\right) F_{x_{k-1 \mid k-1}}+\bar{K}_{k} y_{k}, \] where \(\bar{K}_{k}\) is computed from the deviation ensembles.

2 As least-squares

TBD. Permits calculating the operations without forming covariance matrices.

3 As Approximate Bayesian computation

Nott, Marshall, and Ngoc (2012) uses Beaumont, Zhang, and Balding (2002);, Blum and François (2010) and Lei and Bickel (2009) to view EnKF this as an approximate Bayesian computation algorithm.

4 Going nonlinear

TBD

The EnKF does not necessarily converge to a Kalman filter in the limit of infinite ensemble size (R. Furrer and Bengtsson 2007)

5 Monte Carlo moves in the ensemble

The ensemble is rank deficient. Question: When can we sample other states from the ensemble to improve the rank by stationary posterior moves?

6 Managing overconfidence

TBD

7 Ensemble methods in smoothing

Katzfuss, Stroud, and Wikle (2016) claims there are two major approaches to smoothing: Stroud et al. (2010) -type reverse methods, and the EnKS (Evensen and van Leeuwen 2000) which augments the states with lagged copies rather than doing a reverse pass.

Here are some other papers I saw N. K. Chada, Chen, and Sanz-Alonso (2021);Luo et al. (2015);White (2018);Zhang et al. (2018).

8 System identification in

Can we use ensemble methods for online parameter estimation? Apparently. Evensen (2009b);Malartic, Farchi, and Bocquet (2021);Moradkhani et al. (2005);Fearnhead and Künsch (2018).

9 Theoretical basis for probabilists

Bishop and Del Moral (2023);P. Del Moral, Kurtzmann, and Tugaut (2017);Garbuno-Inigo et al. (2020);Kelly, Law, and Stuart (2014);Le Gland, Monbet, and Tran (2009);Taghvaei and Mehta (2021).

10 Lanczos trick in precision estimates

Pleiss et al. (2018),Ubaru, Chen, and Saad (2017).

11 Localization

Hunt, Kostelich, and Szunyogh (2007); Ott et al. (2004); Ott et al. (2004)

12 Relation to particle filters

Intimate. See particle filters.

13 Schilling’s filter

Claudia Schilling’s filter (Schillings and Stuart 2017) is an elegant version which looks somehow more general than the original but also simpler. Haber, Lucka, and Ruthotto (2018) use it to train neural nets (!) and show a rather beautiful connection to stochastic gradient descent in section 3.2.

See Neural nets by ensemble Kalman filtering.

14 Handy low-rank tricks for

See low-rank tricks.

