A Dynamic Adaptive Grid Method for Improved Modeling of Biomass Burning Plumes

1. A Dynamic Adaptive Grid Method for Improved Modeling ofBiomass Burning Plumes M. Talat Odman and Yongtao Hu Georgia Institute of Technology D. Scott McRae North Carolina State University Gary L. Achtemeier USDA Forest Service 7th Annual CMAS Conference 6 October, 2008 Georgia Institute of Technology

2. Objective • To improve the prediction of air quality impacts from biomass burnings. • The focus is on prescribed burns and their impacts on local and regional air quality. Simulation with Air Quality Model Burning Options Prescribed Burns at Managed Lands Impact to Regional Air Quality Georgia Institute of Technology

3. Emission Estimation Field Measurements Model Development & Simulations Feedback Model Evaluation Approach • “Characterization of Emissions and Air Quality Modeling for Predicting the Impacts of Prescribed Burns at DoD Lands” Georgia Institute of Technology

4. Model Development • Biomass burning plumes are not well resolved in current regional-scale modeling systems due to: • insufficient grid resolution • inadequate subgrid treatments • A dynamic, solution-adaptive grid algorithm (DSAGA) will be used both in MM5 and CMAQ models to increase their resolutions. • Turbulence parameterization has already been revised in MM5. A subgrid scale plume model will be coupled with CMAQ. Georgia Institute of Technology

5. Dynamic Adaptive GridResolve selected features/characteristics/properties dynamically Georgia Institute of Technology

6. Incorporating DSAGA into CMAQ: Time Stepping • Process modules are called once every ∆t • There is one global ∆t for the entire domain • Since ∆x is constant max(u) determines Δt • For non-uniform grids, min(Δx) and max(u) determine Δt Georgia Institute of Technology

7. ∆t1 < ∆t2 t = N∆t2 ∆t1 > ∆t2 VARTSTEP Algorithm • Every cell is assigned its own local time step, Δti , • whichis an integer multiple of the global time step Δtand an integer divisor of 60 minutes. For example., if the global time step is 5 minutes, the local time step can be 5, 10, 15, 20, 30, or 60 minutes. • The model clock time, t, is advanced by the global time step • When t=NΔti processes are applied for the duration of Δti • Transport requires special attention. Georgia Institute of Technology

8. CMAQ VARTSTEP Dt ~ Dx/u u Dt v DIFFERENCE ≈ 1% Rotating Cone Test Georgia Institute of Technology

9. January 1-9, 2002 Simulation Dx = 12 km CPU savings with VARTSTEP ≈ 25% Georgia Institute of Technology

10. Adaptive Grid MM5 • NCSU Dynamic Solution Adaptive Grid Algorithm (DSAGA) • (r-)Refinement criteria selected beforehand (currently vorticity) • Code determines location and resolution automatically • Adapts in all three dimensions • The NCSU k-zeta (Enstrophy) hybrid turbulence model • Four equations based on exact equations derived from the Navier-Stokes and modeled term by term • MM5 has several sources of dissipation (e.g., Asselin filter) that limit the resolution • LES resolution of turbulence scales not yet achieved

11. 11 January 1972 Boulder Windstorm Turbulent breakdown of topographically forced gravity waves • 2-D test • Same setup as in Boyle et al. (2000)

12. Adaptive Grid at t=3h

13. Velocity Vectors at t=3h

14. Daysmoke • A dynamic-stochastic model consisting of : • Entraining turrets representing hot rising air which define the plume boundary, • Large eddy parameterization for plume deformation due to turbulent fluctuations • Detraining parcels that cross plume boundary due to stochastic plume turbulence • Multiple plume boundaries can exist simultaneously allowing Daysmoke to simulate complex plume structures (e.g., multiple-core updrafts)

15. Daysmoke z x y x

16. Subgrid Chemistry • Daysmoke is a non-reactive model. • Subgrid chemistry of mean parcel concentrations can be modeled by tagging them as “plume” and the rest of the grid cell as “ambient” (Parcel-Grid Method of Chock and Winkler, 1994). • Turbulent fluctuation correlations of concentrations can also be modeled (Advanced Plume Treatment of Karamchandani et al, 2000). Georgia Institute of Technology

17. Atlanta 28 February 2007 Atlanta Smoke Event • An opportunity to compare new model with base model in terms of agreement with regional observations Georgia Institute of Technology

18. Model Evaluation: Baseline Forecast, HindcastandObservedPM2.5 Indicator of success:improved agreement of predictions with observations Georgia Institute of Technology

19. Summary • In general, adaptive grid models produce more accurate solutions than their static grid counterparts with comparable or even larger computational demands. • The improved versions of the models are expected to result in better resolved dynamics, emissions, and chemical transformations as well as reduced numerical diffusion. • This will be checked by re-evaluation of the 28 February 2007 smoke event in Atlanta, which has already been simulated by using the current uniform grid MM5/CMAQ modeling system. Georgia Institute of Technology

20. Acknowledgements • Strategic Environmental Research and Development Program (SERDP) • Joint Fire Science Program (JFSP) • Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Georgia Institute of Technology