# Multilevel Monte Carlo methods for ensemble variational data assimilation

Mayeul Destouches, Paul Mycek, Selime Gürol, Anthony Weaver, Serge Gratton, Ehouarn Simon

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Mayeul Destouches, Paul Mycek, Selime Gürol, Anthony Weaver, Serge Gratton, et al. Multilevel Monte Carlo methods for ensemble variational data assimilation. Nonlinear Processes in Geophysics, 2025, 32 (2), pp.167-187. 10.5194/npg-32-167-2025. hal-04869802

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# Multilevel Monte Carlo methods for ensemble variational data assimilation

Mayeul Destouches¹,²,³, Paul Mycek¹,², Selime Gürol¹,², Anthony T. Weaver¹,², Serge Gratton⁴, and Ehouarn Simon⁴

¹CERFACS, Toulouse, France

²CECI, Université de Toulouse, CERFACS/CNRS/IRD, Toulouse, France

³Met Office, Exeter, United Kingdom

⁴INPT-IRIT, Toulouse, France

**Correspondence:** Mayeul Destouches (mayeul destouches@umr-cnrm.fr)

Received: 20 November 2024 – Discussion started: 3 December 2024

Revised: 10 March 2025 – Accepted: 19 March 2025 – Published: 23 June 2025

**Abstract.** Ensemble variational data assimilation relies on ensembles of forecasts to estimate the background error covariance matrix **B**. The ensemble can be provided by an ensemble of data assimilations (EDA), which runs independent perturbed data assimilation and forecast steps. The accuracy of the ensemble estimator of **B** is strongly limited by the small ensemble size that is needed to keep the EDA computationally affordable. Here we investigate the potential of the multilevel Monte Carlo (MLMC) method, a type of multifidelity Monte Carlo method, to improve the accuracy of the standard Monte Carlo estimator of **B** while keeping the computational cost of ensemble generation comparable. MLMC exploits the availability of a range of discretization grids, thus shifting part of the computational work from the original assimilation grid to coarser ones. MLMC differs from the mere averaging of statistical estimators, as it ensures that no bias from the coarse-resolution grids is introduced in the estimation. The implications for ensemble variational data assimilation systems based on EDAs are discussed. Numerical experiments with a quasi-geostrophic model demonstrate the potential of the approach, as MLMC yields more accurate background error covariances and reduced analysis error. The challenges involved in cycling a multilevel variational data assimilation system are identified and discussed.

**Copyright statement.** The works published in this journal are distributed under the Creative Commons Attribution 4.0 License. This licence does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other. © Crown copyright 2024

## 1 Introduction

The covariance matrix of background errors plays a key role in variational data assimilation applications in meteorology and oceanography (Bannister, 2008a, b). Historically, these covariance matrices were modelled with strong assumptions of homogeneity and isotropy and with limited flow dependence (e.g. by parameterizing covariances in terms of the

background state). Developments over the last 20 years have enabled operational numerical weather prediction (NWP) centres to incorporate information from an ensemble of forecasts into parameterized background error covariance models (e.g. Raynaud et al., 2009, and Bonavita et al., 2011, for global atmospheric data assimilation; Brousseau et al., 2012, for regional atmospheric data assimilation; Chrust et al., 2025, for global ocean data assimilation). Moving away from parameterized covariance models, Lorenc (2003) and Buehner (2005) showed how a sample-based covariance matrix estimated directly from an ensemble of forecasts could be used in variational data assimilation. This method, inspired by the ensemble Kalman filter, consists of regularizing the standard Monte Carlo (MC) estimator of the covariance matrix by explicitly damping covariances at long separation
distances where the signal-to-noise ratio is small because of comparatively large sampling errors. This localized ensemble representation of the background error covariance matrix is used by many NWP centres, including the Met Office, the UK's national weather service (Clayton et al., 2013), Environment and Climate Change Canada (ECCC; Buehner et al., 2015; Caron et al., 2015), the National Centers for Environmental Prediction (NCEP; Kleist and Ide, 2015), and Météo-France (Montmerle et al., 2018).

Ensemble generation strategies can be broadly divided into two classes, whether the ensemble members come from an ensemble Kalman filter (or flavours thereof), as is done operationally at ECCC for instance (Buehner et al., 2015; Caron et al., 2022), or from an ensemble of (independent) data assimilations (EDA). We will focus on the latter in this paper. EDAs are used operationally to initialize ensemble forecasts and to provide background error information at Météo-France (Pereira and Berre, 2006), ECMWF (Bonavita et al., 2015), and the Met Office (Inverarity et al., 2023). The advantages of an EDA include reduced maintenance cost (compared to maintaining two different schemes for the ensemble and deterministic systems), improved simulation of the errors in the deterministic system, and its “embarrassingly parallel” nature.

Although ensemble estimates from an EDA can substantially improve the representation of background errors, this comes at the price of an increased computational cost, as a non-linear forecast and separate minimization (analysis) have to be performed for each ensemble member. Some research efforts have focused on reducing the cost of performing an ensemble of minimizations by using block-minimization methods (Mercier et al., 2019). A more typical way of reducing the combined cost of the ensemble forecast generation and assimilation steps is to use a cheaper data assimilation algorithm and coarser resolution compared to a deterministic or control forecast (Michel and Brousseau, 2021). The “Mean-Pert” method (Lorenc et al., 2017) gives a theoretical basis to this approach and has been used operationally at the Met Office since December 2019 (Inverarity et al., 2023). In this article, the focus is mostly on maintaining an affordable ensemble generation cost.

In this paper, we explore a method to improve the estimation of background error covariances in a way that is not so different from the dual-resolution strategy mentioned above, i.e. by increasing the ensemble size without increasing the cost of generating the ensemble. Instead of generating an ensemble on a single coarse grid, we generate a set of ensembles on grids with different levels of coarsening. The multilevel Monte Carlo (MLMC) framework ensures that no bias is introduced in the resulting estimates. MLMC was first introduced by Heinrich (2000) and popularized by Giles (2008). A review is provided in Giles (2015). In practice, the MLMC estimator is built as a base MC estimator on a coarse grid but is iteratively refined by adding correction terms from

the difference of pairs of MC estimators from finer and finer grids.

The idea of applying MLMC methods to data assimilation is not new. Several studies have examined MLMC in the context of particle filters (e.g. Gregory et al., 2016; Jasra et al., 2017) and ensemble Kalman filters (e.g. Hoel et al., 2016; Chernov et al., 2021). However, they often focus on the estimation of a scalar quantity or on the asymptotic properties in the limit of continuous space or time discretizations. Here, we focus on the use of MLMC methods for variational data assimilation. In this regard, the natural extension of ensemble variational (EnVar) systems is to use an MLMC estimator of the background error covariance matrix.

MLMC methods have also been used to estimate variances and covariances. Multilevel estimation of the variance field was analysed by Bierig and Chernov (2015) (but used as early as 2010; see references therein). Mycek and de Lozzo (2019) proposed a multilevel estimator for scalar covariances, with a focus on the optimal allocation of members across grids. In the context of the EnKF, a multilevel estimator of the covariance matrix was proposed by Hoel et al. (2016). Most interestingly, Maurais et al. (2023, 2025) have recently proposed a multilevel estimator that can be used to ensure positive-semi-definite covariance matrix estimates.

The main contribution of this paper is to explore the use of MLMC-like methods for variational data assimilation, which, to the best of our knowledge, has never been done before. In doing so, we impose constraints that are relevant for data assimilation in operational applications. For instance, we assume that the finest grid resolution of the data assimilation is fixed so that only coarser grids can be added. As such, we do not delve into asymptotic considerations in the limit of infinitely many refined grids. As another example, we discard algorithms whose cost does not scale linearly or quasi-linearly with respect to the size of the state to estimate. For instance, our proposed algorithms do not involve explicit storage of the background error covariance matrix **B** or computing its eigen-decomposition. This concern for operational applications makes the spirit of this article similar to that of Beiser et al. (2025), who investigated the practical details of using MLMC with an EnKF in an oceanographic application.

The outline of the paper is as follows. We recall the properties of the standard single-level MC ensemble estimator of **B** in Sect. 2, before explaining in Sect. 3 how they extend to multilevel estimators. The setting for numerical experiments is presented in Sect. 4. Results are presented and discussed in Sect. 5 before the conclusion in Sect. 6.

## 2 The Monte Carlo estimator of **B**

This section recalls some statistical properties of the ensemble background error covariances classically used in ensemble variational data assimilation. The notations have been
chosen to facilitate the extension to the multilevel setting in the next section.

Let $\epsilon : \Omega \to \mathbb{R}^p$ be a random vector representing stochastic model inputs, where $\Omega$ is the sample space. For instance, $\epsilon$ could contain uncertainty information on initial conditions, boundary conditions, and model parameters. Let $f: \mathbb{R}^p \to \mathbb{R}^n$ be a forecast model generating ensemble members $\mathbf{X}: \Omega \to \mathbb{R}^n$ from the stochastic inputs as $\mathbf{X} = f \circ \epsilon$, where $\mathbf{X}$ follows the probability distribution of the background state, $n$ is the dimension of the state space, $p$ is the dimension of the space of uncertain input parameters, and $\circ$ is the function composition operator. Our goal is to estimate the covariance matrix of $\mathbf{X}$, $\mathbf{B} := \text{Cov}[\mathbf{X}, \mathbf{X}]$. We assume for the rest of the paper that all random vectors have finite fourth-order moments and thus finite second-order moments. Let $\mathcal{E} := (\epsilon_1, \dots, \epsilon_N)$ denote an ensemble of $N$ independent and identically distributed stochastic inputs, with $N \ge 2$. The standard $N$-member unbiased MC estimator of $\mathbf{B}$ can be built from the ensemble of $N$ forecast members associated with these inputs, $f(\mathcal{E}) := (f(\epsilon_1), \dots, f(\epsilon_N)) = (\mathbf{X}_1, \dots, \mathbf{X}_N)$.

$$ \hat{\mathbf{B}} = \frac{1}{N-1} \sum_{i=1}^{N} (\mathbf{X}_i - \bar{\mathbf{X}})(\mathbf{X}_i - \bar{\mathbf{X}})^{\mathrm{T}}, \quad (1) $$

where $\bar{\mathbf{X}} := 1/N \sum_i \mathbf{X}_i$ denotes the ensemble mean.

