1. Chemical Data Assimilation in Support of Chemical Weather Forecasts Greg Carmichael, Adrian Sandu, Dacian Daescu, Tianfeng Chai, John Seinfeld, Tad Anderson, Peter Hess, Dacian Daescu Data Assimilation

2. Chemical Data Assimilation in Support of Chemical Weather Forecasts Outline  Motivation  Current State of Forward Models  Data Assimilation Framework (4d- Var) – Issues  Preliminary Results  Future Directions

3. Models are an Integral Part of Atmospheric Chemistry Studies Flight planning Provide 4-Dimensional context of the observations Facilitate the integration of the different measurement platforms Evaluate processes (e.g., role of biomass burning, heterogeneous chemistry….) Evaluate emission estimates (bottom-up as well as top-down) Emission control strategies testing Air quality forecasting

4. TRACE-P/Ace- Asia/ITCT-2K1 EXECUTION Emissions -Fossil fuel -Biomass burning -Biosphere, dust Long-range transport from Europe, N. America, Africa ASIA PACIFIC Satellite data in near-real time: MOPITT TOMS SEAWIFS AVHRR LIS 3D chemical model forecasts: - x - GEOS-CHyEM - CFORS - z FLIGHT PLANNING Boundary layer chemical/aerosol processing ASIAN OUTFLOW Stratospheric intrusions PACIFIC

5. Forward Models Are becoming More Comprehensive 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

6. Fight Planning: Frontal outflow of biomass burning plumes E of Hong Kong Observed CO –Sacshe et al. Observed aerosol potassium - Weber et al. Biomass burning CO forecast Longitude 100 ppb

7. Predictability – as Measured by Correlation Coefficients Met Parameters are Best Performance decreases with altitude < 1km

8. 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 +

9. Development of a General Computational Framework for the Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints (NSF ITR/AP&IM 0205198 – Started Fall 2002) A collaboration between: Greg Carmichael (Dept. of Chem. Eng., U. Iowa) Adrian Sandu (Dept. of Comp. Sci., Virginia Tech.) John Seinfeld (Dept. Chem. Eng., Cal. Tech.) Tad Anderson (Dept. Atmos. Sci., U. Washington) Peter Hess (Atmos. Chem., NCAR) Dacian Daescu (Dept. Math, Portland State) http://atmos.cgrer.uiowa.edu/people/tchai/

10. Basic Idea of 4D-Var Define a cost functional Derive adjoint of tangent linear model Where adjoint variables are the sensitivities of the cost functional with respect to state variables (concentrations), i.e. Update Initial conditions using the gradients Useful by themselves !!

11. Assimilation Results  Assimilate O 3 /NO 2 with O 3 /NO 2 observations in the window [0,6] GMT, March 01, 2001;  Twin experiments framework;  Full 3D simulation with SAPRC chemical mechanism. O3O3

12. CO-assimilation

13. Observation Frequency vs Number of Species O3O3 O 3 - only O 3 & NO 2

14. Recovery of O 3 and NO 2 is Different WHY? NO 2 O3O3

15. Most of the grid points values are recovered within in 1%; but some locations the error is > 20%. 1% 20% Assimilation requires better algorithms (with known error behavior) Additional details see Chai’s paper on Thursday

16. Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimil. of chemical data. Ensemble methods

17. Chemical Assimilation and Big-Iron “BIGMAC”@VT Ranked 3rd with measured performance = 10 Tflop/s. A Pentium class cluster with 16-24 processors has ~ 50 Gflop/sec. On such a cluster we run parallel STEM (TraceP): 1 hour simulation time / 5 minutes cpu time On the terrascale machine we can run in parallel an ensemble of 200 simulations for the same simulation / cpu time ratio.

18. Assimilation of Aerosol Dynamics Theoretical framework enables the solution of coupled coagulation and growth with minimal number of size bins; Piecewise polynomial discretizations; Adjoint/assimila- tion system built Data FrequencyGradient Methods Recovery of Initial Distribution

19. We plan to test some of these developments in an operational setting this summer as part of a large field experiment.

20. We are Developing General Software Tools to Facilitate the Close Integration of Measurements and Models The framework will provide tools for: 1) construction of the adjoint model; 2) handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis of results; 6) remote access to data and computational resources. Adjoints being developed for MOZART, plans for WRF-Chem http://atmos.cgrer.uiowa.edu/people/tchai/

21. Chemical Data Assimilation: The Future? Feasible & necessary. Just the beginning— more ??s than answers – but we have test beds! Huge implications for measurement systems and models. Need to grow the community. TWO-SCENARIO FORECAST

23. http://www.wmo.ch/web/arep/gaw/urban.html

24. Air Quality Forecasting Research Elements Summary of USWRP Air Quality Forecasting Workshop April 29 - May 1, 2003 Houston, TX