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The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors)

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,

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The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors)

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

  2. 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-dependentbackground error variances (for all variables including humidityand unbalanced variables) • for obs. quality control and for theminimization. • 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.

  3. “OPTIMIZED” SPATIAL FILTERING OF THE VARIANCE FIELD « TRUE » VARIANCES FILTEREDVARIANCES (N = 6) Vb* ~ r Vb wherer = signal/(signal+noise) (Raynaud et al 2008a) RAWVARIANCES (N = 6) (Berre et al 2007,2010, Raynaud et al 2008,2009,2011)

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

  5. 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-dependentbackground errorcorrelations 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.

  6. Flow-dependent background errorcorrelationsusing 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)

  7. Impact of wavelet flow-dependent correlationsagainst spectral static correlations (Varella et al 2011b) Vertical profile of RMS for +48h Vertical profile of RMS for +96h 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

  8. 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). • Initialisationof 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.

  9. Estimation of model error and its representation in EnDA (Raynaud et al 2011) Methodology : 1. Variances of « total » forecast error V[ M ea + em] from obs-forecast departures (after filtering of obs errors).2. Comparison with ensemble spread V[ M ea ] and estimation of inflation factor a.3. Inflationof forecast perturbations (by a > 1).

  10. 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 obs-forecast Estimate from AEARP, when model error is neglected Estimate from AEARP, when model error is represented

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