## 2.1 Sampling noise

The MC estimator $\hat{\mathbf{B}}$ is built from random vectors, so it is itself a random quantity. Each element $\hat{B}_{ij}$ of the MC estimator of $\mathbf{B}$ is an estimator of $B_{ij}$. As for any statistical estimator, we can define its mean square error (MSE), which can be decomposed as a variance and a squared bias term:

$$ \begin{aligned} \text{MSE}[\hat{B}_{ij}, B_{ij}] &:= \mathbb{E}[(\hat{B}_{ij} - B_{ij})^2] & (2) \\ &= \mathbb{V}[\hat{B}_{ij}] + (\mathbb{E}[\hat{B}_{ij} - B_{ij}])^2. & (3) \end{aligned} $$

This bias-variance decomposition is somewhat artificial here since the MC estimator of the covariance is unbiased. As a consequence, the MSE of the estimator reduces to its variance:

$$ \text{MSE}[\hat{B}_{ij}, B_{ij}] = \mathbb{V}[\hat{B}_{ij}]. \quad (4) $$

This variance is a measure of the amplitude of the sampling noise affecting the MC estimator and is not to be mistaken for the variance of $\mathbf{X}$, which is the diagonal of $\mathbf{B}$. Rather, it is the variance of the covariance estimator, which can be expressed as a function of ensemble size and the statistical moments of $\mathbf{X}$ (see Mycek and de Lozzo, 2019, Eq. 11):

$$ \mathbb{V}[\hat{B}_{ij}] = \frac{1}{N} \mathbb{M}_{ijj}^{(4)} - \frac{N-2}{N(N-1)} B_{ij}^2 + \frac{1}{N(N-1)} B_{ii} B_{jj}, \quad (5) $$

where $\mathbb{M}^{(4)}$ is the tensor of fourth-order central moments of $\mathbf{X}$,

$$ \mathbb{M}_{ijkl}^{(4)} := \mathbb{E}[(X_i - \mathbb{E}[X_i])(X_j - \mathbb{E}[X_j]) \times (X_k - \mathbb{E}[X_k])(X_l - \mathbb{E}[X_l])] \quad (6) $$

Note that Eq. (5) is valid independently of the distribution of the ensemble members $\mathbf{X}$. We do not assume the members to be normally distributed for instance. The MSE of the covariance estimator behaves asymptotically as $(\mathbb{M}_{ijj}^{(4)} - B_{ij}^2)/N$ as $N \to \infty$. The slow decay rate $\mathcal{O}(N^{-1/2})$ of the root mean square error (RMSE) is a well-known property of MC estimators. In practice, this means that to reduce the sampling noise by half for instance, the ensemble size needs to be increased by a factor of 4.

## 2.2 Total MSE and total variance

A natural choice to define the total MSE of the full covariance matrix estimator is to use the Frobenius norm $\|\mathbf{A}\|_F^2 := \sum_i \sum_j A_{ij}^2$. With this choice of norm, the total MSE is just the sum of the matrix element MSEs, and a simple bias-variance decomposition follows:

$$ \text{MSE}[\hat{\mathbf{B}}, \mathbf{B}] := \mathbb{E}[\|\hat{\mathbf{B}} - \mathbf{B}\|_F^2] \quad (7) $$

$$ = \mathbb{E} \left[ \sum_{i=1}^{n} \sum_{j=1}^{n} (\hat{B}_{ij} - B_{ij})^2 \right] \quad (8) $$

$$ = \sum_{i=1}^{n} \sum_{j=1}^{n} \mathbb{V}[\hat{B}_{ij}] + (\mathbb{E}[\hat{B}_{ij} - B_{ij}])^2 \quad (9) $$

$$ = \mathbb{V}[\hat{\mathbf{B}}]. \quad (10) $$

The bias term vanishes in Eq. (9), and $\mathbb{V}$ denotes the total variance, defined as the sum of the element variances:

$$ \mathbb{V}[\hat{\mathbf{B}}] := \sum_{i=1}^{n} \sum_{j=1}^{n} \mathbb{V}[\hat{B}_{ij}] \quad (11) $$

$$ = \sum_{i=1}^{n} \sum_{j=1}^{n} \left[ \frac{1}{N} \mathbb{M}_{ijj}^{(4)} - \frac{N-2}{N(N-1)} B_{ij}^2 + \frac{1}{N(N-1)} B_{ii} B_{jj} \right], \quad (12) $$

where we have used Eq. (5).

## 2.3 Impact of localization

In practice, the MC covariance matrix estimate is never used as such. In data assimilation applications, the ensemble covariance matrix is regularized by localization (Hamill et al., 2001; Lorenc, 2003), i.e.

$$ \hat{\mathbf{B}}_{\text{loc}} = \hat{\mathbf{B}} \odot \mathbf{L}, \quad (13) $$

where the localization matrix $\mathbf{L} \in \mathbb{R}^{n \times n}$ is a correlation matrix, i.e. a symmetric positive-definite matrix with unit diagonal, and $\odot$ denotes the Schur (element-wise) product. Localization is designed to remove long-distance sampling noise,
and doing so increases the rank of the covariance matrix estimates. The localization matrix, which has elements between 0 and 1, introduces bias in the estimator $\hat{\mathbf{B}}_{\text{loc}}$. This additional bias may be compensated for by an associated reduction of the variance. For any element $ij$, we have

$$ \mathbb{E}[\hat{\mathbf{B}}_{\text{loc},ij} - B_{ij}] = \mathbb{E}[\hat{\mathbf{B}}_{ij} L_{ij} - B_{ij}] \\ = \mathbb{E}[\hat{\mathbf{B}}_{ij}] L_{ij} - B_{ij} = B_{ij}(L_{ij} - 1), \quad (14) $$

$$ \mathbb{V}[\hat{\mathbf{B}}_{\text{loc},ij}] = \mathbb{V}[\hat{\mathbf{B}}_{ij} L_{ij}] = L_{ij}^2 \mathbb{V}[\hat{\mathbf{B}}_{ij}]. \quad (15) $$

Ménétrier et al. (2015a, b) proposed a framework for optimal localization based on minimizing the MSE of the localized estimator of $\hat{\mathbf{B}}_{\text{loc}}$.

In the next section, we focus on non-localized and unbiased estimators of $\mathbf{B}$. As a technical (though vital) tool, the interaction with localization will be detailed later, along with other technical considerations, in the numerical experiments of Sect. 5.

## 3 The multilevel B

In this section, we focus on using MLMC techniques (Heinrich, 2000; Giles, 2008, 2015) to build a multilevel $\mathbf{B}$ in order to reduce the total variance of the MC estimator without introducing any additional bias. The MC estimator can be seen as a specific case of a multilevel estimator with only one level. In Sect. 3.1 we first introduce the multilevel estimation of a covariance matrix. In Sect. 3.2 we derive the variance of this estimator and show how it can be minimized by an optimal allocation of the ensemble members on the different fidelity levels. Finally, a weighted extension of the classical multilevel estimator is introduced in Sect. 3.3.

### 3.1 Multilevel estimation of a covariance matrix

Contrary to simple MC estimators which are built from an ensemble of simulations of equal fidelity, MLMC estimators are built from a set of ensembles of simulations of different fidelity. In the multilevel approach, we introduce a hierarchy of models of increasing fidelities $f_1, \dots, f_L$, with $L \ge 2$ and $f_L = f$. We assume that the models are ordered from the least (fidelity level $\ell = 1$) to the most accurate (fidelity level $\ell = L$) and from the computationally cheapest ($\ell = 1$) to the most computationally expensive ($\ell = L$). These models have the same input and output spaces as $f$; i.e. $f_\ell : \mathbb{R}^p \to \mathbb{R}^n$. In practice, $f_1, \dots, f_{L-1}$ is typically obtained by using intermediate coarser discretization grids. This implies that a prolongation operator is required to ensure that the output of each model is on the same fine grid in $\mathbb{R}^n$. The choice of interpolation operators will be discussed further in Sect. 4 and has been discussed in a similar context by Briant et al. (2023). The varying fidelity can originate from sources other than grid discretization, for instance from computations in simple or half precision, simplified dynamical cores, or surrogate

<table><caption>**Table 1.** Example coupling structure for a three-level MLMC estimator. Each row corresponds to a different coupling group.</caption><thead><tr><th>Stochastic input set k</th><th>Fidelity level ℓ</th><th>Ensemble sizes</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>N<sup>(1)</sup></td></tr><tr><td>2</td><td>1, 2</td><td>N<sup>(2)</sup> each, 2N<sup>(2)</sup> in total</td></tr><tr><td>3</td><td>2, 3</td><td>N<sup>(3)</sup> each, 2N<sup>(3)</sup> in total</td></tr></tbody></table>

models. Although this is not the focus of this article, note that multilevel techniques exist that do not require the fidelity levels to have a natural ordering by accuracy and computational cost (see Sect. 3.3).

For each $1 \le k \le L$, we define a set of stochastic inputs with $N^{(k)}$ members in $\Omega \to \mathbb{R}^p$,

$$ \mathcal{E}^{(k)} = \{ \epsilon_1^{(k)}, \dots, \epsilon_{N^{(k)}}^{(k)} \}. \quad (16) $$

There are thus $\sum_{k=1}^L N^{(k)}$ stochastic inputs in total, all independent and identically distributed. Technically, each “stochastic input set” $\mathcal{E}^{(k)}$ could be given as input to any of the simulators $f_\ell$, thus yielding an output ensemble $\mathcal{X}_\ell^{(k)} := f_\ell(\mathcal{E}^{(k)})$. If the same stochastic input set $\mathcal{E}^{(k)}$ is given to several simulators, several output ensembles are produced, all of size $N^{(k)}$ and all with their own fidelity level $\ell$ but all generated from the same stochastic inputs. As such, these ensembles would not be mutually independent, with one-to-one correlations between members across fidelity levels. We refer to such ensembles as a “stochastically coupled ensemble”.

To build an MLMC estimator, we generate several groups of stochastically coupled ensembles, one group for each of the $L$ stochastic input sets. A base group is defined using the lowest fidelity level only (a group with one output ensemble only) and coupling groups are defined from pairs of successive fidelity levels (groups with two stochastically coupled ensembles each).

