Editorial Type:
Article
Article Type:
Research Article

An Adaptive Compensatory Approach of the Fixed Localization in the EnKF

Xinrong Wu Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Wei Li Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Guijun Han Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Lianxin Zhang Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Caixia Shao Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Chunjian Sun Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Lili Xuan Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China

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Print Publication:
01 Nov 2015
DOI:
https://doi.org/10.1175/MWR-D-15-0060.1
Page(s):
4714–4735
Full access

Abstract

Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.

Corresponding author address: Xinrong Wu, 93 Liuwei Road, Hedong District, Tianjin 300171, China. E-mail: xinrong_wu@yahoo.com

Abstract

Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.

Corresponding author address: Xinrong Wu, 93 Liuwei Road, Hedong District, Tianjin 300171, China. E-mail: xinrong_wu@yahoo.com
Keywords: Mathematical and statistical techniques; Bayesian methods; Kalman filters; Optimization; Forecasting; Ensembles; Models and modeling; Data assimilation

1. Introduction

The flow-dependent background error covariance is an advantage of the ensemble Kalman filter (EnKF; Evensen 2007) over variational analysis methods. However, a commonly used finite model ensemble often underestimates prior variance and introduces spurious correlation between a state variable and remote observations. While various variance inflation schemes (e.g., Anderson and Anderson 1999; F. Zhang et al. 2004; Anderson 2008; Houtekamer et al. 2009; Miyoshi 2011) have been developed to address the first issue, a localization technique is usually used to ease the second problem. The original localization scheme (e.g., Houtekamer and Mitchell 1998; Anderson 2001; Hamill et al. 2001; Szunyogh et al. 2008) was the fixed localization approach, which is usually realized by a Schur product (an element-by-element multiplication) of the ensemble-evaluated error covariance with an analytic localization operator. All fixed localization models need to determine an impact radius that defines a maximum impact range of observations and significantly influences the analysis quality of the EnKFs. The optimal impact radius depends on the ensemble size, the observing system, and the numerical model itself (Houtekamer and Mitchell 1998; Mitchell et al. 2002). However, it is time consuming to tune the optimal impact radius, especially for a general circulation model (GCM).

Given the above disadvantages of fixed localization models, many adaptive localization algorithms (e.g., Anderson 2007; Bishop and Hodyss 2007, 2009a,b, 2011; Bishop et al. 2011; Jun et al. 2011; Anderson and Lei 2013; Lei and Anderson 2014) have been proposed. Several adaptive schemes (e.g., Anderson 2007) entail trivial additional cost to a standard filter and are routinely used in GCMs and other very large models. A more detailed review of these localization methods can be found in Berre and Desroziers (2010). Because of the low computational cost and feasible implementation, the fixed method is commonly used in actual data assimilation. However, the assimilation quality of EnKF with a fixed localization strongly depends on the impact radius. An overly small (large) impact radius may cause the loss (contamination) of longwave information (analysis states). Accordingly, some multiscale analysis methods have been presented to extract the multiscales of the observations. Miyoshi and Kondo (2013) proposed a dual-localization method, which was demonstrated to be superior to a single-localization method under twin experiments. Wu et al. (2014, hereafter W14) utilized a multiscale analysis (MSA) technique to extract the multiscale information from the observational residuals (the difference between the observations and the interpolated analysis ensemble means produced by the EnKF without inflation) caused by extreme (that is overly small or overly large) impact radii and then corrected the analysis ensemble mean of the EnKF without inflation. Their results demonstrated that the EnKF-MSA method can greatly improve the assimilation quality of the EnKF without inflation for extreme impact radii and has less dependence on the impact radius than the EnKF without inflation. However, the computational cost of the MSA will be huge for high-resolution numerical models and intensive observing systems, and the EnKF-MSA method made things worse than the EnKF without inflation for moderate impact radii.

In this study, we present an adaptive approach to make up for the above demerits. At each analysis step, after the EnKF without inflation is completed, if the root-mean-square error (RMSE) between the interpolated analysis ensemble mean and observations exceeds a threshold, a multigrid analysis (MGA) is adaptively activated to retrieve the multiscale information from the observational residual; then the analysis field of the MGA is added to the analysis ensemble mean of the EnKF without inflation to form the final analysis ensemble mean. With a global barotropic spectral model and biased twin experiments, as well as an idealized observing system, the performance of the proposed algorithm is investigated. Although localization can also be applied to the background error covariance in the observation space (e.g., Houtekamer and Mitchell 1998; Ott et al. 2004; Hunt et al. 2007; Greybush et al. 2011), we only focus on the background error covariance localization in the state space in this study. Note that the EnKF-MSA and the adaptive EnKF-MGA are different from the running in place and quasi-outer-loop (RIP/QOL) method of Yang et al. (2012) in some aspects. The RIP/QOL method proposed activation criteria to determine when the second loop is activated to use the same observations more than once. In contrast, the MSA and the MGA are applied only to the observational residuals rather than the original observations. There is some similarity between the adaptive EnKF-MGA and the RIP/QOL: that is, both methods use criteria to decide whether to enter the next loop; in contrast, the EnKF-MSA triggers the MSA at each analysis step without any judgment.

The remainder of this paper is organized as follows. Section 2 briefly describes the global barotropic spectral model and the data assimilation algorithms used. Section 3 introduces biased twin experiments. Detailed performance of the proposed method is investigated in section 4. Summary and discussion are given in section 5.

2. Methodology

a. The model

To clearly address the issues raised in the introduction, we employ a global barotropic spectral model based on the equation of potential vorticity conservation (Haltiner and Williams 1980):
e1
where and f represent the relative vorticity and planetary vorticity (i.e., Coriolis parameter), respectively, and H is the depth of the atmospheric layer.
After introducing the geostrophic streamfunction and the Cressman parameter , Eq. (1) becomes
e2
where represents the effect of topography, , f denotes the Coriolis parameter, y represents the northward meridional distance from equator, and denotes the Jacobian operator.

A rhomboidal 21 truncation is applied to the transformation between spectral coefficients and grid values. For the time stepping of the model, the state variables are spectral coefficients. The integration step size is half an hour. A leapfrog time step is used to integrate the model, and a Robert–Asselin time filter (Robert 1969; Asselin 1972) with the filter coefficient γ is applied to damp spurious computational modes. For the data assimilation, the state variable is the atmospheric streamfunction at the 64 (longitude) × 54 (latitude) Gaussian grid points.

b. The EAKF algorithm in Anderson (2001)

In this study, we use one of the deterministic EnKFs [i.e., the ensemble adjustment Kalman filter (EAKF; Anderson 2001)], to perform data assimilation experiments. When observational errors are assumed to be uncorrelated, the EAKF can sequentially assimilate observations. For a single observation yo, the implementation of EAKF can be compressed to the following two steps. First, compute the observational increment as follows:
e3
where i indexes ensemble member; represents the prior ensemble mean of yo; and r and denote the standard deviation of observational error and the prior standard deviation of yo, respectively. The ith prior ensemble member of yo, , is usually obtained through applying a linear interpolation to the ith prior ensemble member of state variable x. Second, use the following linear regression to project the observational increment to the model grids:
e4
where j indexes state variable, while is the prior error covariance between xj and yo, and Δx represents the state increment.
During these two steps, the background error covariance between yo and xj only appears in the linear regression formula Eq. (4). Therefore, when the covariance localization is introduced into the EAKF, the local support correlation is imposed in the numerator of the coefficient of linear regression as follows:
e5
where ρj,y represents the localization function between yo and xj.
Although various fixed localization models exist (e.g., the exponential and Matérn functions), we focus on a widely used Gaspari–Cohn (GC) function (Gaspari and Cohn 1999) in this study: that is,
e6
where b denotes the physical distance between yo and xj, and a represents the half-width of the GC function. In this study, we use a to denote the impact radius of observations for the EnKFs. Table 1 lists the key notations used in this study.
Table 1.

Glossary of notations in this study.

Table 1.

Because of the existence of model error, model nonlinearity, sampling error, and so on, the prior variance of the model state is underestimated, which may cause the filter to diverge. Thus, inflation in some form (normally explicit) is used universally in large applications. In this study, we use “the standard EnKF” to represent the EnKF with inflation.

c. Multiscale analysis

W14 used the MSA to extract multiscale information from the observational residual, which is defined as
e7
where yo, , and represent the observation vector, the observation operator, and the analysis ensemble mean derived by the EnKF without inflation, with the dimensions being K × 1, K × M, and M × 1, respectively.
The cost function for the lth scale level in the MSA is
e8
where LMSA is the number of scale levels, and the superscript “T” indicates the transpose of the matrix; , , , , and represent the increment of the state vector xMSA, the linear projection operator from the observation space to the state space, the observation error covariance matrix in the state space, the smoothing operator, and the observational innovation vector for the lth scale level in the MSA. The dimensions of the above five matrices are M × 1, M × K, M × M, M × M, and K × 1, respectively.
In the implementation of the EnKF-MSA method, is defined as
e9
where is the mapping operator from the state space to the observation space for the lth scale level. For each scale level, the dimension of is K × M. The mapping operator maps to the observation space. Note that, since the MSA is used to extract the multiscale information from the observational residual [i.e., yres in Eq. (7)], is equal to yres. Thus, is equal to . The expression represents the analysis result of the (l − 1)th scale level. The observational error covariance matrix is set to the identity matrix . The smoothing term in Eq. (8) takes the shape of the square of the second derivative of with respect to the longitude and latitude of the analysis grid (i.e., the model grid) [see Eqs. (28) and (29) in W14].
Thus, the gradient of with respect to can be written as
e10
The total increment of x produced by the MSA is
e11

Keeping the analysis grid as the model grid for all scale levels, the MSA extracts multiscale information from the observational residual by varying the correlation factor, which is involved in . The (i, j)th element of , that is Lij(l), denotes the weight of the jth observation on the ith state variable [see Eq. (27) in W14] for the lth scale level. The numerator of Lij(l) is the GC localization function , where a(l) represents the impact radius (i.e., the GC half-width) of observations for the lth scale level in the MSA; bij represents the physical distance between the jth observation and the ith state variable; and the denominator of Lij(l) is a normalization factor.

