2.1. The ALADIN-France LAM

The ensemble experiments which will be described in Section 2.3 have been performed using the ALADIN-France data assimilation and forecasting system. ALADIN-France is a high-resolution limited-area spectral model (e.g. Horanyi et al., 1996) with bi-periodic extension of the computation domain. It uses elliptical truncation of double-Fourier series, semi-implicit semi-Lagrangian integration scheme, fourth order horizontal diffusion and digital filter initialisation (DFI). It is run over a European domain centred over France, with a Lambert grid, a 7.5-km horizontal resolution (in both x and y directions) and 70 vertical levels from the surface up to 0.1 hPa.

It is built on the basis of the global model IFS/ARPEGE and keeps the same dynamics, physics and vertical coordinate (a terrain-following pressure hybrid coordinate). The primitive hydrostatic equations are solved for the horizontal wind components, temperature, specific humidity and surface pressure (e.g. Bubnova et al., 1995; Cordoneanu and Geleyn, 1998). ALADIN-France uses a 6-h 3D-Var data assimilation scheme (Fischer et al., 2005), which assimilates a wide range of conventional observations (e.g. SYNOP, BUOY, TEMP and PILOT) and of remote sensing data (such as AMSU-A and B, HIRS, MHS, AMV, SEVIRI, AIRS and IASI data). ALADIN-France was used at Météo-France operationally from 1996 to 2012, and other ALADIN versions are still used operationally in several NWP centres in Europe and North Africa.

2.2. LBCs for ALADIN-France

As any LAM, ALADIN needs information about the state of the atmosphere outside of its integration domain. Figure 1 shows the horizontal domain of ALADIN-France. The central zone represents the region of meteorological interest, where the forecast is fully adapted to resolved small-scale conditions. The width of this zone corresponds to 381 grid points. The lateral zone represents the coupling zone where a large-scale solution, generally obtained from ARPEGE, is mixed with the solution resulting from the LAM integration. LBCs are updated every 3 h and linearly interpolated in time between available updates in order to have LBC forcing field at each time step of ALADIN. These values on the boundaries are needed for solving the model system equations. The width of this zone corresponds to eight grid points. The one-way nesting is achieved by using a Davies relaxation scheme (Davies, 1976, 1983; Davies and Turner, 1977).

Initial LBCs (i.e. specified at 0-h forecast range) are determined by the ALADIN-France 3D-Var analysis itself, while the LBC specified at 3- and 6-h forecast ranges correspond to 3- and 6-h forecasts of the global ARPEGE system, which are interpolated onto the ALADIN-France grid. Using analysis fields as initial LBC data in all experiments avoids generating spurious spin-up behaviour in the early forecast ranges, as shown in Fischer and Auger (2011). This choice is also consistent with more recent experimental findings for the AROME assimilation at Météo-France (Pierre Brousseau, personal communication). An incremental DFI is additionally applied to ALADIN-France initial conditions, following the study of Fischer and Auger, 2011.

2.3. Ensembles description

Three regional ensemble data assimilation experiments are performed using the ALADIN-France model. Each ensemble consists of six ALADIN members, and it is run in the same way as described, for example, by Houtekamer et al. (1996), Fisher (2003) and Berre et al. (2006). For each member of each ensemble, and for each analysis step, observation perturbations ${\mathbf{\epsilon }}_o$ are added to observation values, in order to simulate observation errors. These observation perturbations ${\mathbf{\epsilon }}_o$ are provided by random draws of the observation error covariance matrix $\mathbf{R}:{\mathbf{\epsilon }}_o={\mathbf{R}}^{\frac{1}{2}}\zeta$, where ζ are random Gaussian draws which are spatially uncorrelated and which have zero mean and unit variance (i.e. , I being the identity matrix in observation space). In addition to these explicit perturbations of observations, the ensembles use implicit perturbations of the background, provided by the perturbation evolution during the 6-h data assimilation cycle. Apart from the use of perturbed LBCs, the ensemble simulations do not use explicit model perturbations (which amounts to using a perfect model assumption). Likely, this will be important to consider in future studies, because model errors are expected to limit the sensitivity of ensemble spread to perturbations of LBCs, to some extent, as will be indicated also in Section 3.1.

The three ensembles are run from April 23, through May 10, 2010. This 18-d period is relatively short, but it has been found to be sufficiently long in order to diagnose several features which are either time-averaged (e.g. regarding spatial variations of spread and also its sensitivity to the amplitude of drawn perturbations) or time-varying (e.g. regarding the evolution of space-averaged spread but also some flow-dependent aspects during this period). The initial ALADIN-France background on April 23 at 00 UTC is taken from the operational deterministic system. This implies that initial background perturbations are equal to zero, and that the ensemble spread increases during the first data assimilation cycles before reaching stable values, as will be illustrated in Section 5.1.

