The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model

Belo-Pereira, Margarida; Berre, Loïk

doi:10.1175/mwr3189.1

Cited by 132 publications

(154 citation statements)

References 20 publications

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“…Only a simple diffusion operator is considered (and not the generalized diffusion equation). This estimation is partly based on the computation of the local length-scale (Belo Pereira and Berre, 2006;Pannekoucke et al, 2008).…”

Section: O Pannekoucke and S Massartmentioning

confidence: 99%

“…The estimation of the background-error correlations using an assimilation ensemble has proved to be an efficient method (Belo Pereira and Berre, 2006). The ensemble used in this study is based on ten sets of perturbed observations derived from the Envisat/MIPAS reference dataset of ASSET (version 4.61 delivered by the German Processing and Archiving Center, D-PAC, of the European Space Agency).…”

Section: The Used Ensemble Datasetmentioning

confidence: 99%

“…As explained in Belo Pereira and Berre (2006), the previously described ensemble of perturbed forecasts is used to estimate the background-error correlation in the grid points. Spatial correlations of the background error are then computed over the whole MOCAGE spatial domain.…”

Section: Estimation Of the Local Diffusion Tensor For The Ozone And Imentioning

confidence: 99%

“…The anisotropy vector diagnosis is considered in a similar fashion to Belo Pereira and Berre (2006). The local anisotropy vector corresponds to the leading principal axis of the diffusion tensor ν(x).…”

Section: Estimation Of the Local Diffusion Tensor For The Ozone And Imentioning

confidence: 99%

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Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion equation

Pannekoucke

Massart

2008

Quart J Royal Meteoro Soc

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ABSTRACT:As the background-error covariance matrix is a key component of any assimilation system, its modelling is an important step. Usually, this matrix is decomposed into correlations and standard deviations matrices. An interesting method for modelling the correlation matrix of the background error for complex geometry, like an ocean grid, consists in computing correlation functions using a diffusion operator. The background-error correlation functions can be estimated for example from an ensemble of perturbed forecasts. The diffusion operator is able to represent heterogeneous correlation functions at a reasonable numerical cost. But a first challenge resides in the determination of the local diffusion tensor corresponding to the local correlation function. Then the second challenge resides in the determination of the normalization to make sure that the matrix modelled through the diffusion operator is a correlation matrix. In this article, we propose to build a background-error correlation matrix using a diffusion operator based on a local diffusion tensor. The estimation of this local tensor is performed using an ensemble of perturbed forecasts. A validation within a randomization method illustrates the feasibility and the accuracy of the proposed method. In particular, it is shown that the local geographical variations of diagnosed correlation functions (through an ensemble of perturbed forecast) are well represented. This is first illustrated in an analytical one-dimensional framework. In that context, the diffusion field and the normalization field are deduced from a given correlation length-scale field. The resulting length-scales are shown to correspond to the initial length-scale when the given length-scale field spectrum is red. The approximate normalization, computed from the local length-scale, is close to the true normalization under the same condition of a red spectrum.Then, the method is illustrated in a real context using an ensemble of perturbed forecasts from the MOCAGE-PALM assimilation system. In that case, length-scale and anisotropy diagnosis reveal the complexity of the correlation of stratospheric ozone forecast errors. The local diffusion tensor deduced from these diagnoses is shown be able to represent such an existing heterogeneity and anisotropy. As in the one-dimensional case, the approximate normalization, based on the local diffusion tensor, appears to be a really good approximation of the true normalization.

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Section: O Pannekoucke and S Massartmentioning

confidence: 99%

Section: The Used Ensemble Datasetmentioning

confidence: 99%

Section: Estimation Of the Local Diffusion Tensor For The Ozone And Imentioning

confidence: 99%

Section: Estimation Of the Local Diffusion Tensor For The Ozone And Imentioning

confidence: 99%

See 2 more Smart Citations

Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion equation

Pannekoucke

Massart

2008

Quart J Royal Meteoro Soc

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“…Rather than using the ensemble directly to construct an estimate of the covariance matrix, it may be used indirectly to calibrate specific parameters of a covariance model (Fisher, 2003;Žagar et al, 2005;Belo Pereira and Berre, 2006;Berre et al, 2006;Küçükkaraca and Fisher, 2006). The use of a covariance model has the advantage of providing a full-rank (implicit) representation of the covariance matrix and thus allows the assimilation method to produce corrections to the background state in a much larger space than that spanned by a limited number of ensemble members.…”

