Ensemble data assimilation in an operational context: the experience at the Italian Weather Service...

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


CNMCA NWP System

Domain size 385 x 257

Grid spacing 0.25 Deg (28 km)

Number of layers 40

Time step and scheme 150 sec, split semi-implicit

Forecast range 72 hrs

Initial time of model run 00/12 UTC

L.B.C. IFS

L.B.C. update frequency 3 hrs

Initial state CNMCA 3D-PSAS

Initialization Digital Filter

External analysis None

Status Operational

Hardware IBM Power4

N° of processors used 32 (Model), 90 (Analysis)


CNMCA NWP System


Domain size 465 x 385

Grid spacing 0.0625 (7 km)

Number of layers 35

Time step and scheme 40 s,3 time-lev split-expl

Forecast range 60 hrs

Initial time of model run 00 UTC

Lateral bound. condit. IFS

L.B.C. update frequency

3 hrs

Initial state EURO-HRM 3D-PSAS

Initialization Digital Filter

External analysis T,u,v, PseudoRH, SP

Special features Filtered topography

Status Operational

Hardware IBM P690 (ECMWF)

N° of processors 120

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


CNMCA NWP System

xxPxxxyRxy bb

Tb

T HHJ 11

21

21

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:


CNMCA NWP System Multivariate (T,u,v – Surf.

Press.,u,v) correlation functions in spherical geometry

Thermal wind - geostrophic constraint on analysis increments


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

covariances4. 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 Pb => O(Nensemble), i.e. very low dimensional w.r.t. model and observations degrees of freedom


Motivations

Possible remedies:

Hybrid EnKF (Hamill & Snyder, 2000; Etherton & Bishop, 2004):

Covariance spatial localization (Houtekamer & Mitchell, 2001)


staticf

ensff BBP )( 1


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


70

1

.

))(*(

staticf

ensf

staticf corrcorrVARP

HEnKF data assimilation cycle

Member j 3DVAR / ENKF

using members i≠j

18 UTC Observations

Member j PerturbedObservations

6h Forecast

12 UTCB. C.

Member j 3DVAR / ENKFusing members i≠j

00 UTCObservations

Member j PerturbedObservations

6h Forecast

18 UTCB. C.

The reference run uses all members to compute the backgrounderror correlations and unperturbed observations.




Effect of flow-dependent background error covariances


Covariance spatial localization:

1. Horizontal decorrelation length Lc= 600 Km

2. Vertical decorrelation parameter Kp = 1


ccexp)(),(_ L

rLrLrhorizCorr c 1

21

1

)ln(),(_

jip ppKjivertCorr



Effect of flow-dependent background error covariances (u-wind component 500 hPa)



Effect of horizontal covariance localization (Temperature 500 hPa)



Effect of horizontal covariance localization (v-wind component,500 hPa)



Effect of vertical covariance localization

Impact studies: Verification methodology

Comparison of forecasts produced from the analyzed fields with SYNOP and RAOB observations.

Impact studies


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



Careful tuning of HEnKF parameter: , Lc, Kp

Use of ensemble covariances, not just correlations

System is intrinsically suitable for parallelization but still expensive in terms of billing units: Nensx(analysis and t+6h forecasts) ! further reduction of analysis resolution, but tradeoff with realistic covariance structures



Explore the possibility of using ensemble members for short range EPS


Thank you!


Ensemble data assimilation in an operational context: the experience at the Italian Weather Service...

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