Each of the three ensembles uses perturbed initial LBCs (i.e. at 0-h forecast range) which are determined by the corresponding ALADIN-France perturbed analyses of each member. The three considered ensembles differ with respect to the set of LBCs specified at 3- and 6-h forecast ranges:

1. The first ensemble, which will be called unperturbed LBC (ULBC), uses unperturbed 3- and 6-h LBCs which are the downscaled deterministic global forecasts of ARPEGE. Each ALADIN-France ensemble member thus receives the same 3- and 6-h LBC as the ALADIN-France deterministic configuration. The omission of such LBC perturbations amounts to assuming that there are no errors in these forecast LBCs which are used in the ALADIN-France data assimilation cycle.
2. The second ensemble has been run using 3- and 6-h LBCs taken from the global ensemble data assimilation AEARP, which has been operational at Météo-France since 2008 (Berre et al., 2007, 2009). It is made of six members with a global spectral truncation of T399 (i.e. a global uniform horizontal resolution of 50 km), 70 vertical levels and a 4D-Var assimilation scheme. This ensemble is considered as a reference for comparison purposes, and it will be referred to as GLBC.
3. The third ensemble uses perturbed 3- and 6-h LBCs, produced by adding LBC perturbations to the interpolated deterministic forecasts of ARPEGE, with LBC perturbations generated by using the same method as in El Ouaraini and Berre (2011) to create initial background perturbations: they are constructed from random draws of a specified error covariance matrix P_LBC, which is assumed to be representative of LBC errors: ${\mathbf{\epsilon }}^{LBC}={\mathbf{P}}_{LBC}^{\frac{1}{2}}\mathrm{\hspace{0.17em} }\eta$, where , I being here the identity matrix in model space, and ${\mathbf{\epsilon }}^{LBC}\mathrm{}~(0,{\mathbf{P}}_{LBC})$. In the present study, this specified covariance P_LBC has been simply constructed as a scaled version of the ALADIN-France background error covariance matrix $\mathbf{B}:{\mathbf{P}}_{LBC}^{\frac{1}{2}}= \alpha {\mathbf{B}}^{\frac{1}{2}}$, where α is a scaling factor whose value has been tuned to 0.3, in order to obtain roughly similar amplitudes of LBC perturbations as in the GLBC configuration. The sensitivity with respect to this amplitude factor α will be illustrated in Section 4.3. This ensemble will be called PLBC in the remainder of the paper. Moreover, it can be noticed that ULBC can be seen as a limit case of PLBC corresponding to α=0, which is also a way to provide information on sensitivity with respect to α in PLBC.

Note that the choice α=0.3 instead of a value close to 1 may reflect several aspects. Firstly, this may be related to effects of seasonal differences (as shown, e.g., in Monteiro and Berre, 2010), as the specified ALADIN-France matrix B has been computed over a winter period in January–February 2009, which is likely to be associated to larger background errors than in the spring period (April–May 2010) that is considered in this paper. Secondly, the relatively large values in the specified ALADIN-France matrix B may also suggest that these specified values were overestimated, which tends to be supported by the fact that such an overestimation has been diagnosed for the related ARPEGE system in 2009. Thirdly, P_LBC is supposed to represent ARPEGE analysis and short-range forecast errors, which are expected to have amplitudes that are partly different from ALADIN-France background errors, so that α is not really expected to be equal to 1.

Note also that the specified matrix B, and associated drawn lateral boundary perturbations, are partly flow-dependent. This corresponds to flow-dependent error standard deviations of vorticity provided by the global ARPEGE ensemble (Berre et al., 2007) and also to the use of flow-dependent non-linear and omega balances (Fisher, 2003).

In the next sections, associated error and perturbation equations are examined formally, and then the ensemble spread sensitivities with respect to LBC perturbations are studied experimentally for analyses and 6-h forecasts. Ensemble spread is computed using the following formula:

with ${\mathbf{\epsilon }}_n(i,j,z)={\tilde{x}}_n(i,j,z)-\overline{\tilde{x}}(z)$, where n is the ensemble index, N is the ensemble size, (i, j) are indices of horizontal position, z is the index of vertical position, ${\tilde{x}}_n$ is the model state of member n, and $\overline{\tilde{x}}$ is the model state of the ensemble mean $\left(\overline{\tilde{x}}=\frac{1}{N}\sum _{n=1}^N{\tilde{x}}_n\right)$.

3.1. Evolution of errors during successive analysis/forecast steps

The evolution of errors during a given analysis step (denoted by the index l) is given by the following equation of analysis errors e^a:

where ${\mathbf{e}}_l^b,{\mathbf{e}}_l^o$ are background and observation errors, respectively, K_l is the specified gain matrix, and H_l is the observation operator.

