Ensemble data assimilation in an operational context: the experience at the Italian Weather Service Massimo Bonavita and Lucio Torrisi CNMCA-UGM, Rome (Italy) SREPS Workshop, Bologna 7-8 April 2005

Scope  Current NWP system at CNMCA  Motivations  CNMCA Hybrid ENKF setup  Impact studies on the CNMCA NWP system  Conclusions and developments SREPS Workshop, Bologna 7-8 April 2005

CNMCA NWP System Domain size385 x 257 Grid spacing 0.25 Deg (  28 km) Number of layers40 Time step and scheme 150 sec, split semi-implicit Forecast range72 hrs Initial time of model run00/12 UTC L.B.C.IFS L.B.C. update frequency3 hrs Initial stateCNMCA 3D-PSAS InitializationDigital Filter External analysisNone StatusOperational HardwareIBM Power4 N° of processors used32 (Model), 90 (Analysis) SREPS Workshop, Bologna 7-8 April 2005

CNMCA NWP System SREPS Workshop, Bologna 7-8 April 2005 Domain size465 x 385 Grid spacing (7 km) Number of layers35 Time step and scheme40 s,3 time-lev split-expl Forecast range60 hrs Initial time of model run00 UTC Lateral bound. condit. IFS L.B.C. update frequency3 hrs Initial stateEURO-HRM 3D-PSAS InitializationDigital Filter External analysisT,u,v, PseudoRH, SP Special featuresFiltered topography StatusOperational HardwareIBM P690 (ECMWF) N° of processors120

CNMCA NWP System  Intermittent (6-h) data assimilation cycle  Observations: 1. Synoptic: TEMP, PILOT, SYNOP, SHIP, BUOY 2. A-synoptic: AMSUA rad, AMDAR-AIREP, AMV, Wind Profilers, QUIKSCAT-ERS2 scatt. winds SREPS Workshop, Bologna 7-8 April 2005

CNMCA NWP System  3D-PSAS objective analysis in (T,u,v,Pseudo RH, Surf. Press.; Bonavita and Torrisi, 2005)  Parallel (MPI) minimization algorithm of the c.g.d. type of the cost function : SREPS Workshop, Bologna 7-8 April 2005

CNMCA NWP System  Multivariate (T,u,v – Surf. Press.,u,v) correlation functions in spherical geometry  Thermal wind - geostrophic constraint on analysis increments SREPS Workshop, Bologna 7-8 April 2005

Motivations  Known limitation of 3D-Var approach: Stationary forecast error covariances  Possible solution: Ensemble Kalman Filter (Evensen, 1994) 1. Limited computational cost w.r.t. Extended KF; 2. Algorithmic simplicity w.r.t. 4DVar: does not require development of a linear and adjoint model; 3. It does not require linearized evolution of forecast error covariances 4. It may provide good initial perturbations for ensemble forecasting SREPS Workshop, Bologna 7-8 April 2005

Motivations … but  Limited ensemble size may lead to small ensemble spread  The analysis increments can only occur within the subspace spanned by P b => O(N ensemble ), i.e. very low dimensional w.r.t. model and observations degrees of freedom SREPS Workshop, Bologna 7-8 April 2005

Motivations Possible remedies:  Hybrid EnKF (Hamill & Snyder, 2000; Etherton & Bishop, 2004):  Covariance spatial localization (Houtekamer & Mitchell, 2001) SREPS Workshop, Bologna 7-8 April 2005

CNMCA Hybrid ENKF setup  24 Perturbed Obs. Members + reference member (unperturbed observations)  Analysis step at half model resolution (0.5°)  Ensemble used to correct only correlation part of covariance product SREPS Workshop, Bologna 7-8 April 2005

HEnKF data assimilation cycle Member j 3DVAR / ENKF using members i≠j 18 UTC Observations Member j Perturbed Observations 6h Forecast 12 UTC B. C. Member j 3DVAR / ENKF using members i≠j 00 UTC Observations Member j Perturbed Observations 6h Forecast 18 UTC B. C. The reference run uses all members to compute the background error correlations and unperturbed observations. SREPS Workshop, Bologna 7-8 April 2005

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005 Effect of flow-dependent background error covariances

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005

CNMCA Hybrid ENKF setup  Covariance spatial localization: 1. Horizontal decorrelation length L c = 600 Km 2. Vertical decorrelation parameter K p = 1 SREPS Workshop, Bologna 7-8 April 2005

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005 Effect of flow-dependent background error covariances (u-wind component 500 hPa)

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005 Effect of horizontal covariance localization (Temperature 500 hPa)

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005 Effect of horizontal covariance localization (v-wind component, 500 hPa)

CNMCA Hybrid ENKF setup SREPS Workshop, Bologna 7-8 April 2005 Effect of vertical covariance localization

Impact studies: Verification methodology Comparison of forecasts produced from the analyzed fields with SYNOP and RAOB observations.

Impact studies SREPS Workshop, Bologna 7-8 April 2005

Impact studies SREPS Workshop, Bologna 7-8 April 2005

Impact studies SREPS Workshop, Bologna 7-8 April 2005

Impact studies SREPS Workshop, Bologna 7-8 April 2005

 Current CNMCA HEnKF forecast skill is overall comparable to pure 3DVAR assimilation.  HEnKF have been set up based on recent literature and heuristic assumptions. Conclusions and future plans SREPS Workshop, Bologna 7-8 April 2005

Conclusions and future plans  Careful tuning of HEnKF parameter: , L c, K p  Use of ensemble covariances, not just correlations  System is intrinsically suitable for parallelization but still expensive in terms of billing units: N ens x(analysis and t+6h forecasts) !  further reduction of analysis resolution, but tradeoff with realistic covariance structures SREPS Workshop, Bologna 7-8 April 2005

Conclusions and future plans  Explore the possibility of using ensemble members for short range EPS SREPS Workshop, Bologna 7-8 April 2005

Thank you! SREPS Workshop, Bologna 7-8 April 2005