To facilitate the comparison between the MGA and the MSA, we analyze the computational cost of the MSA. To gain an optimal numerical approximation of , the cost function, the gradient, and an initial guess should be provided to an optimization algorithm [like the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method; e.g., Liu and Nocedal (1989)]. For the lth scale level, the computational cost of the MSA consists of the calculations of and as well as the expense of the iteration of the L-BFGS algorithm. In this study, we use the number of elementwise multiplications to measure the algorithm complexity (i.e., the computational cost). The computational costs of and can be easily computed as MK and MK, respectively. For each iteration of the L-BFGS algorithm, the computational costs of the cost function [Eq. (8)] and the gradient [Eq. (10)] are M2 + MK + 2M + 2 and M2 + MK, respectively. Thus, the computational cost of the MSA for the lth scale level is
e12
where TMSA represents the number of iterations of the L-BFGS algorithm in the MSA. The total computational cost of the MSA is .

From the above analysis, we can see that the computational cost of the MSA is mainly caused by the large dimensions of the analysis grid for each scale level and by the mapping from the observation space to the state space. It can be expected that the computational cost of the MSA will be very large for high-resolution and/or high-dimension numerical models and intensive observing systems.

d. The EnKF-MSA method

W14 presented the EnKF-MSA method, the implementation of which at each analysis step can be expressed as the following five steps: First, use the EnKF without inflation to assimilate all available observations with a-GC half-width (i.e., the GC half-width in the EAKF, see a in Table 1). Second, compute the observational residual using Eq. (7). Third, use the MSA to extract multiscale information from the observational residual with a prescribed configuration of a(l). Fourth, add the analysis field of the MSA to the analysis ensemble mean produced by the EnKF without inflation to obtain the final ensemble mean. Last, add the new ensemble mean to the analysis ensemble perturbations produced by the EnKF without inflation to generate the final ensemble members. More detail on the EnKF-MSA method can be found in W14.

e. Limitations of the EnKF-MSA method

Since the EnKF-MSA activates the MSA at each analysis step, the limit on the high computational cost of the MSA for high-resolution and/or high-dimension numerical models and intensive observing systems is also shared by the EnKF-MSA.

In addition, as we stated in the introduction, although the EnKF-MSA method can greatly improve the performance of the EnKF without inflation for extreme (that is, overly small or overly large) impact radii, it made things worse than the EnKF without inflation for moderate impact radii (see Fig. 1 in W14).

To analyze the reason, we partition the observational residual [Eq. (7)] into the following two parts:
e13
where xt represents the truth of model state. The first term on the RHS of Eq. (13) represents the unknown true observational error, while the second term indicates the unknown true analysis error that is projected to the observation space. In other words, the above two terms represent the noise and the signal, respectively. Under specific configurations of ensemble size, model error, and observing system, the analysis error may be smaller than the observational error for moderate impact radii, which is what happened in W14. Under this circumstance, the observational residual is dominated by the noise. It is difficult for the MSA to extract the multiscale information of the signal, because the EnKF-MSA method activates the MSA at each analysis step without checking the signal-to-noise ratio (SNR) of the observational residual.
Here, we define the SNR for each element of yres as follows:
e14
where k indexes the observations; equals to ; and hk represents the kth row vector of . SNR greater than 1.0 indicates that the known observational residual represents the analysis error more than the observational error. Thus, if an approach can be used to adaptively detect the SNR of yres so as to determine whether the multiscale information should be extracted, the performance of the EnKF-MSA method may be further improved for moderate impact radii.

In summary, there are mainly two limitations of the EnKF-MSA method. One is the high computational cost of the MSA. The other is the inferior performance to the EnKF without inflation for moderate impact radii.

f. Multigrid analysis

To ease the first limitation of the EnKF-MSA, an economical algorithm that can realize similar functions of the MSA is needed. Inspired by this idea, we introduce an MGA method that was initially suggested for solving differential equations (Briggs et al. 2000) and later introduced into the data assimilation community (e.g., Li et al. 2008, 2010; Xie et al. 2011).

The MGA sequentially extracts multiscale signals from the observational residual through gradually refining the analysis grid. The cost function for the lth grid in the MGA is formulated as
e15
where LMGA is the number of levels of analysis grids. The expressions , , , , and represent the increment of the state vector xMGA, the mapping operator from the state space to the observation space, the observational error covariance matrix, and the observational innovation vector and the smoothing matrix for the lth grid in the MGA. The dimensions of the above five matrices are, respectively, ml × 1, K × ml, K × K, K × 1, and ml × ml, where ml represents the dimension of the lth grid. Note that, to retrieve multiple signals from longwave to shortwave, ml monotonically increases from m1 to as l increases from 1 to LMGA. Here, ml is set to (2l−1 + 1) × (2l−1 + 1) = 4l−1 + 2l + 1: that is, the resolution of the lth analysis grid is approximately twice that of the (l − 1)th grid.
For each grid level, is defined as
e16
where is the observational operator from the lth analysis grid to the observation space; maps to the observation space. Similar to the analysis of , is also equal to . Thus, is equivalent to , and represents the analysis result of the (l − 1)th grid level.
In the implementation of the MGA, is also set to the identity matrix, and takes the same shape as . Because of the even grid for each grid level in the MGA, the smoothing term in Eq. (15) can be written as
e17
where is the (i, j)th element of .
The gradient of the cost function with respect to the control vector can be derived as follows:
e18
Thus, the analytic solution of Eq. (15) is
e19
Then, is linearly mapped onto the finest grid (i.e., the LMGAth grid) with the operator . Last, the total adjustment (located on the model grid) of x produced by the MGA is
e20
where linearly projects the total analysis of the MGA located on the finest grid onto the model grid. It is worth mentioning that, although we can use a smoothing operator that contains the second derivatives at all scales to achieve the same analysis quality of the MGA, the MGA here can greatly save the computational cost because the convergence speed of the shortwave is known to be faster than that of the longwave (e.g., Xie et al. 2011).
The main difference between the MGA and the MSA is that the MGA extracts multiscale information through gradually refining the analysis grid without introducing any correlation scale, while the MSA achieves this goal through gradually reducing the correlation scale, with the constant analysis grid being the model grid. Here, we also analyze the computational cost of the MGA. For the lth grid level, the computational cost of the MGA consists of the calculation of , the iteration of the L-BFGS algorithm, and mapping onto the finest grid. The computational cost of is Kml−1. For each iteration of the L-BFGS algorithm, the computational costs of the cost function [Eq. (15)] and the gradient [Eq. (18)] are and , respectively. The computational cost of is . Thus, the computational cost of the MGA for the lth grid level is
e21
where TMGA represents the number of iterations of the L-BFGS algorithm in the MGA. The total computational cost of the MGA is , where the second term indicates the cost of the projection from the finest analysis grid of the MGA to the model grid.

Here, we compare the computational cost of the MGA to that of the MSA. For small l, ml is much smaller than M. If TMGA equals TMSA, . Because LMGA usually has a similar magnitude to that of LMSA, the computational cost of the MGA is also much smaller than that of the MSA. Thus, the MGA is expected to be much more computationally effective than the MSA.

Since the MGA has similar functions as the MSA, a direct way to ease the first limitation of the EnKF-MSA is to replace the MSA by the MGA so as to put forward the EnKF-MGA method.

g. An adaptive EnKF-MGA method

To further avoid the second limitation of the EnKF-MSA method, we present an adaptive EnKF-MGA method based on the above EnKF-MGA method. At each analysis step, the adaptive approach includes the following sequential steps:

  1. Sequentially adjust the ensemble members using the EnKF without inflation with a-GC half-width.

  2. Project the analysis ensemble mean produced by the EnKF without inflation to the observation positions to get the EnKF-estimated posterior observations. Then the observational residual is computed by Eq. (7).

  3. Compute the RMSE between the original observations and the EnKF-estimated posterior observations. If the RMSE is larger than a critical value (denoted as θ), we go through.

    • 3.1) Retrieve the multiscale information from the observational residual using the MGA method.

    • 3.2) Add the analysis field generated by the MGA to the ensemble mean produced by the EnKF without inflation to obtain the final analysis ensemble mean.

    • 3.3) Add the final analysis ensemble mean to the ensemble perturbations produced by the EnKF without inflation to generate the final ensemble members.