These analysis errors ${\mathbf{e}}_l^a$ evolve into forecast errors ${\mathbf{e}}_l^f$ according to:

where M_l is the actual (imperfect) 6-h forecast model operator (using imperfect LBCs and an approximate representation of dynamical and physical processes), while ${\mathbf{e}}_l^m$ is the 6-h-accumulated model error:

where ${\mathbf{M}}_{l\mathrm{\star }}^{DP\mathrm{\star }}$ is the perfect model operator, that is, using not only perfect LBCs (as denoted by the subscript $\mathrm{\star }$) but also a perfect representation of dynamical (D) and physical (P) processes (as denoted by the superscript $DP\mathrm{\star }$), and ${\mathbf{x}}_{l\mathrm{\star }}$ is the exact initial state for the considered lth analysis/forecast step.

This model error ${\mathbf{e}}_l^m$ can be expanded as the sum of two different terms, in order to distinguish LBC errors and other model errors:

where ${\mathbf{M}}_{l\mathrm{\star }}$ is the model operator based on perfect LBCs but on the actual (imperfect) representation of physics and dynamics. Equation (4) thus amounts to expressing model error ${\mathbf{e}}_l^m$ as the sum of LBC errors ${\mathbf{e}}_l^{LBC}$ and of model errors ${\mathbf{e}}_l^{DP}$, which are related to approximations in the dynamics and in the physics:

where ${\mathbf{e}}_l^{LBC}$ corresponds to the contribution of LBC errors during the considered 6-h forecast integration:

which is the difference between two forecast integrations starting from a perfect initial state ${\mathbf{x}}_{l\mathrm{\star }}$, but using either imperfect or perfect LBCs (respectively), while ${\mathbf{e}}_l^{DP}$ corresponds to accumulated model errors related to an imperfect representation of dynamical and physical processes:

By considering the cycling of this analysis/forecast process in data assimilation, it is possible to expand forecast errors for the ith cycle as follows, as a function of previous observation errors, LBC errors and other model errors (note also that ${\mathbf{e}}_i^b={\mathbf{e}}_{i-1}^f$, since the background for the analysis step i is a forecast issued from the previous analysis step i – 1):

where T₀=I can be seen as a weight matrix associated to errors introduced during the current ith analysis/forecast step [which corresponds to l=i in the sum above (eq. (8))], while for l between 0 and i – 1, T_i–l is a weight matrix assigned to errors added during the lth analysis/forecast step:

and ${\mathbf{e}}_0^b$ is the initial background error (i.e. the error associated to the very first background from which the LAM data assimilation cycle is initiated), whose weight matrix in eq. (8) corresponds to

Equations (8), (9) and (10) indicate that this operator T can be seen as a temporal propagation operator of previous errors, which are accumulated (and partly damped) during the data assimilation cycle. As studied in El Ouaraini and Berre (2011), T tends to act as a damping operator of previous error contributions, because eigenvalues of $\mathbf{I}-{\mathbf{K}}_{l\prime }{\mathbf{H}}_{l\prime }$ are smaller than 1 (as discussed in Daley, 1991, p. 127). This implies for instance that the influence of the initial background error becomes negligible after 3–4 d of cycling, in the context of the study of El Ouaraini and Berre (2011).

Moreover, the forecast error equation [eq. (8)] indicates that the forecast error e^f can be seen as the sum of contributions arising from observation errors e^o, LBC errors e^LBC and other model errors e^DP. Therefore, eq. (8) shows that the relative contribution of LBC errors to forecast errors depends on the relative amplitudes of observation errors, LBC errors and other model errors.

It also suggests that this relative LBC error contribution is likely to depend on the spatial coverage (and density) of the observation network. For instance, an observation network covering most of the LAM domain implies that the relative contribution of observation errors (compared to LBC errors) will be relatively large over the whole domain. Conversely, an extreme opposite configuration would be the case where the observation network would be reduced to a single observation (located in the centre of the domain for instance). In this extreme case, forecast errors ${\mathbf{e}}_i^f$ (which are expected to be much larger than with a regular and dense observation network) would essentially arise from the accumulation of LBC errors ${\mathbf{e}}_l^{LBC}$ and of other model errors ${\mathbf{e}}_l^{DP}$ during the data assimilation cycle, except in the neighbourhood of the single observation.