Base group with one ensemble :

$$ \mathcal{X}_\ell^{(1)} = f_\ell(\mathcal{E}^{(1)}), \quad \ell = 1 \quad (17) $$

Coupling group with stochastically coupled ensemble:

$$ \text{pair } (\mathcal{X}_\ell^{(k)}, \mathcal{X}_\ell^{(k)}), \ell = k, \ell' = k-1, 2 \le k \le L \quad (18) $$

The subscripts here refer to the fidelity level $\ell$, while the superscripts refer to the stochastic input set $k$. The coupled ensembles $\mathcal{X}_{k-1}^{(k)}$ and $\mathcal{X}_k^{(k)}$ are not independent as they use the same stochastic inputs and are thus stochastically coupled. Conversely, ensembles with different superscripts are independent as they are generated from different stochastic input sets. As an example, the coupling structure of an MLMC with three fidelity levels is shown in Table 1.

Denoting by $\hat{\mathbf{B}}_\ell^{(k)}$ the unbiased MC covariance estimator built from ensemble $\mathcal{X}_\ell^{(k)}$, the MLMC covariance estimator
is built from a base estimator on the lowest fidelity level, successively corrected by correction terms:

$$ \hat{\mathbf{B}}^{\text{ML}} = \hat{\mathbf{B}}_{1}^{(1)} + \sum_{k=2}^{L} (\hat{\mathbf{B}}_{k}^{(k)} - \hat{\mathbf{B}}_{k-1}^{(k)}). \quad (19) $$

There are a few important points to note about this estimator. First, building the ensembles requires $N^{(L)}$ integrations of the high-fidelity model $f_L = f$ and $N^{(\ell)} + N^{(\ell+1)}$ integrations of $f_\ell$ for $1 \le \ell \le L-1$. Compared to an MC estimator, this means that some computational budget must be moved from the high-fidelity level to the coarser ones to conserve the same computational cost. In practice, the low-fidelity models are much cheaper to run, which allows them to be run with large ensembles even with a fraction of the total computational resources. Second, this estimator is unbiased, as the expectations are not dependent on the stochastic input index and cancel each other out in a telescopic fashion.

$$ \mathbb{E}[\hat{\mathbf{B}}^{\text{ML}}] = \mathbb{E}[\hat{\mathbf{B}}_{1}^{(1)}] + \sum_{k=2}^{L} (\mathbb{E}[\hat{\mathbf{B}}_{k}^{(k)}] - \mathbb{E}[\hat{\mathbf{B}}_{k-1}^{(k)}]) \quad (20) $$

$$ = \mathbf{B}_1 + \sum_{k=2}^{L} (\mathbf{B}_k - \mathbf{B}_{k-1}) \quad (21) $$

$$ (\text{where } \mathbf{B}_{\ell} := \text{Cov}[f_{\ell}(\boldsymbol{\epsilon}), f_{\ell}(\boldsymbol{\epsilon})]) \quad (21) $$

$$ = \mathbf{B}_L = \mathbf{B} \quad (22) $$

where we used the fact that although $\hat{\mathbf{B}}_{k-1}^{(k-1)} \neq \hat{\mathbf{B}}_{k-1}^{(k)}$, they have the same expectation $\mathbf{B}_{k-1}$. Third, it is not obvious under which conditions this multilevel estimator is more accurate than the standard MC one. This is discussed in Sect. 3.2.

Finally, multilevel estimators are not range-preserving. If random samples lie in a given interval, an MC estimate of their mean will also lie in this interval, but an MLMC estimate may lie outside the interval. This concept generalizes to covariance matrix estimators, with multilevel estimates being symmetric matrices with no guarantee of positive semi-definiteness. This is discussed further in Sect. 5.

## 3.2 Error reduction and optimal ensemble sizes

There are various ways to split a given computational budget into the different coupling groups. How the computational budget is allocated determines the respective ensemble sizes $N^{(k)}$, which in turn determine the MSE of the multilevel covariance estimator. We will explain in this section how the member allocation (i.e. choice of the ensemble sizes) can be optimized to minimize the statistical error (sampling noise) of the covariance matrix estimator. The approach we follow here is similar to what can be found in the literature, for instance in Mycek and de Lozzo (2019) for the multilevel estimation of a scalar covariance. The results in this section, taken from Destouches et al. (2023), differ from previously published results in two respects: (a) we extend the problem

from the scalar multilevel covariance estimator to the multi-level covariance matrix estimator, in a setting where the finest level $L$ is fixed, and (b) we ensure that the problem is kept numerically feasible for high-dimensional systems.

### 3.2.1 Minimizing an upper bound of the multilevel MSE

First, let us compute the MSE of the covariance matrix estimator. Since the multilevel covariance estimator is unbiased, its total MSE is equal to its total variance. The mutual independence of correction terms allows us to write the total variance as

$$ \mathcal{V}[\hat{\mathbf{B}}^{\text{ML}}] = \mathcal{V}[\hat{\mathbf{B}}_{1}^{(1)}] + \sum_{k=2}^{L} \mathcal{V}[\hat{\mathbf{B}}_{k}^{(k)} - \hat{\mathbf{B}}_{k-1}^{(k)}]. \quad (23) $$

As shown by Mycek and de Lozzo (2019, Eq. 2.31) for a scalar covariance estimator, the scalar variance of each scalar multilevel covariance estimator $\hat{\mathbf{B}}_{ij}^{\text{ML}}$ can be bounded by a sum over the coupling groups $k$ of terms inversely proportional to $N^{(k)} - 1$. By summing these terms over all covariances $\hat{\mathbf{B}}_{ij}^{\text{ML}}$ in $\hat{\mathbf{B}}^{\text{ML}}$, we see the same result holds for the total variance of the covariance matrix estimator:

$$ \mathcal{V}[\hat{\mathbf{B}}^{\text{ML}}] \le \sum_{k=1}^{L} \frac{\mathcal{V}^{(k)}}{N^{(k)} - 1}, \quad (24) $$

where $\mathcal{V}^{(k)}$ represents constants independent of the ensemble sizes. These constants can be expressed as functions of central moments of $f_\ell(\boldsymbol{\epsilon})$. Introducing $X_\ell := f_\ell(\boldsymbol{\epsilon})$ and $X_{\ell,i}$, its $i$th element, and defining $X_{0,i} = 0$ by convention for all $i$ values, we have, for $k \ge 1$,

$$ \mathcal{V}^{(k)} := \sum_{1 \le i, j \le n} \frac{1}{2} \left( \sqrt{\mathbb{M}^4[X_{k,i} - X_{k-1,i}] \mathbb{M}^4[X_{k,j} + X_{k-1,j}]} + \sqrt{\mathbb{M}^4[X_{k,j} - X_{k-1,j}] \mathbb{M}^4[X_{k,i} + X_{k-1,i}]} \right), \quad (25) $$

where $\mathbb{M}^4$ is the fourth-order central statistical moment: $\mathbb{M}^4[u, v, x, y] := \mathbb{E}[(u - \mathbb{E}[u])(v - \mathbb{E}[v])(x - \mathbb{E}[x])(y - \mathbb{E}[y])]$ for scalar, real-valued random variables $u, v, x$, and $y$, and $\mathbb{M}^4[u] := \mathbb{M}^4[u, u, u, u]$.

Although we can derive an exact expression of the total variance (see Sect. 3.2.2 below), Eq. (24) is helpful to understand why the multilevel approach can be effective. Intuitively, the upper bound will be small if either the ensemble sizes $N^{(k)}$ are large or the constants $\mathcal{V}^{(k)}$ are small. The first condition can be met for small $k$ if the low-fidelity simulations are substantially cheaper to run than the high-fidelity ones, thus allowing large ensemble sizes for low-fidelity ensembles. For higher-fidelity ensembles, the ensemble sizes are necessarily smaller due to higher computational cost. This can be compensated for by smaller $\mathcal{V}^{(k)}$ constants, which can be related to small fourth-order moments of the correction terms and therefore to strong correlations between
stochastically coupled simulations. However, building computationally cheaper low-fidelity models that are strongly correlated with the highest-fidelity model can be technically challenging.

### 3.2.2 Directly minimizing the multilevel MSE

The exact total variance can be computed by expanding Eq. (23):

$$ \nu[\hat{\mathbf{B}}^{\text{ML}}] = \sum_{1 \le i, j \le n} \left\{ \mathbb{V}[\hat{\mathbf{B}}_{1,ij}^{(1)}] + \sum_{k=2}^{L} \left( \mathbb{V}[\hat{\mathbf{B}}_{k,ij}^{(k)}] + \mathbb{V}[\hat{\mathbf{B}}_{k-1,ij}^{(k)}] \right. \right. \\ \left. \left. - 2\text{Cov}[\hat{\mathbf{B}}_{k,ij}^{(k)}, \hat{\mathbf{B}}_{k-1,ij}^{(k)}] \right) \right\}. \quad (26) $$

All the terms in this expression can be expressed as functions of the ensemble sizes $N^{(k)}$ and central moments of $X_\ell$. We can derive the following expression for the covariance of scalar covariance estimators (Ménétrier et al., 2015a, their Eq. 9):

$$ \text{Cov}[\hat{\mathbf{B}}_{\ell,ij}^{(k)}, \hat{\mathbf{B}}_{\ell',ij}^{(k)}] = \frac{\mathbb{M}^4[X_{\ell,i}, X_{\ell',i}, X_{\ell,j}, X_{\ell',j}] - \text{Cov}[X_{\ell,i}, X_{\ell,j}]\text{Cov}[X_{\ell',i}, X_{\ell',j}]}{N^{(k)}} \\ + \frac{\text{Cov}[X_{\ell,i}, X_{\ell',j}]\text{Cov}[X_{\ell,j}, X_{\ell',i}]}{N^{(k)}(N^{(k)} - 1)} \\ + \frac{\text{Cov}[X_{\ell,i}, X_{\ell',i}]\text{Cov}[X_{\ell,j}, X_{\ell',j}]}{N^{(k)}(N^{(k)} - 1)}. \quad (27) $$

For the case $\ell = \ell'$, this simplifies to an expression for the variance:

$$ \mathbb{V}[\hat{\mathbf{B}}_{\ell,ij}^{(k)}] = \frac{\mathbb{M}^4[X_{\ell,i}, X_{\ell,i}, X_{\ell,j}, X_{\ell,j}] - \text{Cov}[X_{\ell,i}, X_{\ell,j}]^2}{N^{(k)}} \\ + \frac{\text{Cov}[X_{\ell,i}, X_{\ell,j}]^2 + \mathbb{V}[X_{\ell,i}]\mathbb{V}[X_{\ell,j}]}{N^{(k)}(N^{(k)} - 1)}. \quad (28) $$

