The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors) Ensemble assimilation (operational with 6 members…) :

The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors)  Operational since July 2008 : six perturbed global members, T399 L70 with 4D-Var Arpege (explicit obs perturbations, and implicit background perturbations through perturbed DA cycling).  Flow-dependent background error variances (for all variables including humidity and unbalanced variables)  for obs. quality control and for the minimization.  Flow-dependent background error correlations experimented using wavelet filtering properties (Varella et al 2011 a,b, Prevassemble project).  Initialisation of M.F. ensemble prediction (PEARP) by EnVar, since 2009 : PEARP is based on 35 members, T538 c2.4 L65, EnVar+SVs and 10 physics.  Inflation of ensemble B / model error contributions, to be replaced by on-line inflation of perturbations in 2012.

(Raynaud et al 2008a) “OPTIMIZED” SPATIAL FILTERING OF THE VARIANCE FIELD V b * ~  V b where  = signal/(signal+noise) « TRUE » VARIANCES VARIANCES FILTERED VARIANCES (N = 6) VARIANCES RAW VARIANCES (N = 6) (Berre et al 2007,2010, Raynaud et al 2008,2009,2011)

Errors of the day for 3-hr forecasts provided by the Ensemble Data Assimilation Ens Assim. 4D-Var Ens Assim. 3D-Var Fgat  Klaus storm. The error maximum is better forecast by the 4D-Var version of the ensemble assimilation. 24/01/2009 at 00h/03h

The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors)  Operational since July 2008 : six perturbed global members, T399 L70 with 4D-Var Arpege (explicit obs perturbations, and implicit background perturbations through perturbed DA cycling).  Flow-dependent background error variances (for all variables including humidity and unbalanced variables)  for obs. quality control and for the minimization.  Flow-dependent background error correlations experimented using wavelet filtering properties (Varella et al 2011 a,b, Prevassemble project).  Initialisation of M.F. ensemble prediction (PEARP) by EnVar, since 2009 : PEARP is based on 35 members, T538 c2.4 L65, EnVar+SVs and 10 physics.  Inflation of ensemble B / model error contributions, to be replaced by on-line inflation of perturbations in 2012.

Flow-dependent background error correlations using EnVar and wavelets Wavelet-implied horizontal length-scales (in km), for wind near 500 hPa, averaged over a 4-day period. (Varella et al 2011b, and also Fisher 2003, Deckmyn and Berre 2005, Pannekoucke et al 2007)

Impact of wavelet flow-dependent correlations against spectral static correlations (Varella et al 2011b) SOUTHERN HEMISPHERE (3 weeks, RMS of geopotential) EUROPE AND N. ATLANTIC (3 weeks, RMS of geopotential) Time evolution of RMS for +96h, at 500 hPa Time evolution of RMS for +48h, at 250 hPa Vertical profile of RMS for +48h Vertical profile of RMS for +96h

The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors)  Operational since July 2008 : six perturbed global members, T399 L70 with 4D-Var Arpege (explicit obs perturbations, and implicit background perturbations through perturbed DA cycling).  Flow-dependent background error variances (for all variables including humidity and unbalanced variables)  for obs. quality control and for the minimization.  Flow-dependent background error correlations experimented using wavelet filtering properties (Varella et al 2011 a,b, Prevassemble project).  Initialisation of M.F. ensemble prediction (PEARP) by EnVar, since 2009 : PEARP is based on 35 members, T538 c2.4 L65, EnVar+SVs and 10 physics.  Inflation of ensemble B / model error contributions, to be replaced by on-line inflation of perturbations in 2012.

Estimation of model error and its representation in EnDA (Raynaud et al 2011) Methodology : 1. Variances of « total » forecast error V[ M e a + e m ] from obs-forecast departures (after filtering of obs errors). 2. Comparison with ensemble spread V[ M e a ] and estimation of inflation factor . 3. Inflation of forecast perturbations (by 

Estimation of model error and its representation in EnDA (Raynaud et al 2011) Vertical profiles of standard deviation estimates of forecast errors (K) Estimate from AEARP, when model error is neglected Estimate from AEARP, when model error is represented Estimate from obs-forecast