The relative LBC error contribution is also expected to depend on the quality of the observation network. On the one hand, eq. (8) indicates that forecast errors ${\mathbf{e}}_i^f$ are partly proportional to current observation errors ${\mathbf{e}}_i^o$ in particular. On the other hand, this is expected to be partly mitigated by the fact that eigenvalues of the gain K_i are smaller than 1 (as discussed in Daley, 1991, p. 127), and also by the fact that these gain coefficients become smaller when specified observation error variances are increased. Examining the formula of the variance of ${\mathbf{K}}_i\mathrm{\hspace{0.17em} }{\mathbf{e}}_i^o$ in a simple scalar case (see Appendix A) suggests that the contribution of observation errors tends to be maximum for observations whose quality is roughly similar to the background quality, and that this contribution decreases for accurate observations (because their noise becomes small) and also for inaccurate observations (because their weight in the analysis becomes very small). This suggests, in turn, that the LBC error contribution will be relatively large (compared with observation error contributions) in the case of accurate observations (because large LBC errors during the current forecast step will tend to predominate over small initial condition errors) and also in the case of inaccurate observations (because observation errors will be heavily damped by the analysis itself, while LBC errors will tend to accumulate during the data assimilation cycle; note also that background errors will be relatively large in such a configuration).

The effect of other model errors is more direct to some extent: eq. (8) indicates that, the larger the errors ${\mathbf{e}}_l^{DP}$ in the dynamical and physical schemes are, the smaller the relative contribution of LBC errors will be.

Last but not least, the appearance of the operator M in equations such as eqs. (6) and (9) reflects, for example, the influence of advective effects during each 6-h forecast integration: for instance, in the case of strong inflow, LBC errors will be advected towards a large part of the inner LAM domain.

3.2. Evolution of perturbations in the ensemble cycling

By analogy with the forecast error equation [eq. (8)], it is relatively easy to show that the equation of forecast perturbations ${\mathbf{\epsilon }}_i^f$, for the different LAM ensembles which are conducted using a perfect model assumption, can be written as follows:

where ${\mathbf{\epsilon }}_0^b$ is the initial background perturbation (which is equal to zero in the considered experiments, because all ensemble members start from the deterministic system), ${\mathbf{\epsilon }}_l^o$ are observation perturbations which are drawn from the specified observation error covariances, and ${\mathbf{\epsilon }}_l^{LBC}$ are LBC perturbations associated to the considered LAM ensemble configuration, whose equations will now be made explicit.

In order to derive equations of LBC perturbations, model states such as the LAM analysis and the LAM 6-h forecast will be denoted by ${\mathbf{x}}_{L_{a0}}$ and ${\mathbf{x}}_{L_{f6}}$, respectively: the L index refers to the LAM system, which can be distinguished from the G index referring to the global system that provides LBCs to the LAM forecasts; the α0 and f6 indices refer respectively to the analysis valid at initial time (akin to 0-h forecast range) and to the forecast valid at 6-h range.

In order to examine the perturbation evolution associated to the 3-h update of LBCs, the model operator M will be considered thereafter to correspond to either 3- or 6-h forecast integrations (as indicated by specific indices), and the LBCs that are used by the LAM model operator M will be indicated by two indices referring to the LBCs used at initial and final times: for instance, the 3-h forecast model operator to be applied to the analysis will be denoted by ${\mathbf{M}}_{L_{a0},G_{f3}}$, in order to indicate that the initial LBC is the LAM analysis ${\mathbf{x}}_{L_{a0}}$ and that the final LBC is the global 3-h forecast ${\mathbf{x}}_{G_{f3}}$ (after interpolation onto the LAM domain). Similarly, the subsequent 3-h forecast model operator to be applied to the LAM 3-h forecast, and which provides the 6-h forecast state, will be denoted by ${\mathbf{M}}_{G_{f3},G_{f6}}$, in order to express the use of the global 3-h forecast ${\mathbf{x}}_{G_{f3}}$ as the initial LBC and the global 6-h forecast ${\mathbf{x}}_{G_{f6}}$ as the final LBC. ${\mathbf{x}}_{L_{b0}}$ is the LAM background state, which is used during the considered analysis state.

Using these notations, the evolution of the LAM state during a given analysis/forecast cycle can be expressed by the following three equations for the deterministic LAM system:

With respect to the GLBC ensemble for instance, the same kind of equations can be written for the perturbed LAM state, denoted by ${\tilde{x}}_L$, where the tilde symbol reflects the perturbed feature of the state:

which allows equations of perturbations $\mathbf{\epsilon }=\tilde{x}-x$ to be derived:

