1. Atmospheric Chemistry Measurement and Modeling Capabilities are Advancing on Many Fronts Closer Integration is Needed

2. Predictability – as Measured by Correlation Coefficient Met Parameters are Best Performance decreases with altitude < 1km O3 predicted “better” than CO Carmichael et al., JGR, 2003

3. Model vs. Observations Modeled O 3 vs. Measured O 3 Cost functional measures the model- observation gap. Goal: produce an optimal state of the atmosphere using:  Model information consistent with physics/chemistry  Measurement information consistent with reality  All with errors +

4. Challenges in chemical data assimilation A large amount of variables (~100 concentrations of various species at each grid points) –Memory shortage (check-pointing required) Various chemical reactions (>200) coupled together (lifetimes of different species vary from seconds to months) –Stiff differential equations Chemical observations are very limited, compared to meteorological data –Information should be maximally used, with least approximation Highly uncertain emission inventories –Inventories often out-dated, and uncertainty not well-quantified

5. Data assimilation methods Simple data assimilation methods –Nudging –Optimal Interpolation (OI) –3-Dimensional Variational data assimilation (3D-Var) –Ensembles Advanced data assimilation methods –4-Dimensional Variational data assimilation (4D-Var) Fisher and Lary (1995), AutoChem model CTMs with 4D-Var applications: STEM, EURAD, CHIMERE –Kalman Filter (KF) Many variations, e.g. Ensemble Kalman Filter (EnFK) CTMs with KF applications: EUROS, LOTOS, MOZART, EURAD

6. Extensive Real-Time Evaluation of Regional Forecasts – Stu McKeen http://www.etl.noaa.gov/programs/2004/neaqs/verification/

7. Forecast Skill (One Model vs Ensemble) -- observation-based bias corrections help Ensemble (8 models) One CTM model

8. 4D-Var data assimilation (old forecast) (new) (initial condition for NWP) xx

9. 4D-Var application with CTMs Observations Forward CTM model evolution Backward adjoint model integration Optimization Cost function Gradients Update control variables Checkpointing

10. Our Analysis Framework Mesoscale Meteorological Model (RAMS or MM5) MOZART Global Chemical Transport Model STEM Prediction Model with on-line TUV & SCAPE Anthropogenic & biomass burning Emissions TOMS O 3 Chemistry & Transport Analysis Meteorological Dependent Emissions (biogenic, dust, sea salt) STEM Tracer Model (classified tracers for regional and emission types) STEM Data- Assimilation Model Observations Airmasses and their age & intensity Analysis Influence Functions Emission Biases/ Inversion

11. Assimilation of AIRNOW O 3 surface observations for July 20, 2004 Observations: circles, color coded by O 3 mixing ratio Surface O 3 (forecast)Surface O 3 (analysis)

12. Assimilation of elevated observations for July 20, 2004 NOAA P3 flight observations Ozonesonde observations (Rhode Island ) We are exploring these issues with a new NOAA GCP grant

13. Change of Initial O 3 after Assimilation Date: July 20, 2004 Observations: AirNow, P3-O3, Ozonesonde Isosurfaces of relative changes: -20% (blue), +20% (yellow), +100% (red)

14. Effect of O 3 Assimilation on Forecast

15. Courtesy John Reilly, MIT Which species to assimilate ?

16. A Key Issue Is Which Data To Assimilate -- Example Impact of Assimilating NOy Leads to improved prediction of NO, NO2, PAN, and HNO3

17. Modeling the Background Error Term AR Models Improved 4D-Var Results

18. 12 EDT July 20 (w/o (top) and w (bottom) assimilation) 4d-Var data assimilation results are visibly improved when using the new AR background covariance Observation error 8%; I.C. error 10ppbv; Initial ozone is control

19. Ensemble-based Chemical Data Assimilation Formulation and Challenges Examples

20. Experimental setting of the ensemble-based data assimilation system 50 members, perturbed I.C., B.C., and emissions 30% initial std, AR correlations + TESV perturbations O 3 and NO 2 observations at 24 ground locations in 3 countries, and in one vertical column. Perturbation 0.1% std, uncorrelated Quality of analysis in a sub-domain including observation sites

21. Continued Improvement in the Forward Models are Needed: Effects of Physical Removal Processes – which are significant sources of uncertainty High Dry Dep Case Change in surface ozone (ppb) With/W-o wet dep Change in column BC

22. Improving Emissions is a Top Priority: Models, Emissions, and Observations are not Perfect – Inverse Modeling

23. Where do we go from here? Example of Use of 3-D CFORS modeling system at TRACE-P Information Day in Hong Kong

24. Chemical Data Assimilation Feasible & necessary. Just the beginning— more ??s than answers – we need test beds! Important implications for measurement systems and models. Need to grow the community.

27. Objectives: 1.To ensure accurate, comprehensive global observations of key atmospheric gases and aerosols; 2.To establish a system for integrating ground- based, in situ and satellite observations using atmospheric models; 3.To make the integrated observations accessible to users. Lbarrie@wmo.int Joerg.Langen@esa.int An international process: Panel of 19 experts from 12 countries and independent reviewers from 7 countries. Integrated Global Atmospheric Chemistry Observation (IGACO) System Satellite Observations Aircraft Ground-based IGACO System Links to: Space agencies, WCRP, GCOS, IGBP, IGOS themes Implemented by WMO See Overleaf NO 2 Products