If the RMSE is equal to or smaller than θ, the analysis of the EnKF without inflation will be taken as that of the adaptive EnKF-MGA method: that is, the adaptive method reduces to the EnKF without inflation.

Note that no inflation is applied to the first EnKF step in the adaptive EnKF-MGA. There are two motivations: one is to facilitate the comparison between the EnKF-MSA and the adaptive EnKF-MGA; the other is to investigate whether the adaptive EnKF-MGA without inflation can produce comparable (or better) assimilation results to (than) the standard EnKF, which is meaningful for the future application of the adaptive EnKF-MGA in GCMs. In addition, similar to the implementation of the EnKF-MSA, the adaptive EnKF-MGA only updates the ensemble mean produced by the first EnKF step but keeps on using the same ensemble perturbations from the first EnKF step. The unadjusted ensemble perturbations or covariance will introduce an inconsistency that is common to the RIP/QOL method and will be discussed in section 5 in detail.

Since the threshold θ serves as the on–off switch of the MGA, it is a key parameter of the adaptive EnKF-MGA method. Here, we use the following test to determine the value of θ given a significance level α:

  • Null hypothesis: All elements of yres are dominated by the observational error.

  • Alternative hypothesis: At least one element of yres is not dominated by the observational error.

The null hypothesis indicates that each element of yres (denoted as ) can be approximated by the observational error, which is simulated by a Gaussian noise, with the mean and the standard deviation being 0 and r, respectively. With a commonly made assumption that observational errors are uncorrelated, the sum of the square of the normalized observational errors [i.e., ] will conform to the χ2 probability distribution function with K degrees of freedom. If the real observed value of the statistic [i.e., ] is greater than , the null hypothesis will be denied with the significance level α. Here, represents the (1 − α) upper fractile of the χ2(K) distribution.

If we use RMSEres to denote the RMSE between and , that is,
e22
the condition that denies the null hypothesis can be written as
e23
The threshold θ is accordingly set to the RHS of the above inequality. If the RMSEres is greater than θ, the null hypothesis will be denied with the significance level α and the MGA will be activated to extract the multiscale information from yres. Obviously, the unit of θ is the same as that of the observation (i.e., m2 s−1 in this study). Note that, because there are no Gaussian and uncorrelation assumptions of the analysis errors in the observation space, the analysis error is not involved in the determination of θ.

According to Eq. (23), θ is a function of the significance level α, the number of observations K, and the standard deviation of observational error r. Since r is usually assumed to be a constant (106 m2 s−1 in this study), θ mainly depends on K and α. Figure 1 displays the variation of θ with respect to K for α = 0.01 (red), 0.05 (black), and 0.10 (blue). Obviously, θ varies inversely with both K and α. The sensitivity mostly occurs for small values of K. When K exceeds a critical value, θ gradually saturates to a constant for each value of α. This justifies that θ can be set to a constant value when K is sufficiently large (e.g., 107 in practice).

Fig. 1.
Fig. 1.

Variation of the threshold used to trigger the MGA in the adaptive EnKF-MGA method with respect to the number of observations for 0.01 (red), 0.05 (black), and 0.10 (blue) significance levels. Note that the threshold is a function of the number of observations, the significance level, and the standard deviation of observational error that is set to a constant 106 m2 s−1 in this study.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

3. Biased twin experiments setup

Biased twin experiments are designed to investigate the performances of the standard EnKF, the EnKF-MSA, and the adaptive EnKF-MGA methods. The model error is assumed to arise from the uncertainty of the time filter coefficient γ. We assume γ = 0.01 in the truth model and γ = 0.02 in the assimilation model. The sole difference produces an apparent bias between the assimilation model and the truth model.

Starting from the streamfunction at 1200 UTC 1 January 1991 derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis 500-hPa u and υ data, both the truth model and the assimilation model are spun up for 30 model days. Then the truth model is further integrated for another 200 model days to sample “observations.” The observational interval is set to 6 h (12 time steps). A Gaussian noise with the standard deviation of 106 m2 s−1 is imposed to the “truth” streamfunction to simulate the observational error. Here, we set the standard deviation of the observational error to be 4% of the global mean (2.5 × 107 m2 s−1) of the natural variability (i.e., climatological standard deviation) of the streamfunction. For the observing system, we assume observations are randomly and uniformly distributed in area A (0°–180°, 0°–90°N) and in area B (180°–360°, 0°–90°N) and the Southern Hemisphere (denoted as area C). To reflect the spatial distribution of the observing system, we assume the spatial sampling density of the observations in area A is twice that in area B and three times that in area C. In area A, the number of observations is 864, which equals the number of model grids in area A. Figure 2 displays the observing system (dots) and the model grids (pluses). The values of K and M in this study are 1872 and 3456, respectively.

Fig. 2.
Fig. 2.

Model grids (pluses) and observational locations (dots) in the twin experiment in this study. Labels A, B, and C represent three areas with different sampling densities of observations. The thick lines divide the three areas.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Initial model ensembles of atmospheric streamfunction for the assimilation are generated by adding a Gaussian white noise with the mean being zero and the same standard deviation of observational error to the biased initial condition generated by the assimilation model. Note that, because of the coarse resolution of the barotropic model, the Gaussian noise is independently added to each model grid without any coherent length scale. The ensemble size is set to a typical value of 20. Additionally, because the leapfrog scheme is used to integrate the model, a two-time-level adjustment method (S. Zhang et al. 2004) is applied to the data assimilation: namely, the observations at time t are used to adjust the model states at times t and t − 1.

Four types of experiment are conducted to evaluate the performances of the three assimilation algorithms. The first is the ensemble free run (without observational constraints), serving as the reference experiment (CTL). The second is the standard EnKF implemented by the EAKF. The third is the EnKF-MSA method, and the last one is the adaptive EnKF-MGA method. Note that the EnKF-MSA and adaptive EnKF-MGA methods do not employ inflation in this study. In addition, because the EnKF-MSA and the adaptive EnKF-MGA are based on the EnKF without inflation, we also conduct the experiment of the EnKF without inflation so as to compare the performance of the adaptive EnKF-MGA to that of the EnKF without inflation, although an EnKF without inflation simply is not an interesting case for comparison in reality. All assimilation schemes are applied to global observational residuals and activated at the initial time. The three data assimilation algorithms use the same observing system and ensemble initial conditions. The number of iterations of the optimization algorithm for both the MGA and the MSA is set to 10 (i.e., TMSA = TMGA = 10). For the adaptive EnKF-MGA method, to determine the threshold θ we set the significance level α to the typical value 0.01. With the above observing system, θ can be computed as 1.04 × 106 m2 s−1. Discarding the assimilation results in the first 100 days as the spinup, the results of the last 100 days are used to conduct error statistics and analysis. Note that, because of the overly large error of , the results of CTL are not plotted in this paper.

To investigate the dependence of the adaptive EnKF-MGA method on a, 16 values of a starting from 250 to 4000 km with an even increment of 250 km are used. According to the description of the EnKF-MSA method in W14, the configuration of is set to {250, 500, 1000, 2000, 4000 km} for all experiments of the EnKF-MSA in this study. Thus, LMSA equals 5.

In addition, the multiplicative variance inflation
e24
is used in all experiments of the standard EnKF. Here, υ represents the inflation factor, and indicates the inflated ith ensemble member for the jth model state. The bar stands for the ensemble mean. For each assimilation experiment of the EAKF, the best value of υ is determined by minimizing the time-mean RMSE through varying υ from 1.00 to 3.00 with an even increment of 0.01. The time-mean RMSE is defined as
e25
where S represents the number of analysis steps, s indexes the analysis step, and RMSEs denotes the RMSE at the sth analysis step; im and jm are the dimensions of zonal and meridional model grids (which are 64 and 54), respectively. The ensemble mean of the streamfunction is represented by . The superscripts “prior” and “t” denote the prior and the truth, respectively. Note that, because of the coarse resolution of the model, we can use this simple method to determine the best υ. In GCM applications, some adaptive methods (e.g., Anderson 2008) are usually used. The black curve in Fig. 3 shows the variation of υ for the standard EnKF. For a = 250 km, υ is 1.0 (i.e., no need to apply the variance inflation). As the value of a increases, υ gradually increases. Note that, when a equals 4000 km, the EnKF crashes for all values of υ varying from 1 to 3. For values of υ larger than 3, it also fails. This may be caused by the spatially and temporally uniform inflation factor.
Fig. 3.
Fig. 3.

Variation of the optimal inflation factor with respect to the GC half-width (km) for the adaptive EnKF-MGA with inflation (red) and the standard EnKF (black) methods. Note that no inflation is used in the adaptive EnKF-MGA method in the main body of this study. Here, the result of the adaptive EnKF-MGA with inflation is used in section 5.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

4. Results

a. Determination of the number of grid levels

There is a factor that may complicate the application of the MGA. It is the value of LMGA. Thus, a question arises as follows: How does one determine LMGA?

To answer this question, we take a = 250 km as an example to investigate the dependence of the adaptive EnKF-MGA method on LMGA. Since the dimension of the first analysis grid level in the MGA is usually set to 2 × 2, the value of LMGA depends on the selection of , given ml = (2l−1 + 1) × (2l−1 + 1). We perform 11 experiments of the adaptive EnKF-MGA with LMGA varying from 0 to 10. When LMGA = 0, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is also the standard EnKF because the optimal υ = 1.0 (the black curve in Fig. 3).

