Ozone Assimilation in the Chemistry Transport Model CHIMERE using an Ensemble Kalman Filter (EnKF) : Preliminary tests over the Ile de France region 2 Laboratoire Interuniversitaire des Systèmes Atmosphériques University of Paris XII 1 Centre d’Etudes et de Recherche en Thermique, Environnement et Systèmes University of Paris XII A. Coman (1), G. Forêt (2), A. Ionescu (1), Y. Candau (1), M. Beekmann (2), G. Bergametti (2), C. Schmechtig (2)

Outline ◦ Regional Chemical Transport Model CHIMERE ◦ The Ensemble Kalman Filter (EnKF) ◦ First test of the assimilating system - set up of the experiment - preliminary results

CHIMERE: Regional Chemistry Transport Model Developed by: LMD, LISA, INERIS [ ECMWF meteorological forecasts CHIMERE Regional CTM Hourly concentrations of : gases (O 3, NO 2, CO, SO 2 …) EMEP, GENEMIS, CITEPA : emissions USGS land use, land cover MOZART ( gases ) Large scale chemical forcing (from monthly climatology) ● gaseous chemistry module ● advection, turbulence ● dry and wet deposition

The Ensemble Kalman Filter : EnKF Kalman Filter not practical for large systems because of prohibitive computational cost :  Development of low rank Kalman Filter (Ensemble KF, Particles Filter, Reduced Rank Square Root KF and Hybrid methods) EnKF (Evensen, 1994; Burgers et al, 1998)  Use of Monte Carlo approach to propagate covariances: an ensemble of N states is used to sample the probability distribution of the background error.

Assimilation procedure (1) 1. Initial model simulation: «best-guess» estimate 2. Create 20 ensemble members using the «best-guess» estimate and pseudo-random fields (Evensen, 1994) 3. Spin-up simulation (here 10 days)  Generating pseudo random fields with prescribed characteristics mean=0 and variance=1  Covariance function exp(-r 2 /rh 2 ); rh= 50 km (decorrelation length): for each ensemble member, new Ozone concentrations fields are generated by adding smooth pseudo random fields to the original model state with  2 =25% variance

Assimilation procedure (2) Given an ensemble of model forecasts with the forecast error covariance Using R e as ensemble representation of the measurement error covariance matrix where the measurements are treated stochastically and thus perturbed Update equation Kalman gain is calculated as Analysed error covariance becomes 4. Analysis step in EnKF ( 10 days period, July 11-20,1999, with a 3-hour time step)

CHIMERE: Grid and Measurement Sites Grid characteristics: 25  25 cells resolution 6 km  6 km vertical stratification: 8 levels 44 species assimilation period: July 1999 (  Nested simulation) Measurement sites (AIRPARIF) 11 background stations used for assimilation 5 stations used for validation

RESULTS: PRUNAY (assimilation station)

RESULTS: FREMAINVILLE (assimilation station)

RESULTS: GARCHES (validation station)

RESULTS: PARIS13 (validation station)

RESIDUAL ANALYSIS INDICES:  MAE (MEAN ABSOLUTE ERROR)  RMSE (ROOT MEAN SQUARE ERROR ) where N is the number of valid available measurements.

RMSE FOR THE ASSIMILATION STATIONS RMSE CHIMERE :18-32 µg/m 3 RMSE CHIMERE/ENKF: µg/m 3 RMSE IMPROVEMENT : 10%-34%

RMSE FOR VALIDATION STATIONS RMSE CHIMERE :21-24 µg/m 3 RMSE CHIMERE/ENKF: µg/m 3 RMSE IMPROVEMENT : 8%-24%

MAE FOR ASSIMILATION STATIONS MAE CHIMERE :14-23 µg/m 3 MAE CHIMERE/ENKF: µg/m 3 MAE IMPROVEMENT : 15%-35%

MAE FOR VALIDATION STATIONS MAE CHIMERE :16-19 µg/m 3 MAE CHIMERE/ENKF: µg/m 3 MAE IMPROVEMENT : 17%-30%

Planned Work ● Analysis and Optimization of the system (sensivity tests) Uncertainties in CHIMERE: emissions large-scale chemical forcing meteorological fields chemical rate coefficients Implementation of other reduced-rank algorithms (RRSQRT, POEnKF) Considered stochastic and perturbed (Hanea et al, 2003; Constantinescu et al, 2006) ▪ Assimilation of satellite observations (tropospheric ozone over Europe): -Use of Severi-Schiamachy retrievals, projet RAL/ESA (A. Eung,LISA/INERIS) - Use of IASI retrievals: 1) Thèse A. Boynard, LISA/S.A. 2) SPECAT Team (coll. with J. Orphal, LISA) - EnKF  EnKS (Smoother) for emission inversion ?? (Hanea et al, 2006)