15 Incoming

16 References

Alsup, Venturi, and Peherstorfer. 2022. “Multilevel Stein Variational Gradient Descent with Applications to Bayesian Inverse Problems.” In Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference.
Alzraiee, White, Knowling, et al. 2022. “A Scalable Model-Independent Iterative Data Assimilation Tool for Sequential and Batch Estimation of High Dimensional Model Parameters and States.” Environmental Modelling & Software.
Ambrogioni, Guclu, and van Gerven. 2019. “Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference.”
Ameli, and Shadden. 2023. “A Singular Woodbury and Pseudo-Determinant Matrix Identities and Application to Gaussian Process Regression.” Applied Mathematics and Computation.
Anderson, Jeffrey L. 2007. “Exploring the Need for Localization in Ensemble Data Assimilation Using a Hierarchical Ensemble Filter.” Physica D: Nonlinear Phenomena, Data Assimilation,.
———. 2009. “Ensemble Kalman Filters for Large Geophysical Applications.” IEEE Control Systems Magazine.
Anderson, Jeffrey, Hoar, Raeder, et al. 2009. “The Data Assimilation Research Testbed: A Community Facility.” Bulletin of the American Meteorological Society.
Beaumont, Zhang, and Balding. 2002. “Approximate Bayesian Computation in Population Genetics.” Genetics.
Bickel, and Levina. 2008. “Regularized Estimation of Large Covariance Matrices.” The Annals of Statistics.
Bishop, and Del Moral. 2019. “On the Stability of Matrix-Valued Riccati Diffusions.” Electronic Journal of Probability.
———. 2023. “On the Mathematical Theory of Ensemble (Linear-Gaussian) Kalman-Bucy Filtering.” Mathematics of Control, Signals, and Systems.
Bishop, Del Moral, and Niclas. 2020. “A Perturbation Analysis of Stochastic Matrix Riccati Diffusions.” Annales de l’Institut Henri Poincaré, Probabilités Et Statistiques.
Bishop, Del Moral, and Pathiraja. 2017. “Perturbations and Projections of Kalman-Bucy Semigroups Motivated by Methods in Data Assimilation.” arXiv:1701.05978 [Math].
Blum, and François. 2010. “Non-Linear Regression Models for Approximate Bayesian Computation.” Statistics and Computing.
Bocquet, Pires, and Wu. 2010. “Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation.” Monthly Weather Review.
Botha, Adams, Tran, et al. 2022. “Component-Wise Iterative Ensemble Kalman Inversion for Static Bayesian Models with Unknown Measurement Error Covariance.”
Chada, Neil K., Chen, and Sanz-Alonso. 2021. “Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants.” Foundations of Data Science.
Chada, Neil, and Tong. 2022. “Convergence Acceleration of Ensemble Kalman Inversion in Nonlinear Settings.” Mathematics of Computation.
Chen, Chong, Dou, Chen, et al. 2022. “A Novel Neural Network Training Framework with Data Assimilation.” The Journal of Supercomputing.
Chen, Yan, and Oliver. 2013. “Levenberg–Marquardt Forms of the Iterative Ensemble Smoother for Efficient History Matching and Uncertainty Quantification.” Computational Geosciences.
Chen, Yuming, Sanz-Alonso, and Willett. 2021. “Auto-Differentiable Ensemble Kalman Filters.”
Del Moral, P., Kurtzmann, and Tugaut. 2017. “On the Stability and the Uniform Propagation of Chaos of a Class of Extended Ensemble Kalman-Bucy Filters.” SIAM Journal on Control and Optimization.
Del Moral, Pierre, and Niclas. 2018. “A Taylor Expansion of the Square Root Matrix Functional.”
Dolcetti, and Pertici. 2020. “Real Square Roots of Matrices: Differential Properties in Semi-Simple, Symmetric and Orthogonal Cases.”
Dubrule. 2018. “Kriging, Splines, Conditional Simulation, Bayesian Inversion and Ensemble Kalman Filtering.” In Handbook of Mathematical Geosciences: Fifty Years of IAMG.
Duffin, Cripps, Stemler, et al. 2021. “Statistical Finite Elements for Misspecified Models.” Proceedings of the National Academy of Sciences.
Dunbar, Duncan, Stuart, et al. 2022. “Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods.” SIAM Journal on Applied Dynamical Systems.
Evensen. 1994. “Sequential Data Assimilation with a Nonlinear Quasi-Geostrophic Model Using Monte Carlo Methods to Forecast Error Statistics.” Journal of Geophysical Research: Oceans.
———. 2003. “The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation.” Ocean Dynamics.
———. 2004. “Sampling Strategies and Square Root Analysis Schemes for the EnKF.” Ocean Dynamics.
———. 2009a. Data Assimilation - The Ensemble Kalman Filter.
———. 2009b. “The Ensemble Kalman Filter for Combined State and Parameter Estimation.” IEEE Control Systems.
Evensen, and van Leeuwen. 2000. “An Ensemble Kalman Smoother for Nonlinear Dynamics.” Monthly Weather Review.