Gathering Eqs. (26)–(28) gives a theoretical expression of the total variance of the multilevel estimator as

$$ \nu[\hat{\mathbf{B}}^{\text{ML}}] = \sum_{k=1}^{L} \frac{a^{(k)}}{N^{(k)}} + \frac{b^{(k)}}{N^{(k)}(N^{(k)} - 1)}, \quad (29) $$

for some deterministic scalar parameters $a^{(k)}$ and $b^{(k)}$, $1 \le k \le L$, which are independent of the ensemble sizes $N^{(k)}$ and given by

$$ \begin{aligned} a^{(k)} = & \sum_{1 \le i, j \le n} \left( \mathbb{M}^4[X_{k,i}, X_{k,i}, X_{k,j}, X_{k,j}] \right. \\ & - \text{Cov}[X_{k,i}, X_{k,j}]^2 \\ & + \mathbb{M}^4[X_{k-1,i}, X_{k-1,i}, X_{k-1,j}, X_{k-1,j}] \\ & - \text{Cov}[X_{k-1,i}, X_{k-1,j}]^2 \\ & - 2\mathbb{M}^4[X_{k,i}, X_{k-1,i}, X_{k,j}, X_{k-1,j}] \\ & + 2\text{Cov}[X_{k,i}, X_{k,j}]\text{Cov}[X_{k-1,i}, X_{k-1,j}] \\ & \left. \right), \end{aligned} \quad (30) $$

$$ \begin{aligned} b^{(k)} = & \sum_{1 \le i, j \le n} \left( \text{Cov}[X_{k,i}, X_{k,j}]^2 \right. \\ & + \mathbb{V}[X_{k,i}]\mathbb{V}[X_{k,j}] + \text{Cov}[X_{k-1,i}, X_{k-1,j}]^2 \\ & + \mathbb{V}[X_{k-1,i}]\mathbb{V}[X_{k-1,j}] \\ & - 2\text{Cov}[X_{k,i}, X_{k-1,j}]\text{Cov}[X_{k,j}, X_{k-1,i}] \\ & - 2\text{Cov}[X_{k,i}, X_{k-1,i}]\text{Cov}[X_{k,j}, X_{k-1,j}] \\ & \left. \right), \end{aligned} \quad (31) $$

where we define by convention $X_{0,i} = 0$ for all $i$ values. These expressions involve double sums over the $n$ elements of $X_\ell$, i.e. over the space dimension. For operational systems of typical size $\mathcal{O}(10^6) - \mathcal{O}(10^9)$, these summations involve $\mathcal{O}(10^{12}) - \mathcal{O}(10^{18})$ elements. Evaluating these summations is computationally prohibitive. Fortunately, when estimating the statistical moments in Eqs. (27) and (28) using ensemble estimates, sums can be rearranged to replace the double sums over space by a single sum over space, at the expense of a double sum over the ensemble members. More details on this are given in Destouches et al. (2023, Appendix B). Note that the standard ensemble estimators of $a^{(k)}$ and $b^{(k)}$ are biased. These “standard estimators” are obtained by replacing the central moments in Eqs. (30) and (31) with the associated sample central moments about the average. The resulting estimators could be made unbiased by extending the approach of Shivanand (2025), but we have preferred using the biased versions in our numerical experiments for the sake of simplicity.

Obtaining accurate ensemble estimates of fourth-order moments in Eqs. (27) and (28) could also be difficult with small ensemble sizes. We do not expect this to be a problem in practice, however, as the sampling noise in fourth-order estimates is attenuated by the global space averaging on indexes $i$ and $j$.

Given that the total variance of the multilevel covariance matrix estimator is explicitly derived in Eq. (29), the optimal member allocation for a given computational budget $\eta$ can be determined by minimizing the total variance under a cost constraint:
$$ \min_{N^{(k)}, 1 \le k \le L} \sum_{k=1}^{L} \frac{a^{(k)}}{N^{(k)}} + \frac{b^{(k)}}{N^{(k)}(N^{(k)} - 1)} \quad \text{subject to} \\ \sum_{k=1}^{L} (C_{\ell} + C_{\ell-1}) N^{(k)} = \eta \quad \text{and} \quad N^{(k)} \ge 2 \quad (1 \le k \le N), \quad (32) $$

where $C_\ell$ is the computational cost of generating one member on fidelity level $\ell$, and $C_0 = 0$ by convention. This is a low-dimensional optimization problem, with two to five unknowns in practice, which can be solved numerically. The solution of the problem gives ensemble sizes in floating points, which can then be rounded to the closest integers, for instance. Only once this problem has been solved can the optimum variance be compared to the variance of the same cost MC estimator, obtained by choosing $N$ as $\eta/C_L$ in Eq. (12).

## 3.3 Weighted multilevel estimation

Schaden and Ullmann (2020) present an important generalization of the MLMC framework. In their paper, the authors introduce the multilevel best linear unbiased estimator (MLBLUE), a unifying framework that generalizes and outperforms the MLMC estimator as presented here. Although their work focuses on the estimation of a scalar expectation, it can be extended to the estimation of a covariance matrix, as shown by Destouches et al. (2023).

In an MLMC framework, the fidelity levels are ordered from low to high fidelity, and successive fidelity levels are paired into a common coupling group receiving the same stochastic inputs. Each coupling group forms a correction term in Eq. (19). The MLBLUE does not make such assumptions: the fidelity levels do not need to be hierarchized, and a coupling group can include any number of levels. This is relevant, for example, when using non-hierarchized surrogate models (e.g. El Amri et al., 2023). As a consequence, the correction terms are no longer restricted to differences of MC estimators but can be any weighted average of them, with coupling weights differing from the MLMC weights $(1, -1)$.

In the present paper, we do have hierarchized fidelity levels. As such, we choose to consider the MLBLUE only in the context of simpler MLMC-like coupling structures. Two main reasons support this choice.

1. For a given set of $L$ fidelity levels, the MLMC structure ensures that the number $(2L - 1)$ of different ensembles is limited, while it could have been larger for more complex structures. In a context where the total computational budget is expected to be small (typically around 10 fine-grid simulations), keeping the number of ensembles small ensures each ensemble can be populated with a reasonable number of members.

2. Whichever coupling structure is chosen, it has to be kept unchanged for all data assimilation cycles. Since the optimal MLBLUE coupling structure is highly dependent

on the problem under consideration, a complex coupling structure that is optimal for one cycle may not be optimal for the other cycles. This motivates the choice of a simple and robust coupling structure.

Within this MLMC-like structure, the MLBLUE still provides an improvement over MLMC due to the introduction of coupling weights. This yields a weighted MLMC, as already proposed by Šukys et al. (2017). For a covariance matrix, a simplified weighted estimator with scalar weights reads

$$ \hat{\mathbf{B}}^{\text{wML}} = \beta_{1}^{(1)} \hat{\mathbf{B}}_{1}^{(1)} + \sum_{k=2}^{L} \left( \beta_{k}^{(k)} \hat{\mathbf{B}}_{k}^{(k)} + \beta_{k-1}^{(k)} \hat{\mathbf{B}}_{k-1}^{(k)} \right), \quad (33) $$

where the $\beta_\ell^{(k)}$ represents scalar weights, with $\beta_L^{(L)} = 1$ and $\beta_k^{(k+1)} = -\beta_k^{(k)}$ $(1 \le k \le L - 1)$ to ensure the estimator is unbiased. The variance of this estimator now depends on both the ensemble sizes $N^{(k)}$ and the weights $\beta_\ell^{(k)}$. However, the optimal weights can be derived for arbitrary ensemble sizes so that the variance minimization problem can be expressed in terms of ensemble sizes only (Schaden and Ullmann, 2020).

For the simple weighted multilevel covariance matrix estimator proposed above, the ensemble member allocation problem is similar to the MLMC one (Destouches et al., 2023, translating their Eq. 124 to our notations):

$$ \min_{N^{(k)}, 1 \le k \le N} \sum_{k=1}^{L} \left( \beta_{k-1}^{(k)*} \quad \beta_k^{(k)*} \right) \mathbf{C}^{(k)} \begin{pmatrix} \beta_{k-1}^{(k)*} \\ \beta_k^{(k)*} \end{pmatrix} \quad \text{subject to} \\ \sum_{k=1}^{L} (C_\ell + C_{\ell-1}) N^{(k)} = \eta \quad \text{and} \quad N^{(k)} \ge 2 \quad (1 \le k \le N), \quad (34) $$

where

- $\mathbf{C}^{(k)}$ is the matrix of spatially averaged covariances between MC covariance estimators at successive fidelity levels,

$$ \mathbf{C}^{(k)} = \frac{1}{n^2} \sum_{1 \le i, j \le n} \begin{pmatrix} \mathbb{V}[\hat{\mathbf{B}}_{k-1,ij}] & \text{Cov}[\hat{\mathbf{B}}_{k-1,ij}, \hat{\mathbf{B}}_{k,ij}] \\ \text{Cov}[\hat{\mathbf{B}}_{k-1,ij}, \hat{\mathbf{B}}_{k,ij}] & \mathbb{V}[\hat{\mathbf{B}}_{k,ij}] \end{pmatrix}, \quad (35) $$

- the quantities indexed by $k = 0$ are zero by convention;

- $\beta_\ell^{(k)*}$ represents the optimal weights associated with the ensemble sizes $N^{(k)}$. Their expression is given in Appendix A.

In the minimization problem Eq. (34), the ensemble sizes $N^{(k)}$ appear explicitly in the constraint, but they are only implicitly present in the optimal weights $\beta_\ell^{(k)*}$ (see Appendix A) and in the inter-level covariances (see Eq. 27). The summed inter-level covariance matrices $\mathbf{C}^{(k)}$ have to be estimated in
a pre-processing step. This estimation can be made with a cost that is linear in $n$ (see Appendix B of Destouches et al., 2023). Since there is almost no cost overhead in using weighted MLMC over MLMC, the weighted MLMC (wMLMC hereafter) should always be preferred in practice.

# 4 Experimental setting

## 4.1 Presentation of the idealized model

The performance of the covariance estimators is tested with a two-dimensional two-layer quasi-geostrophic channel model. This simplified representation of the mid-latitude dynamics of the atmosphere is based on the conservation of potential vorticity in two well-mixed layers with uniform potential temperature on a $\beta$ plane (Fandry and Leslie, 1984, their Sect. 2a). In this setting, the atmosphere can be represented either by the stream function field $\phi(x, y, z, t)$ or by the potential vorticity field $q(x, y, z, t)$.

