Atmospheric Chemistry Measurement and Modeling Capabilities are Advancing on Many Fronts

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 < 1km O3 predicted “better” than CO Performance decreases with altitude Carmichael et al., JGR, 2003

3. Model vs. Observations + • 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 Modeled O3vs. Measured O3

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 dx (new) (initial condition for NWP) (old forecast)

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

10. Our Analysis Framework Influence Functions Emission Biases/ Inversion Mesoscale Meteorological Model (RAMS or MM5) MOZART Global Chemical Transport Model Anthropogenic & biomass burning Emissions Meteorological Dependent Emissions (biogenic, dust, sea salt) TOMS O3 STEM Tracer Model (classified tracers for regional and emission types) STEM Prediction Model with on-line TUV & SCAPE STEM Data- Assimilation Model Airmasses and their age & intensity Analysis Chemistry & Transport Analysis Observations

11. Assimilation of AIRNOW O3 surface observations for July 20, 2004 Observations: circles, color coded by O3 mixing ratio Surface O3 (forecast) Surface O3 (analysis)

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

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

14. Effect of O3 Assimilation on Forecast

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

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. 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 12 EDT July 20 (w/o (top) and w (bottom) assimilation)

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 • O3 and NO2 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. Integrated Global Atmospheric Chemistry Observation (IGACO) System Objectives: To ensure accurate, comprehensive global observations of key atmospheric gases and aerosols; To establish a system for integrating ground-based, in situ and satellite observations using atmospheric models; To make the integrated observations accessible to users. Implemented by WMO See Overleaf Observations Satellite IGACO System An international process: Panel of 19 experts from 12 countries and independent reviewers from 7 countries. Aircraft Products NO2 Ground-based Links to: Space agencies, WCRP, GCOS, IGBP, IGOS themes Ozone Depletion Climate Air Quality Lbarrie@wmo.int Joerg.Langen@esa.int