Equations (12) and (13) can be combined in order to express the 6-h forecast perturbation as a function of analysis and LBC perturbations:

where ${\mathbf{M}}_{0,6}={\mathbf{M}}_{G_{f3},G_{f6}}{\mathbf{M}}_{L_{a0},G_{f3}}$ is the unperturbed 6-h forecast operator, and ${\mathbf{\epsilon }}_{0,6}^{LBC}$ corresponds to the contribution of LBC perturbations during the 6-h forecast integration:

where ${\mathbf{\epsilon }}_{0,3}^{LBC}=\left({\mathbf{M}}_{\widetilde{L_{a0}},\widetilde{G_{f3}}}-{\mathbf{M}}_{L_{a0},G_{f3}}\right){\tilde{x}}_{L_{a0}}$ is the 3-h forecast perturbation arising from the use of perturbed LBCs at both 0- and 3-h forecast ranges, and ${\mathbf{\epsilon }}_{3,6}^{LBC}=\left({\mathbf{M}}_{\widetilde{G_{f3}},\widetilde{G_{f6}}}-{\mathbf{M}}_{G_{f3},G_{f6}}\right){\tilde{x}}_{L_{f3}}$ is the subsequent 3-h forecast perturbation corresponding to the use of perturbed LBCs at 3- and 6-h forecast ranges.

This equation [eq. (14)] of the LBC perturbation ${\mathbf{\epsilon }}_{0,6}^{LBC}$ in GLBC is similar to the LBC error equation [eq. (6)], which can be further expanded as follows (while omitting the l index of the considered analysis/forecast step), as a function of LBC errors accumulated during the successive two 3-h forecast integrations (i.e. from 0 to 3 h and from 3 to 6 h, respectively):

where ${\mathbf{x}}_{{\mathrm{\star }}_0},{\mathbf{x}}_{{\mathrm{\star }}_3}$ are the exact states at 0- and 3-h ranges, and ${\mathbf{M}}_{{\mathrm{\star }}_0,{\mathrm{\star }}_3},{\mathbf{M}}_{{\mathrm{\star }}_3,{\mathrm{\star }}_6}$ are the corresponding model operators based on perfect LBCs.

Comparing eqs. (14) and (15) indicates that LBC errors are simulated in GLBC through the difference between two integrations that use the same initial condition but different LBCs. Moreover, it can also be noticed that this common initial condition corresponds to ${\tilde{x}}_{L_{a0}}$ in GLBC (with respect to 3-h accumulated LBC perturbations), whereas it may seem preferable to use ${\mathbf{x}}_{{\mathrm{\star }}_0}$ ideally, according to eq. (15). This difference can be seen as corresponding to an additional term, for example, in the 3-h LBC perturbation equation:

To the extent that the amplitude of the perturbation $({\tilde{x}}_{L_{a0}}-{\mathbf{x}}_{{\mathrm{\star }}_0})$ tends to be smaller than the amplitude of ${\mathbf{x}}_{{\mathrm{\star }}_0}$, this additional term $\left({\mathbf{M}}_{\widetilde{L_{a0}},\widetilde{G_{f3}}}-{\mathbf{M}}_{L_{a0},G_{f3}}\right)\left({\tilde{x}}_{L_{a0}}-{\mathbf{x}}_{{\mathrm{\star }}_0}\right)$ is expected to be a second order term in the LBC perturbation equation.

3.3. Evolution of analysis and forecast perturbations in the ULBC ensemble

The equations of state and perturbation evolution, such as (11), are similar in the ULBC ensemble, but the LBCs are partly different from those used in the GLBC ensemble, as expressed by the following equations of 3- and 6-h perturbed forecasts:

where the notation ${\mathbf{M}}_{\widetilde{L_{a0}},G_{f3}}$ refers to the fact that the 3-h forecast model in ULBC uses perturbed LBCs at initial time (provided by ${\tilde{x}}_{L_{a0}}$), and ULBC at final time (corresponding to ${\mathbf{x}}_{G_{f3}}$ at 3-h range). By using similar derivations as in subsection 3.2, this allows the 6-h-accumulated contribution of LBC perturbations for the ULBC ensemble to be expressed as follows, which can be compared to eq. (14) for GLBC:

where ${\mathbf{\epsilon }}_{0,3}^{LBC}=\left({\mathbf{M}}_{\widetilde{L_{a0}},G_{f3}}-{\mathbf{M}}_{L_{a0},G_{f3}}\right)\mathrm{\hspace{0.17em} }{\tilde{x}}_{L_{a0}}$ is the 3-h forecast perturbation arising from the use of perturbed LBCs at initial time, and ${\mathbf{\epsilon }}_{3,6}^{LBC}=\left({\mathbf{M}}_{G_{f3},G_{f6}}-{\mathbf{M}}_{G_{f3},G_{f6}}\right)\mathrm{\hspace{0.17em} }{\tilde{x}}_{L_{f3}}=0$ is the implicitly zeroed perturbation corresponding to the use of ULBC at 3- and 6-h forecast ranges.