Similar implementations as the one chosen in this article have been used at ECMWF by Fisher and Gürol (2017), Laloyaux et al. (2020), Farchi et al. (2021), and Farchi et al. (2023) to explore the impact of 4DVar research before trialling it in operational systems. In this article, we use the model implemented in the OOPS (Object-Oriented Prediction System) repository of the Joint Center for Satellite Data Assimilation (JCSDA). It is derived from the implementation presented in Fisher and Gürol (2017) but uses dimensional variables instead of non-dimensional variables. The only other differences are the domain dimensions, grid sizes, and time step which have been adapted to accommodate easier addition of coarser grids.

The chosen domain is rectangular, with periodic boundary conditions on the east-west direction. The zonal dimension is chosen to be the latitude circle length at 43°N ($L_x = 29277$ km), and the meridional dimension is chosen to ensure a 1:3 ratio between the width and the length of the domain ($L_y = 9759$ km). The domain is discretized on a $n_y \times n_x = 80 \times 240$ grid, with data points defined at the nodes of the grid. Vertically, the two model layers have depths of 4 km (bottom layer) and 6 km (top layer), as in Fisher and Gürol (2017).

The northward winds are assumed to be zero on the north and south boundaries. This implies that the stream function has uniform values on these boundaries. The Dirichlet boundary conditions on the stream function are chosen to impose an average eastward wind speed of $10 \text{ m s}^{-1}$ in the lower layer and $40 \text{ m s}^{-1}$ in the upper layer. A potential vorticity forcing term is added to the bottom-layer equations, corresponding to a Gaussian heating or orography source of e-folding radius 1000 km and of amplitude $5 \times 10^{-5} \text{ s}^{-1}$, centred on a grid point located at one-quarter of the domain in the eastward direction and three-quarters of the domain in the northward direction.

The time integration is based on a semi-Lagrangian upstream scheme to advect potential vorticity. Each time step includes a conversion from stream function to potential vorticity and horizontal winds, an advection of potential vorticity by the winds, and a conversion of the advected potential vorticity into stream function. This last step requires solving a Poisson equation. Preliminary numerical experiments showed that the accuracy of the numerical integration did not improve substantially for time steps smaller than 5 min. This 5 min time stepping has thus been retained.

## 4.2 Reference experiments

The experiments presented here are all based on pure 3DEnVar (three-dimensional ensemble variational) analysis schemes, i.e. 3DVar schemes that rely on a purely ensemble-based background error covariance matrix, with no static (parametric) covariance term. Performance is measured from the analysis RMSE with respect to the truth. We focus on a single analysis in this article and do not investigate the impact on a cycled forecast analysis system.

Hereafter, all computations (performance metrics, minimizations, field perturbations, interpolations) use the stream function representation of the model, consistently with the aforementioned ECMWF studies. Since stream function is a horizontally integrated quantity compared to potential vorticity, it effectively transforms positioning errors into amplitude errors and is thus better suited to Gaussian-based variational assimilation and to root mean square performance metrics. The stream function also offers a representation of the system state with larger length scales than potential vorticity and is therefore better suited to spatial interpolation.

The truth run is initialized by first integrating the model over 60 d to reach a permanent regime. From this initial 60 d forecast, the truth run is integrated forward for an additional 12 h, from time $t=0$ to time $t=12$ h. A background state is generated by adding a perturbation field to the initial true state at $t=0$, as schematized in Fig. 1a. This perturbation field is sampled from a normal distribution $N(0, A)$ with zero mean and covariance matrix $A$, which is defined as follows.

For $1 \le i, j \le n$,

$$A_{ij} = \sigma(y_i)\sigma(y_j) \exp\left(-\frac{(x_i - x_j)^2 + (y_i - y_j)^2}{2d_h^2}\right) \times \exp\left(-\frac{(z_i - z_j)^2}{2d_z^2}\right). \quad (36)$$

For $0 \le y \le L_y$,

$$\sigma(y) = \sigma \min \left( 1, \frac{y}{d_b}, \frac{|L_y - y|}{d_b} \right), \quad (37)$$

where $x_i, y_i$, and $z_i$ are the three-dimensional spatial coordinates of point $i$. This Gaussian spatial covariance model has
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Experimental setting: (a) truth run and background trajectory, (b) generation of three ensemble members from the background trajectory, and (c) generation of a pair of coupled ensemble members on fidelity levels $ℓ$ and $ℓ-1$, with $2 \le ℓ \le L$, based on a given stochastic input $\epsilon_i$. $M_L^{12h}$ denotes a 12h integration by the forecast model discretized on grid $ℓ$. Perturbations $\epsilon_i$ are independent and identically distributed, drawn from a zero-mean Gaussian distribution with covariance matrix $\mathbf{A}$.</figcaption></figure>

length scales of $d_h = 1000$ km horizontally and $d_z = 6$ km vertically and a standard deviation $\sigma = 6 \times 10^6$ m$^2$s$^{-1}$. In practice, the second exponential decay term in $A_{ij}$, associated with the vertical length scale $d_z = 6$ km, is either 1 if $z_i = z_j$ or $\exp(5^2/(2 \times 6^2)) \approx 0.71$ if $|z_i - z_j| = 5$ km (the distance between the centres of each vertical model layer). For consistency with the unperturbed north and south boundary conditions, the standard deviations of the covariance model decay linearly to zero within $d_b = 300$ km of the north and south boundaries. This affects the two rows of grid points in the vicinity of each boundary. A simplified ensemble generation scheme is devised by adding similar perturbations to the already perturbed background state at $t = 0$ and integrating forward in time for 12h (Fig. 1b). The resulting ensemble is Gaussian-distributed at $t = 0$ and weakly non-Gaussian at $t = 12$ h due to the non-linear model integrations. The assimilation step is performed at $t = 12$ h.

In addition to the multilevel estimators (MLMC and wMLMC), two reference $\mathbf{B}$ matrices are computed at the analysis time: an MC estimator with 20 members (referred to as MC) and a reference MC estimator with $10^4$ members

(referred to as 10k MC). The 20-member MC estimator is our baseline, with 20 fine-grid members being the target budget we can afford. The 10k MC estimator will be used as an estimation of the true covariances.

Details on the analysis settings, including on covariance localization, will be given in Sect. 5.

## 4.3 Building a hierarchy of low-fidelity models

In order to improve upon the MC estimation of background error covariances, we need to introduce a hierarchy of low-fidelity models. Here, this hierarchy is provided by coarsening the horizontal model grid and time resolution over the 12h integration period. Details on the hierarchy of discretizations are given in Table 2. A spatial coarsening ratio of 2 is used between each fidelity level so that grid points on coarse grids are a subset of the grid points on finer grids. For fidelity level $ℓ$ ($1 \le ℓ \le 4$) the grid points are positioned at

$$ \begin{cases} x_i(\ell) = i \cdot h_x(\ell), & 0 \le i \le n_x(\ell) - 1 \\ y_i(\ell) = i \cdot h_y(\ell), & 1 \le i \le n_y(\ell) - 1, \end{cases} \quad (38) $$
where the grid cell sizes are defined as $h_x(\ell) = L_x/n_x(\ell)$ and $h_y(\ell) = L_y/n_y(\ell)$. In practice, there is a 1:3 ratio between $n_x$ and $n_y$ and between $L_x$ and $L_y$ so that $h_x = h_y$ for all fidelity levels. Note that the equations in Eq. (38) are also valid for $\ell = 4$, i.e. for the fine discretization introduced in Sect. 4.1.

For each low-resolution grid, independent experiments have been run to choose an integration time step $\Delta t(\ell)$. This time step was chosen as the largest time step that kept the time discretization error significantly smaller than the space discretization error. The time step lengths $\Delta t(\ell)$ are given in Table 2, alongside the discretization grids.

As the respective sizes of the inputs and outputs of the models must be identical across fidelity levels, bicubic interpolation operators are used to define restriction and prolongation between the fine grid (fidelity level 4) and coarser grids (operators $\mathbf{P}_{L \to \ell}$ and $\mathbf{P}_{\ell \to L}$ in Fig. 1c). Since the stencil of this interpolation is of size 4 by 4, an additional row of data is needed at the north and south of the domain to perform the interpolation. These rows are added before the bicubic interpolation by linear extrapolation. Using a higher-order interpolation than the simple bilinear interpolation ensures the interpolated fields are smoothed, thus damping (at least partially) spurious high frequencies that can contaminate the analysis, as illustrated by Briant et al. (2023).

To compare the cost of running a 20-member ensemble on the fine grid with a multilevel ensemble on all grids, we need to quantify the cost of generating an ensemble member on each grid. In practice, the wall-clock time of running a quasi-geostrophic forecast on the two coarsest grids is almost identical. This shows that the computational cost is not dominated by the number of grid points at these coarse (and unrealistic) resolutions. To alleviate this, we chose the more realistic and favourable cost model where the computational cost scales as the number of grid points times the number of time steps over the integration period. This theoretical computational cost of running a 12 h forecast on each grid is given in Table 2.

In this experimental setting, the stochastic coupling exploited by the multilevel approach is based on ensembles on two fidelity levels using the same set of initial conditions. The coupling is strongest before the model integration (almost perfect correlation, up to interpolation errors) and decays with increasing forecast lead time. An illustration of a coupled ensemble across all four fidelity levels is shown in Fig. 2. The vorticity fields are shown here, as they make small details more visible to the eye. All ensemble members are defined on the fine grid, even if they have been computed on a coarser grid, since the low-fidelity models include the restriction and prolongation operators. For each fidelity level, a pair of ensemble members is shown. The difference between the two ensemble members on the fine grid ($\ell = 4$) gives an estimate of the ensemble spread. The associated coupled members on the next coarsest grid ($\ell = 3$) show some correlations with the fine-grid simulations, as evidenced for instance by the position of the vortices. Less signal is preserved for the

next coarsest grids. Note that when building a multilevel estimator, coupled simulations are built only for adjacent fidelity levels. Coupled members across four different levels would never be used, except to compute statistics as an offline pre-processing step. In this four-level setting, only the correlations between levels 4 and 3, 3 and 2, or 2 and 1 are of importance.

The vertical and horizontal lines visible on grids $\ell = 1$ and $\ell = 2$ reveal discontinuities in the potential vorticity fields, i.e. in the second-order spatial derivatives of the stream function field. This is consistent with the bicubic interpolation not guaranteeing the continuity of spatial derivatives.