Figure 4a shows the sensitivity of the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of to LMGA. The vertical line at each LMGA value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE:
e26
The optimal value of LMGA is around 9. However, the RMSE seems to have virtually saturated at LMGA = 7, which has an RMSE close to the optimal value but at less cost. The standard deviations of the RMSE for LMGA = 7, 8, 9, and 10 are smaller than those for LMGA = 0, 1, 2, 3, and 4, demonstrating that the adaptive EnKF-MGA is more stable for the former LMGA values. This can also be justified by Fig. 4b, which plots the RMSE (106 m2 s−1) time series of the prior ensemble mean of with 0 (black) and 7 (blue) grid levels in the adaptive EnKF-MGA method. For LMGA = 7, the dimension of the finest analysis grid is 65 × 65, which is close to the dimensions of the model grid (64 × 54). Because area A in the observing network has the largest sampling density, which is close to the resolution of the model grid, further refining the finest analysis grid of the MGA cannot provide additional useful information. In contrast, too few levels of the MGA cannot sufficiently reduce the analysis error. Results of other experiments with different a values also demonstrate that 7 is a nearly optimal value of LMGA (not shown). In the following experiments of the adaptive EnKF-MGA method, LMGA is set to 7. Note that the observation sampling density may be high in some areas in reality. In such cases, according to the above selection criterion, LMGA will be set to a large value, causing the resolution of the finest grid to be much higher than that of the model grids. When the analysis of the MGA on the fine grid is projected onto the coarser model grids, the small-scale (smaller than the model grid scale) information will be lost rapidly. In addition, overly large LMGA will also significantly increase the computational cost. Thus, LMGA should be determined by both observation sampling density and the resolution of the model grids in actual data assimilation. Generally speaking, LMGA should be set to a value corresponding to the finest grid, the resolution of which is close to the smaller value of the highest model resolution and the largest observation sampling density.
Fig. 4.
Fig. 4.

(a) Sensitivity of the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction with respect to the number of grid levels of the MGA in the adaptive EnKF-MGA method for a = 250 km. The vertical line at each value of the number of grid levels represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. (b) Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for a = 250 km with 0 (black) and 7 (blue) grid levels in the adaptive EnKF-MGA method. Note that the adaptive EnKF-MGA reduces to the EnKF without inflation when the number of grid levels is 0.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

b. Performance of the MGA

We investigate the performance of the MGA from the following four aspects in this section.

First, we examine whether the MGA can further reduce the error produced by the EnKF without inflation at each analysis step. As in W14, we use the same three error statistics, which are as follows: 1) the RMSE of the analysis ensemble mean of produced by the adaptive EnKF-MGA method [RMSEEnKF-MGA; see Eq. (34) in W14]; 2) the RMSE of the analysis ensemble mean of generated by the first EnKF step in the adaptive EnKF-MGA method [RMSEEnKF, see Eq. (35) in W14]; and 3) the difference between RMSEEnKF-MGA and RMSEEnKF (i.e., RMSEEnKF-MGA − RMSEEnKF). A negative difference means that the MGA is valid.

Figure 5 shows the time series of the RMSE differences (106 m2 s−1) for a = 250 km (black) and a = 2500 km (blue), where the dashed line means no difference. The MGA can rapidly correct the model state during the spinup period, which is about 10 model days. After the spinup, the MGA can maintain the error of the model state at an equilibrium, which does not mean that the MGA is invalid. If the MGA is removed at a certain model step, the error of the model state will rebound to the level of the EnKF without inflation. To verify the correctness of the inference, we perform a test experiment of the adaptive EnKF-MGA, which artificially shuts off the MGA on the 40th model day for a = 250 km. Figure 6 plots the time series of the RMSE (106 m2 s−1) of the prior ensemble mean of for the standard EnKF (black), the adaptive EnKF-MGA (red), and the tested EnKF-MGA (blue). Once the MGA is switched off, the adaptive EnKF-MGA will reduce to the EnKF without inflation. Note that the standard EnKF produces larger errors than the adaptive EnKF-MGA during the whole assimilation period (cf. the red curve to the black curve in Fig. 6). Thus, once the MGA is manually shut off, the analysis quality of the adaptive EnKF-MGA will degrade to the level of the EnKF without inflation. In other words, the MGA should not be shut off even after the spinup period of the adaptive EnKF-MGA.

Fig. 5.
Fig. 5.

Time series of RMSE differences (106 m2 s−1; computed by RMSEEnKF-MGA − RMSEEnKF) for a = 250 km (black) and a = 2500 km (blue), where the dashed line represents no difference. Here, RMSEEnKF-MGA and RMSEEnKF represent the RMSEs of the analysis ensemble mean of the streamfunction for the adaptive EnKF-MGA method and for the first EnKF step in the adaptive EnKF-MGA method, respectively.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Fig. 6.
Fig. 6.

Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for the standard EnKF (black), the adaptive EnKF-MGA (red), and the tested EnKF-MGA (blue), which artificially shuts off the MGA at the 40th model day for a = 250 km.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Second, to check whether the MGA can extract the multiscale information from the observational residual, we take the results of the first data assimilation cycle as an instance. We also adopt the following two quantities used in W14: 1) the “true” difference , which indicates the difference between the truth and the analysis ensemble mean of produced by the first EnKF step in the adaptive EnKF-MGA [see Eq. (37) in W14]; and 2) the relative difference , which represents the difference between the absolute error of the analysis ensemble mean of produced by the adaptive EnKF-MGA and that of the analysis ensemble mean of produced by the adaptive EnKF-MGA before the MGA step is run [see Eq. (38) in W14]. A negative (positive) at a grid point means that the MGA is valid (invalid) there. Figure 7 shows the results of (Fig. 7a), observational residuals (Fig. 7b) located at the observational sites, the analysis solution of the MGA (Fig. 7c), and (Fig. 7d) for a = 250 km. Apparently, contains multiscale information, which is much stronger than the standard deviation (106 m2 s−1) of the observational error, causing the pattern in the observational residual to look like . Thus, the observational innovation (i.e., the observational residual) for the first grid level defined by Eq. (16) is signal dominant. With the MGA approach, the multiscale signals implied in the observational residual can be appropriately retrieved (Fig. 7c). From Fig. 7d, the MGA can further refine the model state where the signal of is strong: that is, the darker blue in Fig. 7d happens in the same areas as the darker red or blue areas in Fig. 7a. Figure 8 plots the results of the MGA for the sum of the first four (Fig. 8a), the sum of the first five (Fig. 8b), the sum of the first six (Fig. 8c), and the sum of all seven grid levels (Fig. 8d), where the dots indicate the analysis grids for each grid level in the MGA. Note that Fig. 7c is the same as Fig. 8d, but the latter shows the model grids. Through gradually refining the analysis grid, the MGA can sequentially extract the multiscale information from longwave to shortwave. Thus, the MGA here plays a similar role as the MSA in W14.

Fig. 7.
Fig. 7.

Results (106 m2 s−1) of the adaptive EnKF-MGA method at the first data assimilation cycle for a = 250 km. (a) The true difference defined in the text; (b) observational residuals; (c) analysis result of the MGA; and (d) the relative difference defined in the text, where the black curve indicates the zero contour. Note that all panels use the same shading scale.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Fig. 8.
Fig. 8.

Results (106 m2 s−1) of the MGA in the adaptive EnKF-MGA at the first data assimilation cycle for a = 250 km. (a) Result of the sum of the first four levels; (b) result of the sum of the first five levels; (c) result of the sum of the first six levels; and (d) result of the sum of all seven levels. Note that all panels adopt the same shading scale, and the dots represent the analysis grids.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Third, we check the time series of RMSEres [Eq. (22)], because this parameter acts as the switch to invoke the MGA. Figure 9 shows the time series of RMSEres (106 m2 s−1) for a = 250 km (Fig. 9a), a = 1500 km (Fig. 9b), and a = 2500 km (Fig. 9c), where the dashed line represents the critical value of 1.04. For a = 250 and 2500 km, the MGA is frequently activated. However, for a = 1500 km, the MGA is rarely called, justifying that the SNR of the observational residual is low. Thus, the assimilation quality of the adaptive EnKF-MGA method is nearly the same as that of the EnKF without inflation. Note that the activation of the MGA is completely adaptive rather than manual.

Fig. 9.
Fig. 9.

