Coupling a sub-grid scale plume model for biomass burns with adaptive grid CMAQ: part 2 Aika Yano Fernando Garcia-Menendez Yongtao Hu M. Talat Odman Gary.

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Presentation transcript:

Coupling a sub-grid scale plume model for biomass burns with adaptive grid CMAQ: part 2 Aika Yano Fernando Garcia-Menendez Yongtao Hu M. Talat Odman Gary Achtemeier (USDA Forest Service) 10 th Annual CMAS Conference 25 October,

Motivation U.S. EPA lists prescribed burning as the 3 rd largest source of fine particulate matter. Smoke plume must be preserved in order to predict downwind concentration accurately in CMAQ Use of sub grid model and adaptive grid can help resolve smoke plume in CMAQ 2

Objective Find out the ideal relationship between sub-grid model coupling and grid adaptation for burn plumes. 1.Effectiveness of the coupling method, handover, on both uniform and adaptive grid. 2.Different methods of inserting smoke emissions 3.Ground level concentration vs. all vertical layer concentration adaptation (adapt in 2D) 4.Adaptive weighting function parameters 5.Frequency of tracer/wall analysis 3

Column Injection Set distance 4

Handover Remain in DaysmokeInto CMAQ 5

Atlanta Smoke Case Feb Oconee NF (1542 acres) 5 yr old 10:00 am - 3:00 pm total 5hr Piedmont NWR (1460 acres) 1 yr old 11:00 am - 2:00 pm total 3hr 6

uniform CMAQuniform handover CMAQ Mean fractional error 76.27%69.35% Handover with uniform grid 7

Adaptive grid weighting function 8

Surface only vs. all layers concentration adaptation 3.6% improvement surface adaptationall layer adaptation Mean fractional error80.47%76.82% 9

Global vs. Output time step handover analysis global handoverhandover per output Mean fractional error 65.08%65.19% 10

Plume modeling criteria UniformvsAdaptive grid All layer adaptionvsSurface layer adaption Colum InjectionvsHandover Global time step handovervsOutput time step handover 11

Adaptive grid weight function 12

Areal adaptation: e1 Smoothing factor: NSMTH Mean Fractional Error Smaller cells weighted more C reduced 13

14

Judging criteria 15

Error analysis (NSMTH,e1) 16

NSMTH=6, e1=-0.5 using global time step handover analysis with adaptive grid CMAQ models this burn case plume the best. 17

Summary Boundary between sub grid model to CMAQ should be determined by analysis on error due to unresolved plume concentration Optimal parameters for grid adaptation were determined for prescribed burning plumes Similar analysis to be done for other burn cases 18

Acknowledgements Strategic Environmental Research and Development Program Joint Fire Science Program Georgia Department of Natural Resources, Environmental Protection Division US Forest Service, Southern Research Station 19

20

Adaptive Grid 21