# 5 Numerical results

This section is divided into four parts. In a pre-processing stage, we first use a multilevel ensemble coupled across all levels to estimate the optimal ensemble sizes in each coupling group. Optimality is defined here in terms of the accuracy of the covariance estimator. Building on this optimal allocation, we then verify that the associated multilevel covariance estimator is indeed more accurate than its MC counterpart. Before introducing this multilevel covariance estimator into a variational analysis scheme, we discuss the lack of positive semi-definiteness of the covariance estimates and how this could be circumvented. We then assess the impact of using a multilevel covariance matrix $\mathbf{B}$ on one analysis.

## 5.1 Optimal member allocation

The optimal member allocation is computed from a set of ensembles stochastically coupled across all levels. We used 100 coupled members for this estimation. This value is not unrealistic, as this can typically be computed offline.

From this coupled ensemble, we estimate summed inter-level covariances of MC covariance estimators, as described in Appendix B of Destouches et al. (2023). Using these statistics, we can evaluate the total variance of a multilevel covariance estimator and minimize it under a cost constraint (Eq. 32 for MLMC, Eq. 34 for wMLMC). Due to the spatial averaging, the sampling noise of this estimation is much smaller than the sampling noises we try to minimize. Here, we make the ensemble generation cost equal to that of a 20-member fine-grid MC estimator. We first solve the problem on real numbers using sequential least-square programming and then round the solution to the nearest integer allocations. As this may result in a computational budget slightly different than the target cost of 20 ensemble members, we fine-tune the allocation by removing or adding members to ensure we stay below the target budget while having as many ensemble members as possible on each coupling group. This fine tuning is done automatically, from the most expensive group to the cheapest one.

The optimal ensemble sizes and the associated generation costs are shown in Fig. 3. The member allocation is similar
<table><caption>**Table 2.** Space and time discretizations for the low-fidelity models. Fidelity level 1 refers to the lowest fidelity (coarsest grid), while fidelity level 4 refers to the highest fidelity (original model grid). The degrees of freedom represent the dimension of the state space, i.e. the number of free grid parameters $n_x(\ell) \times (n_y(\ell) - 1) \times 2$. The normalized integration cost is given as a fraction of the cost of a fine-grid run.</caption><thead><tr><th>Fidelity level <i>ℓ</i></th><th><i>n</i><sub>x</sub>(<i>ℓ</i>)</th><th><i>n</i><sub>y</sub>(<i>ℓ</i>)</th><th>Δ<i>t</i>(<i>ℓ</i>) (in min)</th><th>Degrees of freedom</th><th>Normalized integration cost</th></tr></thead><tbody><tr><td>1</td><td>30</td><td>10</td><td>40</td><td>540</td><td>0.00178</td></tr><tr><td>2</td><td>60</td><td>20</td><td>20</td><td>2280</td><td>0.01532</td></tr><tr><td>3</td><td>120</td><td>40</td><td>10</td><td>9360</td><td>0.12342</td></tr><tr><td>4</td><td>240</td><td>80</td><td>5</td><td>37920</td><td>1.00000</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Two ensemble members, $f_4(\epsilon_1)$ and $f_4(\epsilon_2)$, generated with the fine-resolution simulator $f_4$ and associated coupled members $f_\ell(\epsilon_{1,2})$ at coarser resolutions. The vorticity field of the bottom model layer is shown here.</figcaption></figure>

for MLMC and wMLMC. More than half of the computational resources are dedicated to running a 10-member coupled ensemble on the finest coupling group: 10 members on the fine grid $\ell = 4$ and 10 coupled members on grid $\ell = 3$. The ensemble sizes increase as the associated generation cost decreases, until the coarser coupling group, where hundreds of ensemble members can be run for the cost of a single fine-grid member.

The total variance of the covariance estimator is shown in Fig. 4. The total variance is approximately 3 times smaller for the wMLMC estimator (the exact ratio being 0.337). To reach

such a low variance, the single-level MC estimator would have required 60 ensemble members instead of 20.

The weights of the wMLMC associated with this optimal member allocation are, from the coarsest to the finest level, as follows: $\beta_1^{(1)} = -\beta_1^{(2)} = 0.71$, $\beta_2^{(2)} = -\beta_2^{(3)} = 0.79$, $\beta_3^{(3)} = -\beta_3^{(4)} = 0.88$, and $\beta_4^{(4)} = 1$. The benefit of wMLMC over MLMC is only marginal here, increasing the gain from 63% to 66% variance reduction. This could have been different under less favourable circumstances. For instance, doubling the forecast integration time to 24 h instead of 12 h would weaken the stochastic coupling and produce an op-
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Allocation of members in different ensemble groups for different ensemble estimators: MC, MLMC, and wMLMC. The height of each bar is proportional to the computational cost of generating the corresponding coupled ensemble. The number of members in each coupled ensemble is reported on each bar.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Theoretical estimate of the total variance of the covariance matrix estimators introduced in Fig. 3, normalized by the variance of the MC estimator. The total variances are estimated from Eq. (12) for MC, Eq. (29) for MLMC, and the function to minimize in Eq. (34) for wMLMC.</figcaption></figure>

timal variance reduction of 38% for MLMC and 52% for wMLMC, i.e. a 14-point gain by adding optimal weights to each level. Only wMLMC is considered in the rest of this section.

To check that this variance reduction was not specific to a given dynamical situation, we reproduced the experiment for four other starting dates, starting 10, 20, 30 and 40 d after the one we have just detailed. The results were similar, with MLMC variance reductions ranging from 63% to 69% and wMLMC variance reductions ranging from 66% to 72%

across the 5 d. The following results, presented in Figs. 5–10, have been obtained with the first of these 5 d. No attempt has been made to reproduce them on the 4 other days.

Note that there was no guarantee that the multilevel estimator would perform better than the single-level estimator here. A weaker inter-level coupling, or a less favourable cost ratio, could have led to similar or even degraded performance. A first set of experiments (not shown) actually yielded a smaller improvement. We tracked it down to the stochastic coupling being too weak for small length scales, as discussed in Briant et al. (2023). Moving from bilinear to bicubic interpolation operators solved the issue, as a bicubic interpolation acts as a smoother, removing part of the undesirable fine-scale signal while interpolating to a coarser grid.

Note that the member allocation problem can also be generalized to the problem of selecting a subset of fidelity levels, as using all available levels may not be the optimal solution. In practice, we solved the member allocation problem multiple times for all possible subsets of levels that include the finest one. The best theoretical variance was obtained with these four levels. Adding yet another coarser grid only degraded the accuracy of the estimator.

## 5.2 Empirical variance reduction

This reduction in variance is measured as a global average. To see how this translates into local variance reductions and to validate the theoretical computations, we explicitly build 200 realizations of a multilevel covariance estimator and apply them to a Dirac impulse. This has the effect of extracting a column of the covariance matrix estimator. Each one of these 200 realizations uses a different set of coupled ensembles with a total of 603 members per realization (as $603 = 243+2 \times 125+2 \times 45+2 \times 10$; see Fig. 3). From these 200 realizations, we compute an estimate of the statistical expectation and MSE of the estimator. These estimates can be compared to estimates of the expectation and MSE of the 20-member MC estimator, keeping in mind that both estimators have the same ensemble generation cost by construction (see Sect. 5.1 and Fig. 3). The latter estimates are also based on 200 realizations of the MC covariance estimator, with 20 members per realization.

These estimates are shown in Figs. 5 and 6. For the expectation of the covariance column, we also show the “true” expectation, estimated from a $10^4$-member MC estimator. Given that the typical values of stream function fields are of the order of $10^7 \text{ m}^2\text{s}^{-1}$, the typical covariances are of the order of $10^{14} \text{ m}^4\text{s}^{-2}$, and their MSEs are of the order of $10^{28} \text{ m}^8\text{s}^{-4}$. These large values do not affect the numerical accuracy of the sample estimates shown here, since these estimates were obtained by adding or subtracting values with a similar order of magnitude only.

As expected, the empirical expectations all look very similar; no bias is visible in the MC estimator or in the MLMC one. The MSE (or equivalently the variance, in this unbiased
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Statistical expectation of various estimators of a column of the covariance matrix: true expectation estimated from the 10k MC experiment (a), MC estimator with 20 members (b), and wMLMC estimator with same computational budget as a 20-member MC (c). The horizontal and vertical lines indicate the position of the point with respect to which covariances are computed. Only the bottom model layer is shown.</figcaption></figure>

context) has a smaller amplitude for most grid points when compared to the MC estimator. Some minor degradation can be seen close to the northern and southern boundaries. These degradations are likely due to the forced linear decay of the perturbations within 300 km of the north and south boundaries. This can provoke large gradients close to the boundaries, which translate into large values of potential vorticity locally generating high-frequency features. These fine-scale details cannot be accurately captured on the coarse grids, hence the lack of coupling and bad performance in these areas.

For better visualization, a cross-section of the covariance estimate is shown in Fig. 7. The smaller amplitude of the spread around the true value is obvious for the weighted multilevel estimator. It is consistent with the global factor of 1/3 computed in the previous section, as a ratio of 0.337 in variance corresponds to a ratio of $\sqrt{0.337} \approx 0.58$ in standard deviation, which is roughly what is seen in the figure.

## 5.3 From a covariance estimator to B

Before using the MLMC covariance estimator in an analysis scheme, we need to address two questions: how to localize it and how to deal with negative eigenvalues.