Time series of the RMSE (denoted as RMSEres; 106 m2 s−1) between observations and interpolated analysis ensemble mean of the first EnKF step in the adaptive EnKF-MGA method for (a) a = 250 km, (b) a = 1500 km, and (c) a = 2500 km. The dashed line represents the critical value of 1.04.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Last, because of the existence of the observational error in the observational residual, the analysis quality of the MGA strongly depends on the SNR of the observational residual. Here, taking the results of the adaptive method with a = 250 and 1500 km at 1800 UTC on model day 103 as examples, we analyze the SNR of the observational residuals. Figure 10 plots the spatial distributions of the observational residual (106 m2 s−1; Fig. 10a), the observational error (106 m2 s−1; Fig. 10b), the analysis error (106 m2 s−1; Fig. 10c) (in the observation space) of the first EnKF step in the adaptive EnKF-MGA, and the SNR for a = 250 km (Fig. 10d). Comparison between Figs. 10a–c illustrates that the observational residual is dominated by the analysis error rather than by the observational error, especially in area B and area C, where observations are sparsely distributed. The RMSEres in this case is 1.11 × 106 m2 s−1, which is larger than the threshold θ of 1.04 × 106 m2 s−1. Thus, the MGA will be adaptively triggered. Figure 10d verifies that the SNRs are greater than 1.0 (the black contour in Fig. 10d) in most places, especially in area B and area C. Under this circumstance, the MGA will further refine the quality of the analysis ensemble mean of the first EnKF step (not shown). Figure 11 shows the same results as Fig. 10, but for a = 1500 km. Comparison between Figs. 11a–c reveals that the observational residual is dominated by the observational error, causing the SNRs in most places to be smaller than 1.0. The RMSEres here is 0.98 × 106 m2 s−1, which automatically switches off the MGA. Although the above results are snapshots, the same conclusions can be drawn from other cases (not shown). Thus, the threshold is an effective criterion to measure whether the observational residual is signal dominant or not.

Fig. 10.
Fig. 10.

Spatial distributions of (a) the observational residual (106 m2 s−1), (b) the observational error (106 m2 s−1), (c) the analysis error (106 m2 s−1) (in the observation space) of the first EnKF step in the adaptive EnKF-MGA, and (d) the signal-to-noise ratio defined as the absolute value of the quotient between the analysis error and the observational error for a = 250 km at 1800 UTC on model day 103. The black contour in (d) indicates the value of 1.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Fig. 11.
Fig. 11.

As in Fig. 10, but for a = 1500 km. (b) As in Fig. 10b, but with a different shading scale.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

c. Comparison with the standard EnKF

In this section, we compare the performance of the adaptive EnKF-MGA with that of the standard EnKF method from the following three aspects.

First, we investigate the dependence of the time-mean RMSE of the prior ensemble mean of the streamfunction on a. The black and red curves in Fig. 12 show the results of the standard EnKF and the adaptive EnKF-MGA, respectively. For comparison, we also plot the results (the dashed curve) of the EnKF without inflation. Note that the overly large assimilation errors of the EnKF without inflation for a larger than 2250 km are not shown in Fig. 12 and that the standard EnKF only works for a smaller than 4000 km. The vertical line at each a represents the ±ζ bounds of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. If the error produced by method A falls out of the ±ζ bounds of the error produced by method B, we define that A is significantly different from B.

Fig. 12.
Fig. 12.

Dependences of the EnKF-MSA (green), the adaptive EnKF-MGA (red), the standard EnKF (black), and the EnKF without inflation (dashed) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. Note that no inflation is introduced into the EnKF-MSA and the adaptive EnKF-MGA methods.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

For small a (say 250 km), there is no overlap between the ±ζ bound of the time-mean RMSE for the standard EnKF and that for the adaptive EnKF-MGA, demonstrating that the assimilation quality of the adaptive EnKF-MGA is significantly better than that of the standard EnKF.

For moderate a (between 500 and 1750 km), the adaptive EnKF-MGA reduces to the EnKF without inflation because the MGA is rarely called. The ±ζ bound of the time-mean RMSE for the adaptive method is nearly overlapped by that for the standard EnKF, justifying that the difference between the adaptive EnKF-MGA and the EnKF without inflation is not significant.

For relatively large a (between 2000 and 3500 km), error bars show that the adaptive EnKF-MGA is much better than the EnKF without inflation but worse than the standard EnKF. According to Fig. 9c, the MGA is frequently called by the adaptive method for these a values: that is, the analysis ensemble mean of the first EnKF step in the adaptive method is often adjusted by the MGA. Under this circumstance, the analysis ensemble perturbation of the first EnKF step in the adaptive method should also be consistently adjusted. Thus, there are two possible reasons that cause the inferior performance of the adaptive EnKF-MGA compared to that of the standard EnKF: one is the lack of variance inflation, and the other is the preservation of the ensemble perturbations. We will discuss which one is the dominant factor in section 5.

For overly large a (say 3750 km), the RMSE produced by the adaptive method falls out of the ±ζ bound produced by the standard EnKF, proving that there is significant difference between the adaptive method and the standard EnKF, and the former method is better than the latter method. Thus, the adaptive EnKF-MGA can generally weaken the dependence of the assimilation quality on a. Besides, the adaptive EnKF-MGA works for a broader range of a than the standard EnKF. For example, the adaptive EnKF-MGA produces a small assimilation error for a = 4000 km, while the standard EnKF does not work in this case. We even performed an extreme experiment of the adaptive EnKF-MGA, which applied the MGA to an unlocalized EnKF: that is, all observations were allowed to impact global model grids during the first EnKF step in the adaptive EnKF-MGA. Results showed that the EnKF system can run without localization when it is used with the MGA, though the error is very large (i.e., the time-mean RMSE = 2.4 × 106 m2 s−1).

Second, taking three typical values of a (i.e., 250, 1500, and 3750 km) as examples, we conduct error analysis for the standard EnKF and the adaptive EnKF-MGA data assimilation schemes. Figure 13 shows the RMSE time series of the prior ensemble mean of the streamfunction for a = 250 km (Fig. 13a), a = 1500 km (Fig. 13b), and a = 3750 km (Fig. 13c), where the black, red, and green curves represent the results of the standard EnKF, the adaptive EnKF-MGA and the EnKF-MSA methods, respectively. For overly small or overly large values of a, the error produced by the adaptive EnKF-MGA is smaller than that produced by the standard EnKF, and the spinup period of the data assimilation is shortened. Although the adaptive EnKF-MGA method is worse than the standard EnKF when a is moderate, the difference looks small. Figure 14 gives the spinup periods of the standard EnKF (black) and the adaptive EnKF-MGA (red) methods for different a values. If we roughly average the spinup periods for small (250 and 500 km) and large (larger than 2000 km) values of a, the adaptive EnKF-MGA can shorten the spinup period of the standard EnKF by 53%.

Fig. 13.
Fig. 13.

Time series of the RMSE of the prior ensemble mean of the streamfunction for (a) a = 250 km, (b) a = 1500 km, and (c) a = 3750 km, where the curves represent the results of the standard EnKF (black), the adaptive EnKF-MGA (red), and the EnKF-MSA methods (green).

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Fig. 14.
Fig. 14.

Spinup periods of the standard EnKF (black) and the adaptive EnKF-MGA (red) for different a (i.e., GC half-width).

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Last, we investigate the spatial distributions of the RMSE of the prior ensemble mean of the streamfunction for the standard EnKF (Figs. 15a,d,g) and the adaptive EnKF-MGA (Figs. 15b,e,h) methods, taking a = 250 km (Figs. 15a,b), a = 1500 km (Figs. 15d,e), and a = 3750 km (Figs. 15g,h) as examples. Here, RMSE is defined as
e27
where (i, j) indexes the model grid. For a = 250 km and a = 3750 km, the adaptive EnKF-MGA method can greatly reduce the errors in area B and area C compared to the standard EnKF. This indicates that the adaptive EnKF-MGA is superior to the standard EnKF in sparsely observed areas for extreme a values. For a = 1500 km, the adaptive EnKF-MGA enhances the global errors.
Fig. 15.
Fig. 15.

Spatial distributions of RMSEs (106 m2 s−1) of the prior ensemble mean of the streamfunction for (left) the standard EnKF, (middle) the adaptive EnKF-MGA, and (right) the EnKF-MSA methods with (a)–(c) a = 250 km, (d)–(f) a = 1500 km, and (g)–(i) a = 3750 km.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

In summary, the adaptive EnKF-MGA method has four advantages over the standard EnKF in terms of assimilation quality: smaller assimilation errors and shorter spinup periods for extreme a values, weaker dependence on a, and a broader application range of a. These merits are shared by the EnKF-MSA method. It should be mentioned that the EnKF-MSA was compared to the EnKF without inflation in W14. Unlike for the EnKF-MSA method, the adaptive EnKF-MGA can reduce to the EnKF without inflation, which is better than the EnKF-MSA (the green curve in Fig. 12) for moderate a values.

d. Comparison with the EnKF-MSA

In this section, we compare the performance of the adaptive EnKF-MGA to that of the EnKF-MSA on the assimilation quality based on Fig. 12, in which the green curve shows the results of the EnKF-MSA. For moderate a values, the RMSE produced by the adaptive method (the red curve in Fig. 12) is smaller than the −ζ bound produced by the EnKF-MSA, demonstrating that the adaptive EnKF-MGA is better than the EnKF-MSA that triggers the MSA at each analysis. Since the SNR of the observational residual is low (see Fig. 11d for the a = 1500-km case), it is hard for the MSA to effectively extract the analysis error (in the observation space) in the observational residual. Thus, the EnKF-MSA worsens the assimilation quality of the EnKF without inflation. This can also be demonstrated by the results of the EnKF-MSA in Fig. 13b (the green curve) and Fig. 15f for a = 1500 km.

