# The ensemble Kalman filter and smoother part II

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## Outline

• The following topics will be covered in this lecture:
• The iterative ensemble Kalman smoother
• The single-iteration ensemble Kalman smoother

## Motivation

• In the last lecture, we saw how the ensemble Kalman filter (EnKF) can be used to propagate covariance estimates in the nonlinear model, while making the linear-Gaussian approximation at first order.

• Rather than analytically computing a large covariance matrix, and its evolution using the tangent-linear approximation, a nonlinear sample is drawn and a sample-based estimate is performed for the covariance.

• This sample-based covariance thus forms the background weights for the optimization of the nonlinear filtering cost function.

• This can be extended, like the smoothing problem in 4D-VAR, to a global analysis over a time series.

• Such an approach is what is often known as an ensemble-variational (EnVAR) technique, in which a 4D iterative optimization is made over an initial condition,
• but the weights and the estimate are constructed with the ensemble.
• This approach can be considered to thus extend 4D-VAR to include a time-varying background covariance using the ensemble-based estimates.

• Often, however, due to the small feasible ensemble size that can be simulated in the nonlinear model, the ensemble-based background weights are interpolated with a climatological covariance to regularize the problem.

## Hybrid EnVAR in the EnKF analysis

• The ensemble-variational approach is at the basis of the iterative ensemble Kalman filter / smoother (IEnKF/S).

• This technique seeks to perform an ensemble analysis like the square root ETKF by defining the ensemble estimates and the weight vector in the ensemble span

\begin{alignat}{2} & & {\color{#d95f02} {\widetilde{\mathcal{J}} (\pmb{w})} } &= {\color{#d95f02} {\frac{1}{2} \parallel \hat{\pmb{x}}_{0|L-S}^\mathrm{smth} - \mathbf{X}^\mathrm{smth}_{0|L-S} \pmb{w}- \hat{\pmb{x}}^\mathrm{smth}_{0|L-S} \parallel_{\mathbf{P}^\mathrm{smth}_{0|L-S}}^2} } + {\color{#7570b3} {\sum_{k=L-S+1}^L \frac{1}{2} \parallel \pmb{y}_k - \mathcal{H}_k\circ {\color{#1b9e77} { \mathcal{M}_{k:1}\left( {\color{#d95f02} { \hat{\pmb{x}}^\mathrm{smth}_{0|L-S} + \mathbf{X}^\mathrm{smth}_{0|L-S} \pmb{w} } } \right)}}\parallel_{\mathbf{R}_k}^2 } }\\ \Leftrightarrow & & {\color{#d95f02} {\widetilde{\mathcal{J}} (\pmb{w})} } &= {\color{#d95f02} { \frac{1}{2} \parallel \pmb{w}\parallel^2} } + {\color{#7570b3} {\sum_{k=L-S+1}^L \frac{1}{2} \parallel \pmb{y}_k - \mathcal{H}_k\circ {\color{#1b9e77} { \mathcal{M}_{k:1}\left( {\color{#d95f02} { \hat{\pmb{x}}^\mathrm{smth}_{0|L-S} + \mathbf{X}^\mathrm{smth}_{0|L-S} \pmb{w} } } \right)}}\parallel_{\mathbf{R}_k}^2 } } \end{alignat}

• One measures the cost as the discrepancy from the observations with the nonlinear evolution of the perturbation to the ensemble mean,

\begin{align} \hat{\pmb{x}}^\mathrm{smth}_{0|L-S} + \mathbf{X}^\mathrm{smth}_{0|L-S} \pmb{w} \end{align} combined with the size of the perturbation relative to the ensemble spread.

• The key is again, how the gradient is computed for the above cost function.

### Hybrid EnVAR in the EnKF analysis

• The gradient of the ensemble-based cost function is given by,

\begin{align} {\color{#d95f02} {\nabla_{\pmb{w}} \widetilde{\mathcal{J}} } }:= {\color{#d95f02} {\pmb{w}}} - {\color{#7570b3} {\sum_{k=L-S+1}^L \widetilde{\mathbf{Y}}_k^\top \mathbf{R}^{-1}_k\left[\pmb{y}_k - \mathcal{H}_k \circ {\color{#1b9e77} { \mathcal{M}_{k:1}\left({\color{#d95f02} {\hat{\pmb{x}}_{0|L-S}^\mathrm{smth} + \mathbf{X}_{0|L-S}^\mathrm{smth} \pmb{w}} }\right) } } \right]}}, \end{align}

• where $${\color{#7570b3} { \widetilde{\mathbf{Y}}_k } }$$ represents a directional derivative of the observation and state models,

\begin{align} {\color{#7570b3} { \widetilde{\mathbf{Y}}_k } }:= {\color{#d95f02} {\nabla\vert_{\hat{\pmb{x}}^\mathrm{smth}_{0|L-S}} } } {\color{#7570b3} {\left[\mathcal{H}_k \circ {\color{#1b9e77} {\mathcal{M}_{k:1} } } \right] } } {\color{#d95f02} {\mathbf{X}^\mathrm{smth}_{0|L-S}} }. \end{align}

• In order to avoid the construction of the tangent-linear and adjoint models, the “bundle” version makes an explicit approximation of finite differences with the ensemble

\begin{align} {\color{#7570b3} { \widetilde{\mathbf{Y}}_k } }\approx& {\color{#7570b3} { \frac{1}{\epsilon} \mathcal{H}_k \circ {\color{#1b9e77} {\mathcal{M}_{k:1} \left( {\color{#d95f02} { \pmb{x}_{0|L-S}^\mathrm{smth} \pmb{1}^\top + \epsilon \mathbf{X}_{0|L-S}^\mathrm{smth} } }\right) } } \left(\mathbf{I}_{N_e} - \pmb{1}\pmb{1}^\top / N_e \right)} }, \end{align} for a small constant $$\epsilon$$.

• The scheme produces an iterative estimate using a Gauss-Newton- or, e.g., Levenberg-Marquardt-based optimization.

• A similar scheme used more commonly in reservoir modeling is the ensemble randomized maximum likelihood estimator (EnRML).

## The single iteration ensemble transform Kalman smoother (SIEnKS)

• While accuracy increases with iterations in the 4D-MAP estimate, every iteration comes at the cost of the model forecast $${\color{#1b9e77} { \mathcal{M}_{L:1} } }$$.

• In synoptic meteorology the linear-Gaussian approximation of the evolution of the densities is actually an adequate approximation;

• iterating over the nonlinear dynamics may not be justified by the improvement in the forecast statistics.
• However, the iterative optimization over a nonlinear observation operator $$\mathcal{H}_k$$ or hyper-parameters in the filtering step of the classical EnKS can be run without the additional cost of model forecasts.

• This can be performed similarly to the IEnKS with the maximum likelihood ensemble filter (MELF) analysis.
• Subsequently, the retrospective analysis in the form of the filtering right-transform can be applied to condition the initial ensemble

\begin{align} \mathbf{E}^\mathrm{smth}_{0|L} = \mathbf{E}_{0:L-1}^\mathrm{smth} \boldsymbol{\Psi}_L \end{align}

• As with the 4D cost function, one can initialize the next DA cycle in terms of the retrospective analysis, and gain the benefit of the improved initial estimate.

• This scheme, is the single-iteration ensemble Kalman smoother (SIEnKS).

### The single iteration ensemble Kalman smoother (SIEnKS)

• Compared to the classical EnKS, this adds an outer loop to the filtering cycle to produce the posterior analysis.