### 5.3.1 Localization of a multilevel ensemble estimator

Though more accurate, a multilevel ensemble **B** estimate is still rank-deficient and still needs some regularization before being used in a variational analysis scheme. To preserve the unbiasedness property of the MLMC **B** estimator (compared to the expectation of its localized MC counterpart), we could apply a single common localization to the final covariance estimator. However, in operational ensemble variational data assimilation, the Schur products are never computed explicitly. To avoid storing the full covariance matrix, it has been shown that covariance operators of the form $XX^T \odot L$ can be computed through a process that only requires storing **X** (see
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Similar to Fig. 5 for the mean square error. (a) MC estimator with 20 members. (b) wMLMC estimator with the same computational budget. (c) Logarithm of the ratio of MC MSEs over wMLMC MSEs.</figcaption></figure>

Appendix B of Buehner, 2005). Since such a decomposition $XX^T$ does not exist for the MLMC-estimated $\mathbf{B}$, a localized version of it can only be built by applying localization to each MC estimator in the MLMC telescopic sum:

$$ \hat{\mathbf{B}}_{\text{loc}}^{\text{wML}} = \beta_{1}^{(1)} \hat{\mathbf{B}}_{1}^{(1)} \odot \mathbf{L} + \sum_{k=2}^{L} (\beta_{k}^{(k)} \hat{\mathbf{B}}_{k}^{(k)} \odot \mathbf{L} + \beta_{k-1}^{(k)} \hat{\mathbf{B}}_{k-1}^{(k)} \odot \mathbf{L}). \quad (39) $$

As the cost of applying localization to a covariance matrix scales with the grid size and with the ensemble size in practice (e.g. Appendix B of Buehner, 2005), localizing some terms like the base term $\hat{\mathbf{B}}_{1}^{(1)}$ with hundreds of members could become prohibitively expensive if localization is performed on the fine grid. An alternative is to perform localization on the coarse grid, which is cheaper, and then apply the prolongation operators on the localized increments. Denoting $\hat{\mathbf{B}}_{\ell}^{(k)} = (\mathbf{P}_{\ell \to L} \mathbf{X}_{\ell}^{(k)})(\mathbf{P}_{\ell \to L} \mathbf{X}_{\ell}^{(k)})^T$, we have

$$ ((\mathbf{P}_{\ell \to L} \mathbf{X}_{\ell}^{(k)})(\mathbf{P}_{\ell \to L} \mathbf{X}_{\ell}^{(k)})^T) \odot \mathbf{L}_L \\ \approx \mathbf{P}_{\ell \to L} ((\mathbf{X}_{\ell}^{(k)} \mathbf{X}_{\ell}^{(k)^T}) \odot \mathbf{L}_{\ell}) \mathbf{P}_{\ell \to L}^T, \quad (40) $$

where a subscript has been added to the localization matrices to indicate on which grid the localization is performed. The two strategies should yield reasonably close results, especially when the localization length scales are large compared to the largest grid spacings involved. In this article, the small size of the problem under consideration allowed us to perform localization on the fine grid for all these terms, despite the large ensemble sizes involved.

When comparing the costs of MLMC and MC estimators, it should be noted that having comparable ensemble generation costs does not necessarily guarantee comparable memory storage requirements or comparable costs of applying a (localized) matrix-vector product. Instead of imposing a constraint on the ensemble generation cost in Eq. (34), it would also be possible to impose a constraint on the memory storage requirements or on the cost of performing a matrix-vector product with localization. If the cost models of these three aspects evolve differently as a function of the fidelity level $\ell$, choosing a different constraint in the member allo-
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Typical spread of the estimators of a column of the covariance matrix. The estimates shown here correspond to a 1D section of Figs. 5 and 6 at the horizontal latitude line shown in these figures. The filled area for each estimator gives the $\pm\sigma$ range around the mean, where $\sigma$ is the standard deviation of the estimators.</figcaption></figure>

cation problem would yield different optimal ensemble sizes and thus different estimator accuracies.

Localizing an ensemble covariance estimator makes it biased, be it an MC or a wMLMC estimator. Using the same localization as the MC estimator for all wMLMC terms, as proposed in Eqs. (39) and (40), yields a multilevel estimator with the same bias as the localized MC estimator. Here, we rather choose to use different localization parameters for the different terms of the wMLMC-estimated B, even though the resulting sum no longer has telescopic expectations (i.e. the terms no longer average out to zero in expectation). To keep localization tuning feasible, we propose using two sets of localization parameters only: one for the base term and one for the correction terms. This is based on the fact that the base terms has more members and therefore may only need a weaker localization. In addition, the specific nature of correction terms may be better captured by a dedicated localization. The optimal localization parameters chosen for the wMLMC and MC estimators will be described in Sect. 5.4.

### 5.3.2 Negative eigenvalues of a multilevel covariance estimator

Another aspect to consider carefully is the lack of positive semi-definiteness of the (weighted) MLMC covariance es-

timator. The (weighted) MLMC estimator is not positive-definite by construction due to the presence of negative terms in the correction terms. There is no obvious way to constrain this. As long as negative terms are involved, there will be a chance that some realizations of the estimators have negative eigenvalues. This problem is well-known in multilevel data assimilation, the usual solution being to truncate the spectrum to remove negative eigenvalues, as in Hoel et al. (2016). This is equivalent to projecting the symmetric matrix onto the space of symmetric positive-semi-definite matrices, as explained by Higham (2002). More recently, interesting work focused on building a multilevel estimator of a covariance matrix that would be positive by construction (Maurais et al., 2023, 2025). As this involves computing costly matrix logarithms and exponentials, the feasibility of this approach for operational data assimilation in high dimensions remains to be demonstrated.

In our case, we do observe negative eigenvalues in wMLMC estimates, as shown in the spectra in Fig. 8. These spectra were obtained for a single wMLMC estimate with and without localization, a standard MC estimate with and without localization, and the reference B estimated with $10^4$ members (with no localization). The spectra are obtained via a randomized eigenvalue decomposition using 2020 samples, as described in Algorithm 1 of Saibaba et al. (2016).
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Dominant eigenvalues of various covariance matrix estimates. Only the 1000 eigenvalues with the largest amplitude (negative or positive) are shown. The y axis uses a symmetric logarithmic scale, with a linear range from $-10^9$ to $10^9$.</figcaption></figure>

We first note that the standard MC and wMLMC estimates are both rank-deficient, with ranks 19 and 596, respectively, as evidenced by the zero eigenvalues from indexes 20 and 597. For the unlocalized wMLMC-estimated **B**, the first negative eigenvalue appears at index 8, with an amplitude of 0.11 times the first (positive) eigenvalue. Many others follow, to the extent that the full spectrum includes 30% negative eigenvalues. This is clearly not negligible and could cause convergence issues during the minimization of the data assimilation cost function. The situation improves after localization has been applied, with the first negative eigenvalue appearing at index 61 in the spectrum, with amplitude 0.03 times the first (positive) eigenvalue. The ratio of negative eigenvalues is also decreased from 30% to 4%. Although localization helps, it does not fully solve the problem.

### 5.3.3 Handling negative eigenvalues

We now explore various possible practical remedies to treat the presence of negative eigenvalues. The first solution, already mentioned, involves truncating the spectrum to remove negative eigenvalues. This implies performing an eigen-decomposition of the covariance matrix and rebuilding it without the eigenvectors associated with negative eigenvalues. This decomposition can be done before the critical path of an operational data assimilation suite, i.e. before the operationally time-constrained interval between the reception of the observations to assimilate and the delivery of the associated analysis to the subsequent forecast. Based on the fact that an unlocalized wMLMC-estimated **B** has low rank, the numerical cost of the eigen-decomposition could be kept reasonable. For instance, relying on a randomization method, we would only have to apply as many matrix-vector products as the rank of the matrix, and this could be done in parallel.

In our case, an upper bound of this rank is known beforehand from the number of members used to build the estimate. For instance, for the wMLMC-estimated **B** above, the rank is at most $(N^{(1)}-1)+2(N^{(2)}-1)+2(N^{(3)}-1)+2(N^{(4)}-1) = 596$, which is small compared to the size of the matrix (37920 here; see Table 2). However, after removal of negative eigenvalues, there is still a large number of eigenvectors remaining (419 in this case). Contrary to the raw MLMC case, these vectors are now stored on the finest grid, and we are back to the problem of applying localization to a 419-member ensemble on a fine grid. Compared to the original problem of applying localization to a 20-member ensemble, the cost has prohibitively increased. We are thus led to conclude that while randomization approaches may be of interest for off-line diagnostics, they are not a viable solution to the negative eigenvalue problem, unless we allow the cost of applying **B** to increase significantly compared to the cost of applying a standard localized ensemble **B**.

A second solution would be to rely on localization and hybridization. As already mentioned, localization has no theoretical reason to make the matrix positive-semi-definite (PSD), although it helps reduce the amplitude and numbers of negative eigenvalues in our case. Hybridization is more promising for this purpose, however. By defining the covariance matrix as a weighted average of an ensemble **B** and a parametric **B**, hybridization can possibly restore positive semi-definiteness in the covariance estimate. Given the relatively small amplitude of the negative eigenvalues after localization, we can reasonably assume that hybridization could remove most if not all of the negative eigenvalues, even with a small weight for the parametric **B**. In another context, Higham et al. (2016) proposed an algorithm to estimate the smallest hybrid weight that restores positive semi-definiteness. Whether this is applicable to high-dimensional
(but low-rank) problems, and whether this could be done without giving too much weight to the parametric static **B**, is still to be explored.

A last option, which could be combined with the previous one, would be to use a possibly non-PSD matrix in the minimization algorithms and adapt the algorithms to deal with these negative eigenvalues. We first remark that using a **B**-preconditioned conjugate gradient minimization algorithm (Derber and Rosati, 1989) dispenses with the need to define a **UU**^T decomposition of the background error covariance matrix. As a first crude solution to deal with these negative eigenvalues, we propose using a slightly modified **B**-preconditioned conjugate gradient algorithm.

This minimization algorithm was originally designed for use with a PSD **B** matrix. Here, we propose using the non-PSD **B** as such, but with two additional early stopping criteria. At each iteration of the minimization algorithm (Algorithm 2 in Gürol et al., 2014), two intermediate scalar values (inner products) are monitored. The first one is the curvature of the Hessian of the (non-preconditioned) system along the descent direction. The second one is the **B** norm of the residual **r** of the preconditioned system, i.e. **r**^T**Br**. In the standard case with a PSD **B**, both quantities are positive. With a multilevel **B** having negative eigenvalues, the “**B** norm” is no longer a norm, and either or both quantities can give negative values if the descent direction or the residual is pointing to directions associated with negative eigenvalues of **B**. When this happens, the algorithm is exploring directions of control space where the estimate of **B** is clearly poor, which justifies early stopping. In practice, these checks can be applied to the numerator and denominator of step 9 in Algorithm 2 of Gürol et al. (2014).

## 5.4 Impact on a single analysis

Although the multilevel estimators computed so far are better than MC estimators in the sense of reducing the MSE measured in the Frobenius norm, there is no guarantee that they would yield better analysis estimates. We now perform various minimization experiments to assess the impact of multilevel background error covariances on the quality of the analysis.