For small a values (250 and 500 km), the ζ bound produced by the EnKF-MSA is smaller than the −ζ bound produced by the adaptive EnKF-MGA, demonstrating the EnKF-MSA is better than the adaptive EnKF-MGA. Taking a = 250 km as an instance, the green curve in Fig. 13a shows the RMSE time series, and Fig. 15c shows the spatial distribution of the RMSE of the prior ensemble mean of the streamfunction for the EnKF-MSA. Comparison between these results and those (see the red curve in Fig. 13a and Fig. 15b) produced by the adaptive EnKF-MGA also supports the above conclusion.

For large a values (over 2000 km), because the ±ζ bound produced by the adaptive EnKF-MGA (the EnKF-MSA) covers the RMSE produced by the EnKF-MSA (the adaptive EnKF-MGA), the superiority of the EnKF-MSA over the adaptive EnKF-MGA is not significant. Taking a = 3750 km for example, the green curve in Fig. 13c shows the RMSE time series, and Fig. 15i shows the spatial distribution of the RMSE of the prior ensemble mean of the streamfunction for the EnKF-MSA. Comparison between these results and those (see the red curve in Fig. 13c and Fig. 15h) produced by the adaptive EnKF-MGA also justifies that there is no significant difference between the RMSE produced by the EnKF-MSA and that produced by the adaptive EnKF-MGA.

Thus, the adaptive EnKF-MGA has an incremental improvement over the EnKF-MSA for moderate a values. In contrast, the EnKF-MSA is superior to the adaptive EnKF-MGA for small a values. However, it should be noted that this superiority is based on specific selections of a(l) and LMSA in the MSA, which are tuned, rather than adaptively determined.

e. Computational cost

In this section, we simply use the wall-clock time (in minutes) of an experiment to measure the computational cost. Figure 16 shows the computational costs of the standard EnKF (black), the adaptive EnKF-MGA (red), the MGA (red dashed), the EnKF-MSA (green), and the MSA (green dashed) algorithms. All experiments are sequentially conducted on the same computational platform. Here, the computational cost of the MGA (MSA) is computed as the difference between the counterparts of the adaptive EnKF-MGA (the EnKF-MSA) and the standard EnKF.

Fig. 16.
Fig. 16.

Variations of computational costs (min) with respect to a (km) for the standard EnKF (black), the adaptive EnKF-MGA (red), the EnKF-MSA (green), the MGA (red dashed), and the MSA (green dashed).

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

The adaptive EnKF-MGA requires only slightly more wall-clock time compared to the standard EnKF, especially for moderate impact radii, which only trigger the MGA at a handful of times. If we calculate the average value of the computational costs for all impact radii, the computational cost of the MGA (2.1 min) is about 6% of that of the standard EnKF (37.0 min). Thus, relative to the computational expense of the standard EnKF, the cost of the MGA is almost negligible. In contrast, the EnKF-MSA increases the computational cost of the standard EnKF by 76% (from 37.0 to 65.2 min). The MGA can reduce the computational cost of the MSA by 93% (from 28.2 to 2.1 min). Results here are consistent with the analysis of the computational cost of the MSA in section 2c and of the MGA in section 2f. Thus, the MGA is much more computationally effective than the MSA.

5. Summary and discussion

The fixed covariance localization is often used to suppress the spurious long-distance correlation evaluated by a finite ensemble in EnKF. Although fixed covariance localization can greatly improve analysis quality, it has significant limitations, including insufficient longwave information with a small cutoff distance, contaminated analysis states with a large cutoff distance, and nearly unavailable optimal cutoff distance. W14 presented an EnKF and MSA hybrid method to make up for the above demerits. However, the compensatory method has some disadvantages. For example, the computational cost of the MSA is huge for high-resolution and/or high-dimension numerical models and dense observing systems; and the assimilation quality of the compensatory method is worse than the EnKF without inflation for moderate impact radii. To avoid these issues, we presented an adaptive EnKF and MGA hybrid method in this paper. At each analysis step, after the EnKF without inflation is completed, a threshold, which depends on the observing system, is used to decide whether to activate the MGA or not. Similar to the MSA algorithm, the MGA technique is used to extract multiscale information from observational residuals (the differences between observations and interpolated analysis ensemble means produced by the first EnKF step) by gradually refining the analysis grid. The analysis of the MGA is added to the analysis ensemble mean of the first EnKF step to form the final analysis ensemble mean. Then the analysis ensemble perturbations produced by the first EnKF step are added to the final analysis ensemble mean to form the final analysis ensemble members. Within a biased twin experiment framework consisting of a global barotropic spectral model and an idealized observing system, the performance of the proposed method was examined.

There are four advantages of the adaptive EnKF-MGA over the standard EnKF. First, the adaptive EnKF-MGA can produce smaller assimilation errors for overly large or overly small a values. Second, the adaptive EnKF-MGA can shorten the spinup period of the standard EnKF by 53% for both small and large a values. Third, the adaptive EnKF-MGA has less dependence on a than the standard EnKF. Last, the adaptive EnKF-MGA works for a broader range of a than the standard EnKF. For the twin experiments in this study, the EnKF system can run without localization when it is used with the MGA, though the error is very large. At present, although several adaptive localization schemes (e.g., Anderson 2007) have been presented, the fixed localization scheme is still widely used in some operational centers that establish an ensemble data assimilation system. Because of the flow-dependent nature of model states, it is difficult for them to set a fixed impact radius working for all times and spaces. Under this circumstance, the MGA method appears to be a potential cost-effective means of adjusting the output of an EnKF.

There are two demerits of the adaptive EnKF-MGA over the standard EnKF. On the one hand, for moderate a values, because of the absence of the variance inflation and the low SNR of the observational residual, the adaptive EnKF-MGA automatically reduces to the EnKF without inflation, which is worse than the standard EnKF. This demerit is expected to be corrected through introducing variance inflation. On the other hand, for relatively large a values, the adaptive EnKF-MGA is much better than the EnKF without inflation but worse than the standard EnKF. As we analyzed in section 4c, there are two possible reasons causing the above disadvantages of the adaptive EnKF-MGA over the standard EnKF. One is the absence of variance inflation in the first EnKF step of the adaptive EnKF-MGA. The other is not dealing with the ensemble perturbation. To figure out which is the dominant causation, we also apply the multiplicative variance inflation to the adaptive EnKF-MGA to find the optimal inflation factor (the red curve in Fig. 3) for each a value. Note that the variance inflation is only applied to the analysis step whose prior analysis step does not trigger the MGA. The inflation factors for the adaptive EnKF-MGA experiments are much smaller than those for the standard EnKF (the black curve in Fig. 3). The reason is that, once the analysis ensemble mean of the first EnKF step is improved by the MGA, the ensemble spread of the analysis ensemble should be reduced. Thus, the inflation needed for the standard EnKF is reduced. Without consistently adjusting the ensemble perturbations, the optimal inflation factor is also reduced. The red dashed curve in Fig. 17 shows the dependence of the assimilation RMSE on a for the adaptive EnKF-MGA with inflation. For comparison, results of the adaptive EnKF-MGA (the red curve) and the standard EnKF (the black curve) are also presented. The red and black curves here are the same as those in Fig. 12. Comparison between the red curve and the red dashed curve indicates that the inflation can improve the performance of the adaptive EnKF-MGA to approximate the standard EnKF for moderate a values, which rarely call the MGA. The RMSE produced by the adaptive EnKF-MGA with inflation is smaller than the −ζ bound produced by the adaptive EnKF-MGA, demonstrating that inflation can significantly improve the assimilation quality of the adaptive EnKF-MGA for moderate a values. For relatively large a values, there is almost no difference between the RMSE and its bound produced by the adaptive EnKF-MGA and those produced by the adaptive EnKF-MGA with inflation, justifying that the inflation is invalid when the MGA is frequently called. This demonstrates that the lack of consistent adjustment of the ensemble perturbations is the main cause of the inferior performance of the adaptive EnKF-MGA. This issue should be addressed in future work. In addition, in the current version of the adaptive EnKF-MGA, the computational cost of the MGA is negligible relative to that of the standard EnKF.

Fig. 17.
Fig. 17.

Dependences of the adaptive EnKF-MGA with (red dashed) and without (red) inflation, and the standard EnKF (black) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE.

Citation: Monthly Weather Review 143, 11; 10.1175/MWR-D-15-0060.1

Comparison between the adaptive EnKF-MGA and the EnKF-MSA shows that the MGA can realize the basic functions of the MSA and that the adaptive EnKF-MGA has three superiorities over the EnKF-MSA. First and foremost, the MGA can reduce the computational cost of the MSA by 93%. Second, on the assimilation quality, the adaptive EnKF-MGA has an incremental improvement over the EnKF-MSA for moderate a values. That is, the adaptive EnKF-MGA automatically reduces to the EnKF without inflation, which is better than the EnKF-MSA for moderate a values. Last, on the selection of the assimilation parameter, the determination of the analysis grids of the MGA is straightforward and can be adaptive, while the a(l) and LMSA in the MSA have to be manually determined. When the values of a(l) and LMSA are properly selected, the assimilation errors produced by the EnKF-MSA may be smaller than those produced by the adaptive EnKF-MGA, which is like what the EnKF-MSA did in this study.

