Commentary: On the Efficiency of Covariance Localisation of the Ensemble Kalman Filter Using Augmented Ensembles

Introduction

On page 12, Farchi and Bouquet [2] also state that “Such an extension had been discussed by Bishop et al. [3] but without numerical illustration.” This is incorrect. Lei et al. [7] used the GETKF to show that model space ensemble covariance localization provided satellite data assimilation (DA) performance comparable to 3DEnsVar.

To be specific about the forms of the modified gain matrix, let

where K is the total number of ensemble members in the ensemble forecast and where the n-vector is the ith member of the prior ensemble forecast and where the p-vector is the ith member of the prior ensemble forecast of the p-vector y of p observations. When p<K, the numerical cost of the pxp eigen decomposition

is less than the K × K eigen decomposition

In (2), E is a pxp eigenvector matrix for which , Γ_pxp is a pxp diagonal matrix of eigenvalues. In (3), C is a K × K orthonormal matrix of eigenvectors and Γ_KxK is a K × K diagonal matrix of eigenvalues. At least K-p of the eigenvalues in Γ_{K × K} will be equal to zero in the case of K>p. Equation's (2) and (3) are directly connected to the verbose singular value decomposition where where 0_px(K−p) is a px(K−p) matrix of zeros. However, since the columns of C associated with zero eigenvalues cannot contribute to products of the matrix with other vectors, it is more efficient to work with the concise svd given by where lists the p columns of having non-zero eigenvalues. Posselt and Bishop [4, 5] note that is given by

and hence can be computed without performing an eigen decomposition of the larger K × K matrix in (3). Posselt and Bishop [4, 5] prove that for a linear observation operator , if

(see [5], Equation A10) then

The analysis perturbations are given by , hence,

is the perturbation update equation implied by Posselt and Bishop [4, 5].

In the above notation, and when propagation of small amplitude ensemble perturbations by the non-linear model is replaced by the propagation of raw ensemble perturbations by the non-linear model (i.e., no tangent linear model approximation is made), Bocquet's Equation (25) [1] for the ensemble perturbation update takes the form,

A fundamental difference between (7) and (8) is that while Bouquet multiplies Z^f by the KxK matrix Posselt and Bishop multiply it by the Kxp matrix . When K>p, Posselt and Bishop's form only requires the eigenvector decomposition of a pxp matrix, whereas Bouquet's form requires the inversion of a larger K × K matrix. However, when p>K, the eigen decomposition (3) is cheaper than (2), becomes identical to the K × K matrix and becomes the concise svd of . In this case, E_pxK is efficiently given by and (7) becomes

Dividing Equation (9) by recovers Equation (24) of Bishop et al. [3].

The above shows that Bishop et al.'s Equation (24) [3] was not “rediscovered” from Bocquet's [1] form as implied by Farchi and Bocquet [2]. It is an extension of Posselt and Bishop's [4, 5] eigenvalue form to the case of K>p. Equation (9) is just an eigenvalue form of the modified gain matrix of Whitaker and Hamill's [8] Ensemble Square Root Filter.

Equivalence of (9) and (8)

Bocquet's Equation (25) [1] can be derived from (9) with the following steps:

• Drop the dimension subscripts and manipulate [I − (Γ + I)^−1/2]Γ⁻¹ as follows

• Use (10) in (9) to give

• But

• Using (12) and (3) in (11) gives

Equation (13) is equivalent to (8) and Bocquet's Equation (25) [1].

Numerical Issues, Condition Numbers, and Understanding

Numerical cancellation errors increase when the condition number of the matrix increases. Let us define the scalars and to, respectively, denote the maximum and minimum of the eigenvalues listed in the eigenvalue matrix Γ_pxp. The condition number of is . Because can be zero, can be infinite. In contrast, is bounded above by . However, note that the matrix has the eigenvalue decomposition

and hence has which is bounded above by where α is a positive scalar. Hence, α can be chosen to create a matrix that is better conditioned than . Once the eigen decomposition CΛC^T of (14) has been obtained, one obtains the eigenvalues required by the GETKF or ETKF using Γ = Λ − αI. Thus, condition number differences between the Bouquet and eigenvalue form are easily eliminated.

The eigenvalue form lends understanding to the performance of DA schemes in much the same way that Empirical Orthogonal Functions lend understanding to climate variability. Wang et al. [6] used this understanding to correct gross aspects of the eigenvalue overestimation that occurs when the size of the ensemble is much smaller than the rank of the true observation space forecast error covariance matrix.

Discussion

Similarly, Bishop et al. [3] overlooked Bocquet's [1] work because of (i) and (iii). This note serves to clarify the origins and uses of modified gain matrices used in ensemble DA.

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