Estimation of Synoptic and Mesoscale Forecast Error Covariances in a Limited-Area Model

By L. Berre. 2000

215 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dd0fbc709853c6641fb5d93da052577b8fc8de47

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In Atmospheric Science

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Citations in the corpus

A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, methodology): “Similar transforms have been considered for other systems, for example, in the Météo-France Aladin system by Deckmyn and Berre (2005) and the Met Office system by Andrews and Lorenc (personal communication) and Bannister (2007).”
Flow‐dependent background‐error covariances for a convective‐scale data assimilation system (2012, methodology): “This formalism was proposed by Parrish, et al. (1997) and Derber and Bouttier (1999) for global NWP systems, and adapted by Berre (2000) for limited-area NWP systems.”
The AROME-France Convective-Scale Operational Model (2011, methodology): “These covariances are estimated through the same multivariate formulation as ALADIN-France (Berre 2000) using forecast error statistics of vorticity, divergence, temperature, surface pressure, and specific humidity.”
The Use of an Ensemble Approach to Study the Background Error Covariances in a Global NWP Model (2006, methodology): “This method was used in limited-area models too (Berre 2000; Gustafsson et al. 2001).”
Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, methodology): “The correspondence between the variance spectrum and the bi-Fourier representation follows the Berre (2000) formulation of the ALADIN/AROME background error covariance formulation.”
An overview of the variational assimilation in the ALADIN/France numerical weather‐prediction system (2005, methodology): “The formulation of the background-error covariance matrix has been obtained as the bi-Fourier counterpart of the global, spherical harmonic formulation (Berre 2000).”
Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model (2014, background): “d The background-error matrix allows us to spread spatially the information brought by the observations, but the current covariance structures are mainly isotropic and homogeneous and the cross correlations between humidity and other control variables exist but are rather small (Berre 2000).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “using homogeneous and isotropic functions (Berre, 2000; Bannister, 2008; Errera and Menard, 2012), or more generally using using recursive filters (Hayden and Purser, 1995), wavelets (Fisher, 2003; Deckmyn and Berre, 2005; Bannister, 2007), or spectral localization (Buehner and Charron, 2007).”
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization (2015, background): “From Berre (2000), we know that a homogeneous and isotropic filtering in gridpoint space is equivalent to a diagonal filter in spectral space: bsk 5 rkesk , (C2) where rk are real coefficients of the spectral filter, lying between 0 and 1, with r0 5 1 to retain the spatial average.”
Background‐error covariances for a convective‐scale data‐assimilation system: AROME–France 3D‐Var (2011, methodology): “These cross-correlations are represented on Figure 8 by the percentage of error variance calculated using the ratio between the respective variances of balanced and full variables as in page 653 of Berre (2000).”