3.4. Discussion on the effect of LBC perturbations in the different ensemble configurations

On the one hand, eq. (16) indicates that some significant contributions to forecast perturbations are expected from the use of perturbed initial LBCs in ULBC, in addition to the contributions of cycled observation perturbations, as indicated by eq. (11). On the other hand, the comparison between eqs. (14) and (16) also indicates that such contributions of LBC errors are obviously expected to be underestimated in ULBC, due to the use of ULBC at 3- and 6-h ranges. However, in accordance with the discussion in Section 3.1, the amplitude and spatial extent of this spread underestimation is likely to depend on the spatial coverage and quality of the observation network for instance.

As the ULBC configuration is the simplest one to implement for a NWP centre, which does not run a global ensemble system, one of the purposes of the considered experiments is thus to quantify the amount and spatial distribution of underdispersion associated to the use of LBC perturbations that are restricted to initial LBCs in ULBC.

Another purpose of interest is to examine the possibility to simulate LBC errors by using random draws of a specified error covariance model, as done in the PLBC ensemble. This will also include a sensitivity experiment with respect to the amplitude of such drawn perturbations: first, because it is likely to be a sensitive component of the PLBC ensemble, and second, because this is also a way to further document the effect of LBC perturbations in the LAM data assimilation ensemble.

4.1. Horizontal maps of time-averaged spread

Horizontal maps of ensemble spread have been computed for each ensemble configuration, and their temporal average over the 18-d experimental period is plotted in Fig. 2 for 6-h forecasts of zonal wind near 500 hPa. Note that this 18-d average has been considered knowing that the ensemble spin-up has been found to be small (about 1 d), as will be shown in Section 5.1 The bottom panel (corresponding to ULBC) includes only observation perturbations and initial LBC perturbations, while the middle panel (associated to GLBC) additionally includes the effect of 3- and 6-h LBC perturbations. The middle and bottom panels of this Figure can thus be compared in order to diagnose the effect of 3- and 6-h LBC perturbations on ensemble spread, relative to the effect of observation and initial LBC perturbations.

As expected from the use of ULBC at 3- and 6-h ranges, the ensemble spread for ULBC (bottom panel) is artificially close to zero near the lateral boundaries. Moreover, the comparison with GLBC (middle panel) indicates that this underdispersion in ULBC is also pronounced in a relatively large part of the inner computation domain. This is particularly visible in the western part of the area, which covers approximately one-third of the LAM domain, and which corresponds to the Near Atlantic, where large amplitudes in GLBC appear to be largely underestimated in ULBC. This artefact is also well marked in the North-West corner of the domain, which corresponds to relatively large perturbations in GLBC, whereas this is associated to relatively small values in ULBC. This apparent contamination of the inner area by small LBC perturbation amplitudes in ULBC can be interpreted as corresponding to the advection of underestimated LBC perturbations towards the inner part of the domain, as discussed also in the previous section.

Conversely, it can be noticed that the spread underestimation in ULBC is much less visible in the central and North-East parts of the domain for instance. With respect to eqs. (11), (14) and (16), this indicates that observation perturbations and initial LBC perturbations contribute to the main part of the forecast perturbations in these areas, whereas the 3- and 6-h LBC perturbations (as represented in GLBC) contribute much more significantly in the western part of the LAM domain.

The underestimation of ensemble spread is avoided in PLBC (top panel), in accordance with the addition of 3- and 6-h LBC perturbations in this ALADIN-France ensemble configuration. As a consequence, the horizontal variations of ensemble spread are relatively similar in PLBC and in GLBC, with for instance large spread in the Near Atlantic, and smaller spread in data dense continental areas such as Germany and Spain. This finding can be explained by the fact that both GLBC and PLBC ensembles use identical observation perturbations, similar amplitudes of LBC perturbations (related to the tuning of factor α as explained in Section 2.3) and the same data assimilation system for cycling perturbations. It can also be noticed that the ensemble spread tends to be somewhat larger in PLBC than in GLBC, for instance in the Near Atlantic and in the Mediterranean Sea. This aspect will be illustrated further in Section 4.2.

4.2. Meridional and zonal cross-sections of spread maps

The meridional and zonal cross-sections of time-averaged spread of zonal wind near level 500 hPa, temperature near 300 and 850 hPa and specific humidity near 850 hPa are respectively illustrated in Figs. 3 and 4 (using meridional and zonal averages, respectively). At first, these various panels illustrate the fact that the spread underestimation in ULBC, and its propagation towards the inner area, is a general feature valid for different variables and for different vertical levels.

It can also be seen that the underdispersion is particularly pronounced in the western part of the zonal cross-sections (Fig. 4). This feature is consistent with the zonal cross-domain flow which predominates on average during the considered period, as shown in Fig. 5a. One can also notice that the time-averaged wind plotted in Fig. 5a is consistent with the horizontal variations of ULBC underdispersion visible in the bottom panel of Figs. 2 and 5b: for instance, small perturbations near the North-West boundary tend to be advected by the inflow towards the inner area, whereas significant inner perturbations tend to be advected by the outflow towards the North-East boundary.