Fearnhead, and Künsch. 2018. “Particle Filters and Data Assimilation.” Annual Review of Statistics and Its Application.
Finn, Geppert, and Ament. 2021. “Ensemble-Based Data Assimilation of Atmospheric Boundary Layerobservations Improves the Soil Moisture Analysis.” Preprint.
Furrer, R., and Bengtsson. 2007. “Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants.” Journal of Multivariate Analysis.
Furrer, Reinhard, Genton, and Nychka. 2006. “Covariance Tapering for Interpolation of Large Spatial Datasets.” Journal of Computational and Graphical Statistics.
Galy-Fajou, Perrone, and Opper. 2021. “Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation.” Entropy.
Garbuno-Inigo, Hoffmann, Li, et al. 2020. “Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler.” SIAM Journal on Applied Dynamical Systems.
Grooms, and Robinson. 2021. “A Hybrid Particle-Ensemble Kalman Filter for Problems with Medium Nonlinearity.” PLOS ONE.
Grumitt, Karamanis, and Seljak. 2023. “Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems.”
Guth, Schillings, and Weissmann. 2020. “Ensemble Kalman Filter for Neural Network Based One-Shot Inversion.”
Haber, Lucka, and Ruthotto. 2018. “Never Look Back - A Modified EnKF Method and Its Application to the Training of Neural Networks Without Back Propagation.” arXiv:1805.08034 [Cs, Math].
Heemink, Verlaan, and Segers. 2001. “Variance Reduced Ensemble Kalman Filtering.” Monthly Weather Review.
Hou, Lawrence, and Hero. 2016. “Penalized Ensemble Kalman Filters for High Dimensional Non-Linear Systems.” arXiv:1610.00195 [Physics, Stat].
Houtekamer, and Mitchell. 2001. “A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation.” Monthly Weather Review.
Houtekamer, and Zhang. 2016. “Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation.” Monthly Weather Review.
Huang, Schneider, and Stuart. 2022. “Iterated Kalman Methodology for Inverse Problems.” Journal of Computational Physics.
Hunt, Kostelich, and Szunyogh. 2007. “Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter.” Physica D: Nonlinear Phenomena, Data Assimilation,.
Julier, and Uhlmann. 1997. “New Extension of the Kalman Filter to Nonlinear Systems.” In Signal Processing, Sensor Fusion, and Target Recognition VI.
Kantas, Doucet, Singh, et al. 2015. “On Particle Methods for Parameter Estimation in State-Space Models.” Statistical Science.
Katzfuss, Stroud, and Wikle. 2016. “Understanding the Ensemble Kalman Filter.” The American Statistician.
Keller, and Potthast. 2024. “AI-Based Data Assimilation: Learning the Functional of Analysis Estimation.”
Kelly, Law, and Stuart. 2014. “Well-Posedness and Accuracy of the Ensemble Kalman Filter in Discrete and Continuous Time.” Nonlinearity.
Kovachki, and Stuart. 2019. “Ensemble Kalman Inversion: A Derivative-Free Technique for Machine Learning Tasks.” Inverse Problems.
Kuzin, Yang, Isupova, et al. 2018. “Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning.” 2018 21st International Conference on Information Fusion (FUSION).
Labahn, Wu, Harris, et al. 2020. “Ensemble Kalman Filter for Assimilating Experimental Data into Large-Eddy Simulations of Turbulent Flows.” Flow, Turbulence and Combustion.
Lakshmivarahan, and Stensrud. 2009. “Ensemble Kalman Filter.” IEEE Control Systems Magazine.
Law, Tembine, and Tempone. 2016. “Deterministic Mean-Field Ensemble Kalman Filtering.” SIAM Journal on Scientific Computing.
Le Gland, Monbet, and Tran. 2009. “Large Sample Asymptotics for the Ensemble Kalman Filter.” Report.
Lei, and Bickel. 2009. “Ensemble Filtering for High Dimensional Nonlinear State Space Models.” University of California, Berkeley, Rep.
Lei, Bickel, and Snyder. 2009. “Comparison of Ensemble Kalman Filters Under Non-Gaussianity.” Monthly Weather Review.
Luo, Stordal, Lorentzen, et al. 2015. “Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications.” SPE Journal.
Malartic, Farchi, and Bocquet. 2021. “State, Global and Local Parameter Estimation Using Local Ensemble Kalman Filters: Applications to Online Machine Learning of Chaotic Dynamics.” arXiv:2107.11253 [Nlin, Physics:physics, Stat].
Mandel. 2009. “A Brief Tutorial on the Ensemble Kalman Filter.”
Masegosa. 2020. “Learning Under Model Misspecification: Applications to Variational and Ensemble Methods.” In Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20.
Mitchell, and Houtekamer. 2000. “An Adaptive Ensemble Kalman Filter.” Monthly Weather Review.
Moradkhani, Sorooshian, Gupta, et al. 2005. “Dual State–Parameter Estimation of Hydrological Models Using Ensemble Kalman Filter.” Advances in Water Resources.