The data assimilation scheme is a 3D ensemble variational scheme, which means there is no time dimension and that **B** is derived purely from an ensemble (or coupled ensembles), with no parametric (climatological) hybrid component. The observation network consists of direct observations of the stream function at randomly selected grid points. To mimic realistic systems, only 1% of the model grid points are observed. The observation values are derived from the truth run. Perturbations are then added to the observations to simulate observation error. The observation error is Gaussian, with a uniform standard deviation set at the same value as the prior ensemble spread ($\sigma_o \approx \sigma_b$), i.e. $\sigma_o = 9 \times 10^6 \text{ m}^2\text{s}^{-1}$. Note

<table><caption>**Table 3.** Optimally tuned localization length scales, expressed in grid points (horizontal localization) or model layers (vertical localization). The length scales are defined as Daley length scales (Daley, 1993), which match the standard deviation parameter of the Gaussian-shaped localization functions used here.</caption><thead><tr><th>Covariances</th><th>Horizontal localization</th><th>Vertical localization</th></tr></thead><tbody><tr><td>MC</td><td>25</td><td>1.7</td></tr><tr><td>wMLMC base term</td><td>60</td><td>1.3</td></tr><tr><td>wMLMC correction terms</td><td>15</td><td>1.2</td></tr><tr><td>10k MC</td><td>none</td><td>none</td></tr></tbody></table>

that this value of $\sigma_b$ has evolved from $6 \times 10^6 \text{ m}^2\text{s}^{-1}$ at time $t=0 \text{ h}$ (see Sect. 4.2) to $9 \times 10^6 \text{ m}^2\text{s}^{-1}$ at $t=12 \text{ h}$.

As for previous experiments, three covariance models are compared: a 20-member MC estimator, the same-cost wMLMC with optimal member allocation, and a reference **B** with 10 000 members (“10k MC”). Including the reference **B** in these experiments provides a benchmark of the gain that can be achieved by improving the estimation of the background error covariance matrix. In operational situations, we would not know the optimal allocation and optimal weights for wMLMC, as we could not afford to run a pre-processing step to compute them in real time. Instead, we would have to use a climatological value for the ensemble sizes and weights. The weights could be updated every few cycles, which would partially compensate for the sub-optimality of using climatological ensemble sizes.

Both MC and wMLMC covariance estimators are localized. The localization parameters are tuned on an independent realization of the random observation network, random observation errors, and background ensembles to minimize the analysis error. The tuning is performed manually by grid search on the space of localization parameters to minimize the RMSE of the analysis. The chosen localization parameter values are given in Table 3.

The minimization is performed using the updated **B**-preconditioned conjugate gradient algorithm introduced in the previous section. A total of 20 iterations are found to be sufficient to reach convergence in all three settings, as shown in Fig. 9. For this case, a negative **B** norm of the residual led to an early stopping of the wMLMC minimization at iteration 15 out of 20.

Sensitivity to the observation location and errors is removed by running the analyses 200 times with different realizations of the random observation network, random observation errors, and random background ensembles (for MC and wMLMC experiments). Whisker plots of the analysis error with respect to the truth run are shown in Fig. 10.

The impact of using wMLMC covariances in the analysis is globally positive for all percentiles shown. However, the magnitude is quite small, with only 1% reduction of analysis
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Evolution of minimization metrics during the minimization. RMSE with respect to the truth (a), cost function (b), and 2-norm of the residual (c). All quantities are normalized by their initial value. Note that in a real application where we do not have access to the truth, only the bottom two metrics are available.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 10.** Comparison of analysis RMSEs normalized by the RMSE of the background state. The boxes show the quartiles and medians of the 200-realization sets of analyses. The whiskers of the boxes show the 10th and 90th percentiles. The violin plots extend from the smallest sample to the largest sample.</figcaption></figure>

error on average (not shown on Fig. 10). If compared to the smallest possible error achieved by the 10k MC experiment for each realization of the observation network, the relative error reduction increases to 11% on average. This 11% improvement is of less importance than the gain observed on the covariance estimator in Sects. 5.1 and 5.2. This is not surprising, as there is no direct relationship between the MSE of the background error covariance estimator and the MSE of the resulting analysis.

It is not possible from these experiments to predict how these results would change in *cycled* data assimilation experiments. Most of the unknown lies in how the inter-level correlations between coupled members would evolve across cycles. After many cycles, we expect the system to progressively forget the initial conditions, and the only source of inter-level coupling would be the random perturbations of the innovations and (possibly) of model errors. Whether this would be enough to maintain strong inter-level correlations could be the topic of further research.

# 6 Discussion and conclusions

We have discussed in this article the potential of MLMC methods to improve the estimation of background error covariances for ensemble data assimilation. Starting from an EDA, the main idea is to remove a few ensemble members and to reallocate the associated computational resources to generate a much larger ensemble on coarser grids, with the goal of obtaining less noisy estimators. By further combining stochastically coupled ensemble members generated across several grids, multilevel techniques provide estimators that are not only more accurate, but also unbiased.

We have illustrated with a quasi-geostrophic model how optimal ensemble sizes can be determined in a multilevel setting. We have also illustrated how a localized multilevel covariance matrix can be built as a matrix-vector operator. The approach does not require explicit storing of the covariance matrix elements, nor does it require increasing the number of ensemble members to be stored at the resolution of the assimilation grid. The proposed method has a computational cost that scales linearly with the state dimension.

However, using the resulting multilevel background error covariance matrix in a variational data assimilation scheme presents certain challenges since, by construction, the matrix does not guarantee positive semi-definiteness. Although various approaches have been proposed in the literature to enforce PSD multilevel covariance estimates, none of them are computationally affordable for extremely high-dimensional data assimilation problems such as those encountered in NWP. Existing methods either increase the number of ensemble members to be stored on the assimilation grid or scale at least quadratically with the state size.

To ensure that the method is feasible for high-dimensional problems, we proposed adapting the minimization algorithm
to account for a non-PSD background error covariance matrix, rather than trying to render the matrix PSD for the minimization algorithm. In a B-preconditioned conjugate gradient minimization algorithm, for instance, several diagnostics can be used to detect spurious (negative) eigenvalues in the background error covariance matrix. In our experiments, stopping the minimization early, as soon as negative eigenvalues are encountered, resulted in an improved analysis. This approach offers no guarantee of convergence, however, and further research is needed to make this algorithm more robust. Although not explored in this study, another promising approach to enforce positive semi-definiteness would be to hybridize the ensemble covariance matrix with a parametric covariance matrix, which is already common in ensemble variational data assimilation.

In addition to the problem of ensuring positive semi-definiteness, several other areas remain unexplored. For instance, it is unclear how the less accurate estimation of small scales in multilevel ensemble covariances would propagate through the analysis and forecast cycles and how imbalance and spin-up could accumulate or not over cycles. It is also unclear how the method would perform in a cycled data assimilation and forecast context, where the explicit stochastic inputs would come from perturbations to the innovations or from stochastic physics rather than the initial conditions as considered here.

Finally, it is important to remark that the purpose of an EDA is not only to provide background error covariance estimates to the analysis scheme, but also to initialize an ensemble prediction system (EPS). How a multilevel ensemble data assimilation would impact the subsequent EPS is not known. To minimize the impact on the EPS, one could enforce a minimum ensemble size on the fine grid. For instance, reducing the number of ensemble members on the fine grid from 20 to 10 may be acceptable if the EPS runs with 10 or fewer members. Another possibility would be to leverage the potential of the multilevel approach for the EPS. In particular, a coupled multilevel EDA could be used to initialize a coupled multilevel EPS. In theory, multilevel techniques could then be used to estimate any statistic of interest: expectations, percentiles, probability of exceeding a threshold, etc. This may not be effective in practice, as optimal ensemble sizes are likely to vary depending on the application. Using a common approach for all applications would imply a weaker coupling for each of these applications and therefore reduced improvement in the statistic estimators.

## Appendix A: Optimal weights for weighted MLMC

For a given set of ensemble sizes $N^{(k)}$ ($1 \le k \le L$), Destouches et al. (2023) give a formula for the optimal MLMC weights $\beta_{\ell}^{(k)*}$: $1 \le k \le L$, and $\ell = k, k-1$ (see their Sect. 6.2, derived from Schaden and Ullmann, 2020):

$$ \begin{pmatrix} \beta_{k-1}^{(k)*} \\ \beta_{k}^{(k)*} \end{pmatrix} = (\mathbf{C}^{(k)})^{-1} \mathbf{R}^{(k)} \left( \sum_{\ell=1}^{L} \mathbf{R}^{(k)\mathrm{T}} (\mathbf{C}^{(k)})^{-1} \mathbf{R}^{(k)} \right)^{-1} \boldsymbol{\alpha}, \quad (\mathrm{A}1) $$

where $\mathbf{R}^{(k)}$ is the selection matrix that selects levels $k-1$ and $k$ in a vector of size $L$:

$$ \mathbf{R}^{(k)} \in \mathbb{R}^{2 \times L}, \quad R_{i,j}^{(k)} = \begin{cases} 1 & \text{if } i = 1 \text{ and } j = k-1, \\ 1 & \text{if } i = 2 \text{ and } j = k, \\ 0 & \text{otherwise.} \end{cases} \quad (\mathrm{A}2) $$

Here, $\boldsymbol{\alpha}$ is the last vector of the canonical basis of $\mathbb{R}^L$:

$$ \boldsymbol{\alpha} \in \mathbb{R}^L, \quad \alpha_i = \begin{cases} 0 & \text{if } i < L, \\ 1 & \text{if } i = L. \end{cases} \quad (\mathrm{A}3) $$

**Code availability.** The code used for the numerical results presented in this article is publicly available at https://doi.org/10.5281/zenodo.15097074 (Destouches, 2025).

**Data availability.** No data have been used in this article that cannot be regenerated using the code cited in the “Code availability” statement.

**Author contributions.** The contributions of the authors are given following the CRediT taxonomy (https://credit.niso.org/, last access: 19 June 2025). Conceptualization: PM as lead, MD, SGü, and ATW with equal contributions; SGr and ES with supporting contributions. Investigation: MD and PM (equal), SGü, ATW, SGr, and ES (supporting). Software: MD (lead), SGü (supporting). Supervision: PM (lead), SGü and ATW (equal), and SGr and ES (supporting). Writing (original draft): MD. Writing (review and editing): MD, PM, SGü, ATW (equal), SGr, and ES (supporting).

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** We are grateful to Benjamin Ménétrier for his help in setting up OOPS-JEDI and the nested grids of the quasi-geostrophic model. We thank Gordon Inverarity for his informal review of this paper. We are grateful to an anonymous reviewer and to Alban Farchi, whose helpful comments improved the quality of this paper.
**Financial support.** This research has been supported by the INSU-CNRS (program LEFE/MANU for the MFDA project).

**Review statement.** This paper was edited by Natale Alberto Car-rassi and reviewed by Alban Farchi and one anonymous referee.

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