Some other challenges exist before the adaptive EnKF-MGA can be applied for actual weather and climate analysis. First, the MGA mainly serves as a mathematical tool. As we know, the coarser the grid, the larger the representative error of the observations. This effect should be included in (l). Also, the shape of the smoothing term could be further improved to have more physical meanings. Second, the model used in this study is fairly simple compared to realistic models that have highly nonlinear processes like condensation. This raises issues that may affect the performance of the adaptive EnKF-MGA. Simply shifting the whole ensemble according to the adaptive EnKF-MGA may introduce undesirable effects in more complex models. Can the proposed method beat the standard EnKF with a reasonable but not optimal length scale in GCMs? These problems would not be visible in the system studied here. Thus, the MGA should be applied to assimilate actual instrumental measurements into a sophisticated atmospheric, oceanic, or atmosphere–ocean coupled GCM to further verify the validity of the adaptive EnKF-MGA method. Last, for a large ensemble size, the optimal impact radius would increase, which reduces the loss of longwave information. Thus, the performance of the proposed method should also be investigated using large ensemble sizes for extreme impact radii.

Acknowledgments

This research is cosponsored by grants from the National Basic Research Program (2013CB430304), the National Natural Science Foundation (41306006, 41376015, 41376013, 41176003, and 41206178), the National High-Tech R&D Program (2013AA09A505), and Global Change and Air–Sea Interaction (GASI-01-01-12) of China.

REFERENCES

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    • Search Google Scholar
    • Export Citation

  • Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–126, doi:10.1016/j.physd.2006.11.008.

    • Search Google Scholar
    • Export Citation

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    • Export Citation

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    • Search Google Scholar
    • Export Citation

  • Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791–2808, doi:10.1175/1520-0493(2002)130<2791:ESBAME>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519–1535, doi:10.1175/2010MWR3570.1.

    • Search Google Scholar
    • Export Citation

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    • Search Google Scholar
    • Export Citation

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    • Search Google Scholar
    • Export Citation

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  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99–111, doi:10.1016/j.physd.2006.02.011.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2008: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72–83, doi:10.1111/j.1600-0870.2008.00361.x.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741–2758, doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., and L. L. Lei, 2013: Empirical localization of observation impact in ensemble Kalman filters. Mon. Wea. Rev., 141, 4140–4153, doi:10.1175/MWR-D-12-00330.1.

    • Search Google Scholar
    • Export Citation

  • Asselin, R., 1972: Frequency filter for time integrations. Mon. Wea. Rev., 100, 487–490, doi:10.1175/1520-0493(1972)100<0487:FFFTI>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Berre, L., and G. Desroziers, 2010: Filtering of background error variance and correlations by local spatial averaging: A review. Mon. Wea. Rev., 138, 3693–3720, doi:10.1175/2010MWR3111.1.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., and D. Hodyss, 2007: Flow adaptive moderation of spurious ensemble correlations and its use in ensemble-based data assimilation. Quart. J. Roy. Meteor. Soc., 133, 2029–2044, doi:10.1002/qj.169.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., and D. Hodyss, 2009a: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus, 61A, 84–96, doi:10.1111/j.1600-0870.2008.00371.x.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., and D. Hodyss, 2009b: Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere. Tellus, 61A, 97–111, doi:10.1111/j.1600-0870.2008.00372.x.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., and D. Hodyss, 2011: Adaptive ensemble covariance localization in ensemble 4D-VAR state estimation. Mon. Wea. Rev., 139, 1241–1255, doi:10.1175/2010MWR3403.1.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., D. Hodyss, P. Steinle, H. Sims, A. M. Clayton, A. C. Lorenc, D. M. Barker, and M. Buehner, 2011: Efficient ensemble covariance localization in variational data assimilation. Mon. Wea. Rev., 139, 573–580, doi:10.1175/2010MWR3405.1.

    • Search Google Scholar
    • Export Citation

  • Briggs, W. L., V. E. Henson, and S. F. McCormick, 2000: A Multigrid Tutorial. 2nd ed. Society for Industrial and Applied Mathematics, 193 pp.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 2007: Data Assimilation: The Ensemble Kalman Filter. Springer, 187 pp.

  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation

  • Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide, and B. R. Hunt, 2011: Balance and ensemble Kalman filter localization techniques. Mon. Wea. Rev., 139, 511–522, doi:10.1175/2010MWR3328.1.

    • Search Google Scholar
    • Export Citation

  • Haltiner, G. J., and R. T. Williams, 1980: Numerical Prediction and Dynamic Meteorology. 2nd ed. Wiley, 477 pp.

  • Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776–2790, doi:10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., H. L. Mitchell, and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137, 2126–2143, doi:10.1175/2008MWR2737.1.

    • Search Google Scholar
    • Export Citation

  • Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–126, doi:10.1016/j.physd.2006.11.008.

    • Search Google Scholar
    • Export Citation

  • Jun, M., I. Szunyogh, M. G. Genton, F. Zhang, and C. H. Bishop, 2011: A statistical investigation of the sensitivity of ensemble-based Kalman filters to covariance filtering. Mon. Wea. Rev., 139, 3036–3051, doi:10.1175/2011MWR3577.1.

    • Search Google Scholar
    • Export Citation

  • Lei, L. L., and J. L. Anderson, 2014: Empirical localization of observations for serial ensemble Kalman filter data assimilation in an atmospheric general circulation model. Mon. Wea. Rev., 142, 1835–1851, doi:10.1175/MWR-D-13-00288.1.

    • Search Google Scholar
    • Export Citation

  • Li, W., Y. Xie, Z. He, G. Han, K. Liu, J. Ma, and D. Li, 2008: Application of the multigrid data assimilation scheme to the China Seas’ temperature forecast. J. Atmos. Oceanic Technol., 25, 2106–2116, doi:10.1175/2008JTECHO510.1.

    • Search Google Scholar
    • Export Citation

  • Li, W., Y. Xie, S.-M. Deng, and Q. Wang, 2010: Application of the multigrid method to the two-dimensional Doppler radar radial velocity data assimilation. J. Atmos. Oceanic Technol., 27, 319–332, doi:10.1175/2009JTECHA1271.1.

    • Search Google Scholar
    • Export Citation

  • Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization. Math. Program., 45, 503–528, doi:10.1007/BF01589116.

    • Search Google Scholar
    • Export Citation

  • Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791–2808, doi:10.1175/1520-0493(2002)130<2791:ESBAME>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519–1535, doi:10.1175/2010MWR3570.1.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., and K. Kondo, 2013: A multi-scale localization approach to an ensemble Kalman filter. SOLA, 9, 170–173, doi:10.2151/sola.2013-038.

    • Search Google Scholar
    • Export Citation

  • Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415–428, doi:10.1111/j.1600-0870.2004.00076.x.

    • Search Google Scholar
    • Export Citation

  • Robert, A., 1969: The integration of a spectral model of the atmosphere by the implicit method. Proc. WMO/IUGG Symp. on Numerical Weather Prediction, Tokyo, Japan, Japan Meteorological Society, 19–24.

  • Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield, and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A, 113–130, doi:10.1111/j.1600-0870.2007.00274.x.

    • Search Google Scholar
    • Export Citation

  • Wu, X., W. Li, G. Han, S. Zhang, and X. Wang, 2014: A compensatory approach of the fixed localization in EnKF. Mon. Wea. Rev., 142, 3713–3733, doi:10.1175/MWR-D-13-00369.1.

    • Search Google Scholar
    • Export Citation

  • Xie, Y., S. Koch, J. McGinley, S. Albers, P. E. Bieringer, M. Wolfson, and M. Chan, 2011: A space–time multi-scale analysis system: A sequential variational analysis approach. Mon. Wea. Rev., 139, 1224–1240, doi:10.1175/2010MWR3338.1.

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  • Yang, S.-C., E. Kalnay, and B. R. Hunt, 2012: Handling nonlinearity in an ensemble Kalman filter: Experiments with the three-variable Lorenz model. Mon. Wea. Rev., 140, 2628–2646, doi:10.1175/MWR-D-11-00313.1.

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  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with ensemble Kalman filter. Mon. Wea. Rev., 132, 1238–1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

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  • Zhang, S., J. L. Anderson, A. Rosati, M. J. Harrison, S. P. Khare, and A. Wittenberg, 2004: Multiple time level adjustment for data assimilation. Tellus, 56A, 2–15, doi:10.1111/j.1600-0870.2004.00040.x.

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  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    Variation of the threshold used to trigger the MGA in the adaptive EnKF-MGA method with respect to the number of observations for 0.01 (red), 0.05 (black), and 0.10 (blue) significance levels. Note that the threshold is a function of the number of observations, the significance level, and the standard deviation of observational error that is set to a constant 106 m2 s−1 in this study.

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    Model grids (pluses) and observational locations (dots) in the twin experiment in this study. Labels A, B, and C represent three areas with different sampling densities of observations. The thick lines divide the three areas.

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    Variation of the optimal inflation factor with respect to the GC half-width (km) for the adaptive EnKF-MGA with inflation (red) and the standard EnKF (black) methods. Note that no inflation is used in the adaptive EnKF-MGA method in the main body of this study. Here, the result of the adaptive EnKF-MGA with inflation is used in section 5.