Section: Introductionmentioning

confidence: 99%

Ensemble estimation of background‐error variances in a three‐dimensional variational data assimilation system for the global ocean

Daget

Weaver

Balmaseda

2009

Quart J Royal Meteoro Soc

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This paper studies the sensitivity of global ocean analyses to two flow-dependent formulations of the background-error standard deviations (σ b ) for temperature and salinity in a three-dimensional variational data assimilation (3D-Var) system. The first formulation is based on an empirical parameterization of σ b in terms of the vertical gradients of the background temperature and salinity fields, while the second formulation involves a more sophisticated approach that derives σ b from the spread of an ensemble of background states. The ensembles are created by explicitly perturbing both the surface fluxes (wind stress, fresh water and heat) used to force the model and the observations (temperature and salinity profiles) used in the assimilation process. The two formulations are compared in two cycled 3D-Var experiments for the period 1993-2000. In both experiments, the observation-error standard deviations (σ o ) are geographically dependent and estimated from a model-data comparison prior to assimilation. An additional 3D-Var experiment that employs the parametrized σ b but a simpler σ o formulation, and a control experiment involving no data assimilation, were also conducted and used for comparison.All 3D-Var experiments produce a significant reduction in the mean and standard deviation of the temperature and salinity innovations compared to those of the control experiment. The largest differences between the two σ b formulations occur in the upper 150 m, where the parametrized σ b are notably larger than the ensemble σ b . In this region, the innovation statistics are slightly better for the parametrized σ b . Statistical consistency checks indicate that both schemes underestimate σ b , the underestimation being stronger with the ensemble formulation. The error growth between cycles, however, is much reduced with the ensemble σ b , suggesting that the analyses produced with the ensemble σ b are in better balance than those produced with the parametrized σ b . This claim is supported by independent data comparisons involving model fields not directly constrained by the assimilated temperature and salinity profiles. In particular, sea-surface height (SSH) anomalies in the northwest Atlantic and zonal velocities in the equatorial Pacific are clearly better with the ensemble σ b than with the parametrized σ b . Results also show that while some aspects of those variables are improved with data assimilation (SSH anomalies and currents in the central and eastern Pacific), other aspects are degraded (SSH anomalies in the northwest Atlantic, currents in the western Pacific). Areas for improving the ensemble method and for making better use of the ensemble information are discussed.

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Impact of new aircraft observations Mode‐S MRAR in a mesoscale NWP model

2015

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The impact of recently available high-resolution Mode-S Meteorological Routine Air Report (MRAR) wind and temperature observations is evaluated in the mesoscale numerical weather prediction (NWP) model Aire Limitée Adaptation dynamique Développement InterNational (ALADIN). Data available from the airspace communicating with the Ljubljana Airport in Slovenia are assimilated by using the three-dimensional variational assimilation procedure on top of all other observations assimilated operationally. A data selection method based on aircraft type was shown to be important for the first application of the new observations in ALADIN. The evaluation of Mode-S MRAR impact included both winter and summer periods. In both seasons a clear improvement of wind and temperature forecasts was found for in the short forecast range, 1-3 h. The impact in the 24 h forecast range depends on season, with a consistent positive improvement of the boundary layer temperature forecasts obtained for the stable anticyclonic winter situations. In summer, the impact was mixed and it was found to be sensitive to the multivariate aspects of the moisture analysis. Overall presented results suggest that the new aircraft-derived observations Mode-S MRAR have a significant potential for mesoscale NWP and improved data assimilation modeling.

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The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model

Cited by 132 publications

References 20 publications

Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion equation

Estimation of the local diffusion tensor and normalization for heterogeneous correlation modelling using a diffusion equation

Ensemble estimation of background‐error variances in a three‐dimensional variational data assimilation system for the global ocean

Impact of new aircraft observations Mode‐S MRAR in a mesoscale NWP model

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