In contrast with ULBC, meridional and zonal cross-sections of PLBC ensemble spread are relatively similar to those of GLBC. This is particularly visible in the a-panels of Figs. 3 and 4, which correspond to temperature near 850 hPa. One can also notice that the spread tends to be somewhat larger in PLBC than in GLBC, as can be seen in the profiles for wind near 500 hPa. This is likely to reflect the fact that the ensemble spread in PLBC is sensitive to the tuning of the amplitude of the covariance model, from which LBC perturbations are drawn. The tuning of the LBC perturbation amplitude (which corresponds to the factor α in Section 2.3) is relatively rough, leading here to somewhat overestimated ensemble spread in PLBC compared to GLBC. The sensitivity of ensemble spread to the tuning of α is studied in the next section.

4.3. Sensitivity to LBC perturbation amplitudes

In order to examine the sensitivity of PLBC spread to the tuning of the specified covariance amplitude of LBC perturbations, a variant of the PLBC configuration has been run experimentally, based on the amplitude factor α=1 instead of α=0.3 in PLBC. The corresponding time-averaged map of ensemble spread is shown in Fig. 6 for zonal wind near 500 hPa, which can be compared to the top panel of Fig. 2.

It can be seen in Fig. 6 that the ensemble spread is largely increased not only near the lateral boundaries, but also in a large part of the inner area, which corresponds approximately to one-third of the domain size. This spread increase is also more visible in the western part of the domain, in accordance with aforementioned advection effects of the predominant western flow.

The deliberate choice of an excessive amplitude factor (α=1), which is the opposite extreme scenario of taking (α=0) in ULBC, allows the sensitivity of the PLBC ensemble spread to the amplitude of LBC perturbations to be confirmed. This indicates that an adequate tuning of this amplitude is an important component for obtaining realistic ensemble spread in the regional system.

More generally, this kind of sensitivity experiment also confirms the importance of LBCs for both ensemble and deterministic configurations, for instance in the sense that it indicates that forecast error amplitudes in the LAM domain can be significantly influenced by the amplitude of LBC errors.

5.1. Temporal evolution of analysis domain-averaged spread

Figure 7 represents the temporal evolution, over the 18-d considered period, of horizontally averaged analysis spread for temperature near 850 hPa (a-panel) and 300 hPa (b-panel), zonal wind near 500 hPa (c-panel) and specific humidity near 850 hPa (d-panel). Figure 7 shows features for the three ensembles that are consistent with previous results in terms of forecast spread, namely similar spread in PLBC and in GLBC (slightly larger in PLBC due to the roughly tuned amplitude factor) and a clear underestimation of ULBC dispersion (e.g. for temperature near 850 hPa).

While the ensemble spread is nearly stable beyond the first day for most variables, it can be noticed that humidity spread (d-panel) exhibits stronger temporal variations, with larger spread in April than in May. This is related to the fact that this April period is associated to a prevailing westerly moist flow from the Atlantic, while in this May period, a northern continental polar air mass (relatively cold and dry) predominates. The evolution of specific humidity spread thus reflects associated changes in atmospheric water content.

The larger effect of overestimation of spread in PLBC for specific humidity than for temperature and wind, further suggests that a parameter-dependent tuning of covariance amplitudes is likely to be needed. This tends to be supported by the examination of spread maps of surface pressure (not shown), which indicate that, in the vicinity of lateral boundaries, PLBC spread tends to be much larger (by a factor 2–3) than in GLBC (although spread is relatively similar in the innermost part of the domain).

In addition, Fig. 7 indicates that the ensemble spread needs about four analysis cycles to converge towards stable values for, for example, temperature and wind. This spread spin-up time (about 1 d) is relatively short for the regional ALADIN-France ensemble system. This is rather similar to the spin-up period found for the global ARPEGE ensemble system in Raynaud et al. (2012).

This short spin-up period may be interpreted as resulting at least partly from the influence of LBCs. For instance, in the case of GLBC and PLBC, LBC perturbations contribute to increase the spread from zero values in the initial background towards stable values after a 1-d period. In the case of ULBC, the null LBC perturbations are expected to contribute to counteract the spread increase observed during the first day period, but the observed evolution of ULBC spread suggests that observation and background perturbations remain large enough to maintain a stable ensemble spread in the innermost part of the domain, although spread remains globally underestimated throughout the period due to the use of ULBC.

On the whole, whatever LBC perturbations are, the spread spin-up period is about 1 d, suggesting that LBC perturbations affect mostly the asymptotic value to which each ensemble spread converges.