Nott, Marshall, and Ngoc. 2012. “The Ensemble Kalman Filter Is an ABC Algorithm.” Statistics and Computing.
Nychka, and Anderson. 2010. “Data Assimilation.” In Handbook of Spatial Statistics.
O’Kane, Sandery, Kitsios, Sakov, Chamberlain, Collier, et al. 2021. “CAFE60v1: A 60-Year Large Ensemble Climate Reanalysis. Part I: System Design, Model Configuration and Data Assimilation.” Journal of Climate.
O’Kane, Sandery, Kitsios, Sakov, Chamberlain, Squire, et al. 2021. “CAFE60v1: A 60-Year Large Ensemble Climate Reanalysis. Part II: Evaluation.” Journal of Climate.
Oliver. 2022. “Hybrid Iterative Ensemble Smoother for History Matching of Hierarchical Models.” Mathematical Geosciences.
Ott, Hunt, Szunyogh, et al. 2004. “A Local Ensemble Kalman Filter for Atmospheric Data Assimilation.” Tellus A: Dynamic Meteorology and Oceanography.
Pleiss, Gardner, Weinberger, et al. 2018. “Constant-Time Predictive Distributions for Gaussian Processes.” In.
Popov. 2022. “Combining Data-Driven and Theory-Guided Models in Ensemble Data Assimilation.” ETD.
Raanes, P. 2016. “Introduction to Data Assimilation and the Ensemble Kalman Filter.” In.
Raanes, Patrick Nima, Stordal, and Evensen. 2019. “Revising the stochastic iterative ensemble smoother.” Nonlinear Processes in Geophysics.
Reich, and Weissmann. 2019. “Fokker-Planck Particle Systems for Bayesian Inference: Computational Approaches.”
Roth, Hendeby, Fritsche, et al. 2017. “The Ensemble Kalman Filter: A Signal Processing Perspective.” EURASIP Journal on Advances in Signal Processing.
Routray, Osuri, Pattanayak, et al. 2016. “Introduction to Data Assimilation Techniques and Ensemble Kalman Filter.” In Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction.
Sainsbury-Dale, Zammit-Mangion, and Huser. 2022. “Fast Optimal Estimation with Intractable Models Using Permutation-Invariant Neural Networks.”
Sandery, O’Kane, Kitsios, et al. 2020. “Climate Model State Estimation Using Variants of EnKF Coupled Data Assimilation.” Monthly Weather Review.
Schillings, and Stuart. 2017. “Analysis of the Ensemble Kalman Filter for Inverse Problems.” SIAM Journal on Numerical Analysis.
Schneider, Stuart, and Wu. 2022. “Ensemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data.” Journal of Computational Physics.
Shumway, and Stoffer. 2011. Time Series Analysis and Its Applications. Springer Texts in Statistics.
Song, Sebe, and Wang. 2022. “Fast Differentiable Matrix Square Root.” In.
Spantini, Baptista, and Marzouk. 2022. “Coupling Techniques for Nonlinear Ensemble Filtering.” SIAM Review.
Stordal, Moraes, Raanes, et al. 2021. “P-Kernel Stein Variational Gradient Descent for Data Assimilation and History Matching.” Mathematical Geosciences.
Stroud, Katzfuss, and Wikle. 2018. “A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation.” Monthly Weather Review.
Stroud, Stein, Lesht, et al. 2010. “An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation.” Journal of the American Statistical Association.
Taghvaei, and Mehta. 2021. “An Optimal Transport Formulation of the Ensemble Kalman Filter.” IEEE Transactions on Automatic Control.
Tamang, Ebtehaj, van Leeuwen, et al. 2021. “Ensemble Riemannian Data Assimilation over the Wasserstein Space.” Nonlinear Processes in Geophysics.
Tippett, Anderson, Bishop, et al. 2003. “Ensemble Square Root Filters.” Monthly Weather Review.
Ubaru, Chen, and Saad. 2017. “Fast Estimation of \(tr(f(A))\) via Stochastic Lanczos Quadrature.” SIAM Journal on Matrix Analysis and Applications.
Verlaan, and Heemink. 1997. “Tidal Flow Forecasting Using Reduced Rank Square Root Filters.” Stochastic Hydrology and Hydraulics.
Wen, and Li. 2022. “Affine-Mapping Based Variational Ensemble Kalman Filter.” Statistics and Computing.
White. 2018. “A Model-Independent Iterative Ensemble Smoother for Efficient History-Matching and Uncertainty Quantification in Very High Dimensions.” Environmental Modelling & Software.
Wikle, and Berliner. 2007. “A Bayesian Tutorial for Data Assimilation.” Physica D: Nonlinear Phenomena, Data Assimilation,.
Wikle, and Hooten. 2010. “A General Science-Based Framework for Dynamical Spatio-Temporal Models.” TEST.
Yang, Stroud, and Huerta. 2018. “Sequential Monte Carlo Smoothing with Parameter Estimation.” Bayesian Analysis.
Yegenoglu, Krajsek, Pier, et al. 2020. “Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-Performing Gradient Descent.” In Machine Learning, Optimization, and Data Science.
Zhang, Lin, Li, et al. 2018. “An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions.” Water Resources Research.