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Ozone Assimilation in the Chemistry Transport Model CHIMERE using an

Ozone Assimilation in the Chemistry Transport Model CHIMERE using an Ensemble Kalman Filter (EnKF) : Preliminary tests over the Ile de France region. A. Coman ( 1 ) , G. Forêt ( 2 ) , A. Ionescu ( 1 ) , Y. Candau ( 1 ) , M. Beekmann ( 2 ) , G. Bergametti ( 2 ) , C. Schmechtig ( 2 ).

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Ozone Assimilation in the Chemistry Transport Model CHIMERE using an

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

  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

  3. CHIMERE: Regional Chemistry Transport Model Developed by: LMD, LISA, INERIS [http://euler.lmd.polytechnique.fr/chimere] ECMWF meteorological forecasts EMEP, GENEMIS, CITEPA : emissions USGS land use, land cover MOZART(gases) Large scale chemical forcing (from monthly climatology) CHIMERE Regional CTM ● gaseous chemistry module ● advection, turbulence ● dry and wet deposition Hourly concentrations of : gases (O3, NO2, CO, SO2 …)

  4. 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 backgrounderror.

  5. 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) Generating pseudo random fields with prescribed characteristics mean=0 and variance=1 • Covariance function exp(-r2/rh2); 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 3. Spin-up simulation (here 10 days)

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

  7. CHIMERE: Grid and Measurement Sites • Grid characteristics: • 2525 cells • resolution 6 km6 km • vertical stratification: • 8 levels • 44 species • assimilation period: • 11-21 July 1999 • ( Nested simulation) • Measurement sites • (AIRPARIF) • 11 background stations used for assimilation • 5 stations used for validation

  8. RESULTS: PRUNAY (assimilation station)

  9. RESULTS: FREMAINVILLE (assimilation station)

  10. RESULTS: GARCHES (validation station)

  11. RESULTS: PARIS13 (validation station)

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

  13. RMSE FOR THE ASSIMILATION STATIONS RMSE CHIMERE : 18-32 µg/m3 RMSE CHIMERE/ENKF: 12-29 µg/m3 RMSE IMPROVEMENT: 10%-34%

  14. RMSE FOR VALIDATION STATIONS RMSE CHIMERE : 21-24 µg/m3 RMSE CHIMERE/ENKF: 18-22 µg/m3 RMSEIMPROVEMENT: 8%-24%

  15. MAE FOR ASSIMILATION STATIONS MAE CHIMERE : 14-23 µg/m3 MAE CHIMERE/ENKF: 10-20 µg/m3 MAE IMPROVEMENT: 15%-35%

  16. MAE FOR VALIDATION STATIONS MAE CHIMERE : 16-19 µg/m3 MAE CHIMERE/ENKF: 12-15 µg/m3 MAE IMPROVEMENT: 17%-30%

  17. 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)

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