The evolution of ensemble spread has also been examined as a function of forecast range (not shown). It appears that the underdispersion of ULBC (compared to GLBC and PLBC) tends to be augmented during the 3- and 6-h forecast integrations, due to increasing LBC effects. For zonal wind near 500 hPa as an example, the ULBC space-averaged spread underestimation (compared to GLBC) increases from 20 % at analysis time to 25 % at 6-h forecast range. This indicates that the neglect of LBC errors in ULBC has a visible effect during the 6-h forecast (with a 5 % increase of underdispersion from 20 to 25 %) and that this effect tends to be accumulated (up to 20 % at analysis time) during the data assimilation cycle.

5.2. Temporal evolution of domain-averaged correlations between spread maps

While Section 5.1 shows the behaviour of horizontally averaged spread maps over time, this section focusses on a diagnosis of the spatial coherence between flow-dependent spread structures in the different ensembles. This is diagnosed by computing the domain-averaged correlation between the reference (i.e. GLBC) and experimental spread maps, for each date of the 18-day period. The a-panel of Fig. 8, which corresponds to domain-averaged correlations for temperature spread near level 850 hPa, indicates that PLBC produces flow-dependent spread structures which are more consistent with GLBC than those produced by ULBC. This is much less visible for zonal wind near 500 hPa (b-panel of Fig. 8), which is likely to be related to some predominant sampling noise effects, associated to the small ensemble size (namely six members).

In order to investigate this further, some local spatial averages of ensemble spread have been computed, in order to reduce sampling noise effects (as discussed, e.g., in Raynaud et al., 2008; Berre and Desroziers, 2010):

where (i, j) are indices of horizontal position (varying between 1 and N_i, N_j, respectively, N_i and N_j being the numbers of grid points in the zonal and meridional directions of the LAM domain), and ${(2N_m^{ij}+1)}^2$ is the total number of grid points used in the local 2D spatial average; the averaging radius $N_m^{ij}$ has a constant value equal to $N_m^c$ in the inner part of the domain, and it is progressively reduced when approaching the lateral boundaries: $N_m^{ij}=min(N_m^c,i-1,N_i-i,j-1,N_j-j)$.

Domain-averaged correlations between such spatially filtered spread maps are plotted in Fig. 9 for $N_m^c=12$. It can be seen that the increase of correlation for PLBC (dotted line), relative to ULBC (solid line), is more pronounced than in Fig. 8, for both temperature and wind. This supports the idea that a spatial filter is useful to highlight the spread increased realism of PLBC compared to ULBC. One can also note that the difference between ULBC and PLBC correlations for wind is relatively larger at the beginning of the experimental period, which is believed to be related to advection effects of the prevailing western winds for these dates (as suggested in Fig. 2 by the severe underestimation by ULBC of the large values of GLBC spread which are located west of the French Atlantic coast).

5.3. Flow dependency of ensemble spread sensitivities to LBC perturbations

In this section, the bottom panel of Fig. 2, which corresponds to the time-averaged ensemble spread map of ULBC, is compared to the corresponding map for a given date. Two special cases are examined, a case of a strong continental flow which crosses the area from the northwestern side on May 4 of 2010, and a case of a jet which crosses the domain from the West on April 25, 2010 (see Fig. 10).

The conspicuous rolling-up pattern in Fig. 10b, corresponding to the 6-h horizontal wind deterministic forecast near 500 hPa on 4 May 2010, implies a specific and recognisable cyclonic-like structure in the ensemble spread of 6-h zonal wind forecast in Fig. 10d. In the vicinity of the central part of the North boundary, the advection of zero-valued LBC perturbations by a northerly jet towards a large part of the inner area is well marked. This feature is interesting to compare with Fig. 2, as an illustration that, depending on the meteorological situation, the underestimation of ensemble spread in ULBC can be more pronounced in terms of spatial coverage than what can be seen in a time-averaged sense (bottom panel of Fig. 2). Figure 10f which corresponds to the ratio of spread of 6-h zonal wind forecasts near level 500 hPa of ULBC to that of GLBC $\left(\frac{{\sigma }_{ULBC}}{{\sigma }_{GLBC}}\right)$ on 4 May 2010 can also be compared to Fig. 5b, in order to notice the specific location of underestimated spread along the North-West and South-East boundaries, which correspond to inflow borders of the domain (Fig. 10b).

Figure 10a shows the 6-h horizontal wind deterministic forecast near 500 hPa on 25 April, with an apparent jet that crosses the area from the west, and another one, less intensive, which crosses the domain from the extreme northeastern side. The effect of these jets is again well visible in the map of zonal wind spread (Fig. 10c), with the inward advection of zero-valued LBC perturbations by the jets in these two regions. The difference between Fig. 10e and f, along with the difference with the time-averaged spread ratio (Fig. 5b), confirm the strong flow dependency of spread sensitivities to LBC perturbations.