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    (a) Sensitivity of the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction with respect to the number of grid levels of the MGA in the adaptive EnKF-MGA method for a = 250 km. The vertical line at each value of the number of grid levels represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. (b) Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for a = 250 km with 0 (black) and 7 (blue) grid levels in the adaptive EnKF-MGA method. Note that the adaptive EnKF-MGA reduces to the EnKF without inflation when the number of grid levels is 0.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    Time series of RMSE differences (106 m2 s−1; computed by RMSEEnKF-MGA − RMSEEnKF) for a = 250 km (black) and a = 2500 km (blue), where the dashed line represents no difference. Here, RMSEEnKF-MGA and RMSEEnKF represent the RMSEs of the analysis ensemble mean of the streamfunction for the adaptive EnKF-MGA method and for the first EnKF step in the adaptive EnKF-MGA method, respectively.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for the standard EnKF (black), the adaptive EnKF-MGA (red), and the tested EnKF-MGA (blue), which artificially shuts off the MGA at the 40th model day for a = 250 km.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    Results (106 m2 s−1) of the adaptive EnKF-MGA method at the first data assimilation cycle for a = 250 km. (a) The true difference defined in the text; (b) observational residuals; (c) analysis result of the MGA; and (d) the relative difference defined in the text, where the black curve indicates the zero contour. Note that all panels use the same shading scale.

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    Results (106 m2 s−1) of the MGA in the adaptive EnKF-MGA at the first data assimilation cycle for a = 250 km. (a) Result of the sum of the first four levels; (b) result of the sum of the first five levels; (c) result of the sum of the first six levels; and (d) result of the sum of all seven levels. Note that all panels adopt the same shading scale, and the dots represent the analysis grids.

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    Time series of the RMSE (denoted as RMSEres; 106 m2 s−1) between observations and interpolated analysis ensemble mean of the first EnKF step in the adaptive EnKF-MGA method for (a) a = 250 km, (b) a = 1500 km, and (c) a = 2500 km. The dashed line represents the critical value of 1.04.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    Spatial distributions of (a) the observational residual (106 m2 s−1), (b) the observational error (106 m2 s−1), (c) the analysis error (106 m2 s−1) (in the observation space) of the first EnKF step in the adaptive EnKF-MGA, and (d) the signal-to-noise ratio defined as the absolute value of the quotient between the analysis error and the observational error for a = 250 km at 1800 UTC on model day 103. The black contour in (d) indicates the value of 1.

  • <sc>Fig</sc>. 11.
    View in gallery
    Fig. 11.

    As in Fig. 10, but for a = 1500 km. (b) As in Fig. 10b, but with a different shading scale.

  • <sc>Fig</sc>. 12.
    View in gallery
    Fig. 12.

    Dependences of the EnKF-MSA (green), the adaptive EnKF-MGA (red), the standard EnKF (black), and the EnKF without inflation (dashed) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. Note that no inflation is introduced into the EnKF-MSA and the adaptive EnKF-MGA methods.

  • <sc>Fig</sc>. 13.
    View in gallery
    Fig. 13.

    Time series of the RMSE of the prior ensemble mean of the streamfunction for (a) a = 250 km, (b) a = 1500 km, and (c) a = 3750 km, where the curves represent the results of the standard EnKF (black), the adaptive EnKF-MGA (red), and the EnKF-MSA methods (green).

  • <sc>Fig</sc>. 14.
    View in gallery
    Fig. 14.

    Spinup periods of the standard EnKF (black) and the adaptive EnKF-MGA (red) for different a (i.e., GC half-width).

  • <sc>Fig</sc>. 15.
    View in gallery
    Fig. 15.

    Spatial distributions of RMSEs (106 m2 s−1) of the prior ensemble mean of the streamfunction for (left) the standard EnKF, (middle) the adaptive EnKF-MGA, and (right) the EnKF-MSA methods with (a)–(c) a = 250 km, (d)–(f) a = 1500 km, and (g)–(i) a = 3750 km.

  • <sc>Fig</sc>. 16.
    View in gallery
    Fig. 16.

    Variations of computational costs (min) with respect to a (km) for the standard EnKF (black), the adaptive EnKF-MGA (red), the EnKF-MSA (green), the MGA (red dashed), and the MSA (green dashed).

  • <sc>Fig</sc>. 17.
    View in gallery
    Fig. 17.

    Dependences of the adaptive EnKF-MGA with (red dashed) and without (red) inflation, and the standard EnKF (black) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE.

Fig. 1.

Variation of the threshold used to trigger the MGA in the adaptive EnKF-MGA method with respect to the number of observations for 0.01 (red), 0.05 (black), and 0.10 (blue) significance levels. Note that the threshold is a function of the number of observations, the significance level, and the standard deviation of observational error that is set to a constant 106 m2 s−1 in this study.
Fig. 2.

Model grids (pluses) and observational locations (dots) in the twin experiment in this study. Labels A, B, and C represent three areas with different sampling densities of observations. The thick lines divide the three areas.
Fig. 3.

Variation of the optimal inflation factor with respect to the GC half-width (km) for the adaptive EnKF-MGA with inflation (red) and the standard EnKF (black) methods. Note that no inflation is used in the adaptive EnKF-MGA method in the main body of this study. Here, the result of the adaptive EnKF-MGA with inflation is used in section 5.
Fig. 4.

(a) Sensitivity of the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction with respect to the number of grid levels of the MGA in the adaptive EnKF-MGA method for a = 250 km. The vertical line at each value of the number of grid levels represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. (b) Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for a = 250 km with 0 (black) and 7 (blue) grid levels in the adaptive EnKF-MGA method. Note that the adaptive EnKF-MGA reduces to the EnKF without inflation when the number of grid levels is 0.
Fig. 5.

Time series of RMSE differences (106 m2 s−1; computed by RMSEEnKF-MGA − RMSEEnKF) for a = 250 km (black) and a = 2500 km (blue), where the dashed line represents no difference. Here, RMSEEnKF-MGA and RMSEEnKF represent the RMSEs of the analysis ensemble mean of the streamfunction for the adaptive EnKF-MGA method and for the first EnKF step in the adaptive EnKF-MGA method, respectively.
Fig. 6.

Time series of the RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction for the standard EnKF (black), the adaptive EnKF-MGA (red), and the tested EnKF-MGA (blue), which artificially shuts off the MGA at the 40th model day for a = 250 km.
Fig. 7.

Results (106 m2 s−1) of the adaptive EnKF-MGA method at the first data assimilation cycle for a = 250 km. (a) The true difference defined in the text; (b) observational residuals; (c) analysis result of the MGA; and (d) the relative difference defined in the text, where the black curve indicates the zero contour. Note that all panels use the same shading scale.
Fig. 8.

Results (106 m2 s−1) of the MGA in the adaptive EnKF-MGA at the first data assimilation cycle for a = 250 km. (a) Result of the sum of the first four levels; (b) result of the sum of the first five levels; (c) result of the sum of the first six levels; and (d) result of the sum of all seven levels. Note that all panels adopt the same shading scale, and the dots represent the analysis grids.
Fig. 9.

Time series of the RMSE (denoted as RMSEres; 106 m2 s−1) between observations and interpolated analysis ensemble mean of the first EnKF step in the adaptive EnKF-MGA method for (a) a = 250 km, (b) a = 1500 km, and (c) a = 2500 km. The dashed line represents the critical value of 1.04.
Fig. 10.

Spatial distributions of (a) the observational residual (106 m2 s−1), (b) the observational error (106 m2 s−1), (c) the analysis error (106 m2 s−1) (in the observation space) of the first EnKF step in the adaptive EnKF-MGA, and (d) the signal-to-noise ratio defined as the absolute value of the quotient between the analysis error and the observational error for a = 250 km at 1800 UTC on model day 103. The black contour in (d) indicates the value of 1.
Fig. 11.

As in Fig. 10, but for a = 1500 km. (b) As in Fig. 10b, but with a different shading scale.
Fig. 12.

Dependences of the EnKF-MSA (green), the adaptive EnKF-MGA (red), the standard EnKF (black), and the EnKF without inflation (dashed) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE. Note that no inflation is introduced into the EnKF-MSA and the adaptive EnKF-MGA methods.
Fig. 13.

Time series of the RMSE of the prior ensemble mean of the streamfunction for (a) a = 250 km, (b) a = 1500 km, and (c) a = 3750 km, where the curves represent the results of the standard EnKF (black), the adaptive EnKF-MGA (red), and the EnKF-MSA methods (green).
Fig. 14.

Spinup periods of the standard EnKF (black) and the adaptive EnKF-MGA (red) for different a (i.e., GC half-width).
Fig. 15.

Spatial distributions of RMSEs (106 m2 s−1) of the prior ensemble mean of the streamfunction for (left) the standard EnKF, (middle) the adaptive EnKF-MGA, and (right) the EnKF-MSA methods with (a)–(c) a = 250 km, (d)–(f) a = 1500 km, and (g)–(i) a = 3750 km.
Fig. 16.

Variations of computational costs (min) with respect to a (km) for the standard EnKF (black), the adaptive EnKF-MGA (red), the EnKF-MSA (green), the MGA (red dashed), and the MSA (green dashed).
Fig. 17.

Dependences of the adaptive EnKF-MGA with (red dashed) and without (red) inflation, and the standard EnKF (black) methods on a. The y axis represents the time-mean RMSE (106 m2 s−1) of the prior ensemble mean of the streamfunction. The vertical line at each a value represents the ±ζ bound of the time-mean RMSE, where ζ represents the standard deviation of the RMSE.
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