Chemical Data Assimilation in Support of Chemical Weather Forecasts

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

5. Influence Functions Emission Biases/ Inversion Forward Models Are becoming More Comprehensive 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

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

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

8. 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 Modeled O3vs. Measured O3

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. Useful by themselves !! • Update Initial conditions using the gradients

11. Assimilation Results • Assimilate O3/NO2 with O3/NO2observations in the window [0,6] GMT, March 01, 2001; • Twin experiments framework; • Full 3D simulation with SAPRC chemical mechanism. O3

12. CO-assimilation

13. Observation Frequency vs Number of Species O3 & NO2 O3 O3 - only

14. Recovery of O3 and NO2 is Different WHY? NO2 O3

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

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 Gradient Methods Data Frequency • Theoretical framework enables the solution of coupled coagulation and growth with minimal number of size bins; • Piecewise polynomial discretizations; • Adjoint/assimila-tion system built 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. http://atmos.cgrer.uiowa.edu/people/tchai/ Adjoints being developed for MOZART, plans for WRF-Chem

21. TWO-SCENARIO FORECAST 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.

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