On the Use of Satellite Derived Aerosol Products to Improve Air Quality Predictions

1. On the Use of Satellite Derived Aerosol Products to Improve Air Quality Predictions Qiang Zhao (IMSG) Shobha Kondragunta (NESDIS/STAR) JCSDA 7th Workshop on Satellite Data Assimilation, May 12-13, 2009

2. Acknowledgements • NCEP • Jeff McQueen • Sarah Lu • OAR/ARL • Pius Lee • NESDIS • Chuanyu Xu • Pubu Ciren

3. Impact of GOES AOD Assimilation on 24-hr Average CMAQ Predicted Surface PM2.5 • August 1 – August 6, 2006 summer time regional scale urban/industrial haze episode was simulated using NCEP developmental CMAQ PM version with and without GOES AOD assimilation • Assimilation improved surface PM2.5 by a factor of 2 CMAQ (no assimilation) CMAQ (assimilation)

4. Quantitative Evaluation of PM2.5 Predictions

5. FY09 Activities • Why CMAQ model has a minimum surface PM2.5 concentrations in day time ? • How long can the CMAQ model hold the assimilated information? • Prepare for 3D-Var assimilation • Determine model background error • Determine errors in observations

6. Minimum in Surface PM2.5 • Model predicted AOD agrees with in situ observations better for assimilation run than base run • PBL dynamics appear to modulate surface PM2.5 concentrations

8. Comparison of CMAQ and CALIPSO Vertical Profiles of Aerosols (Sulfate Episode) CALIPSO Impact of GOES AOD assimilation on improving PM2.5 predictions depend on CMAQ vertical profiles BASE DA-GOES

9. Next Steps • 3DVar • Combining background forecasts and observations in a statistically sound way • Computationally practical for operational use • Relatively easy to implement • Potential for including different types of observations • Providing starting point toward 4DVar in the future • Why NOT 4DVar or EnKF • Too expensive for operational use NOW • (4DVar) Requires extensive coding for Adjoint and Tangent Linear Models

10. Next Steps • GSI ? • Operationally used at NCEP for global and regional atmospheric models • Allows spatially inhomogeneous and anisotropic background error covariance • Or NOT ? • CMAQ is an off-line chemical transport model, while GSI is meteorology-centered system • GSI-CMAQ Version? • Take GSI framework • Replace control variable set with one derived from CMAQ model • Estimate background error parameters for new variable set • Error variance (or standard deviation) • Correlation length scales • Multivariate coupling based on equilibrium relationships among chemical/aerosol species • Estimate observation error variance • Develop observation operators and their adjoint operators

11. Mean Surface PM2.5 Concentration B E D A C

12. Surface PM2.5 Component Concentration A California B Idaho C Texas D Indiana E Pennsylvania

13. Vertical Profile of PM2.5 Components A California B Idaho C Texas D Indiana E Pennsylvania

14. Surface PM2.5 Background Error Bias

15. Surface PM2.5 Component Bias

16. Conclusions • Demonstrated a positive impact on CMAQ surface PM2.5 predictions for an urban/industrial haze episode using GOES AOD assimilation • Case studies for smoke aerosols and dust aerosols are pending because CMAQ development for biomass burning and dust aerosol sources is underway • Continue development of 3D-var AOD assimilation • Get guidance from air quality working group on how to move forward with GSI work