Coupling a sub-grid scale plume model for biomass burns with adaptive grid CMAQ: part 2

Coupling a sub-grid scale plume model for biomass burns with adaptive grid CMAQ: part 2

Aika YanoFernando Garcia-Menendez

Yongtao HuM. Talat Odman

Gary Achtemeier (USDA Forest Service)

10th Annual CMAS Conference25 October, 2011

1

Motivation

• U.S. EPA lists prescribed burning as the 3rd 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 emissions3. Ground level concentration vs. all vertical layer

concentration adaptation (adapt in 2D)4. Adaptive weighting function parameters5. Frequency of tracer/wall analysis

3

Column Injection

Set distance

4

Handover

Remain in Daysmoke

sum of conc error vs. downwind distance

0

100000

200000

300000

400000

02000400060008000100001200014000160001800020000

downwind distance (meters)

sum

of co

nc

error

(micro

g/m

3)

Into CMAQ

5

Atlanta Smoke Case Feb. 28 2007

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 CMAQ uniform handover CMAQMean fractional error 76.27% 69.35%

Handover with uniform grid

14:2416:4819:1221:36 0:00 2:24 4:48 7:120

50

100

150

200Fire Station 8

14:2416:4819:1221:36 0:00 2:24 4:48 7:120

50

100

150Fort McPherson

uniform grid handover uniform grid

14:24 16:48 19:12 21:36 0:00 2:24 4:48 7:120

50

100

150South Dekalb

Observations

14:2416:48

19:1221:36

0:002:24

4:487:12

0

50

100

150 Jefferson St.

PM2.

5 (μ

g/m

3 )

7

Adaptive grid weighting function

8

Surface only vs. all layers concentration adaptation

3.6% improvement

surface adaptation all layer adaptationMean fractional error 80.47% 76.82%

9

Global vs. Output time step handover analysis

global handover handover per output

Mean fractional error 65.08% 65.19%

0

50

100

150Jefferson St.

PM2.

5 (μ

g/m

3 )

0

50

100

150South Dekalb

0

50

100

150

200 Fire Station 8

14:2416:48

19:1221:36

0:002:24

4:487:12

0

50

100

150

200Confederate Av.

Observations global output

10

Plume modeling criteriaUniform vs Adaptive grid

All layer adaption vs Surface layer adaption

Colum Injection vs Handover

Global time step handover vs Output time step handover

11

Adaptive grid weight function

12

Areal adaptation:e1

-0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 070.00%

72.00%

74.00%

76.00%

78.00% mean fractional error

e1

Smoothing factor: NSMTH

5 5.5 6 6.5 7 7.5 8 8.5 9 9.570.00%71.00%72.00%73.00%74.00%75.00%76.00%77.00%78.00%

Mean fractional error

NSM

3 4 5 6 7 8 9 10

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0NSMTH

e1

Mean Fractional Error

Smaller cells weighted more

C reduced𝛻

13

14:24 16:48 19:12 21:36 0:00 2:24 4:48 7:120

20406080

100120140160

Jefferson St.Co

ncen

trati

on (μ

g/m

3 )

14:24 16:48 19:12 21:36 0:00 2:24 4:48 7:120

50

100

150

200 McDonough

handover (5,-0.5)t2 handover (6,-0.6)t2 Observations handover (6,-0.5)t2handover (6,-0.3)t2 handover (6,-0.5)t1 14

Judging criteriaNormalized Mean Error (%)

Mean Fractional Error (%)

Peak Ratio (%)

Mass Ratio (%)

15

Error analysis (NSMTH,e1)

mass ratio difference

peak ratio difference

Normalized mean fractional error

Normalized abs. mean error 0

0.5

1

handover (5,-0.5)t2

handover (6,-0.5)t2

handover (6,-0.6)t2

handover (6,-0.3)t2

handover (8,-0.5)t2

handover (8,0)t2

16

handover (6,-0.5)t2

handover (5,-0.5)t2

handover (8,-0.5)t2

handover (8,0)t2

handover (6,-0.5)t1

handover (6,-0.3)t2

handover (6,-0.6)t2

handover (8,-0.3) t1

0

0.5

1

1.5

2

2.5

3Combined Error Analysisar

ea o

f the

err

or sq

uare

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

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.000.00E+00

5.00E+01

1.00E+02

1.50E+02

f(x) = 0.330905076722773 x + 11.5971413495838R² = 0.514226187034469

handover (5,-0.5)t2

Observed

Mea

sure

d

handover (8,-0.5)t2y = 0.274x + 13.47

R2 = 0.3703

0.00E+00

5.00E+01

1.00E+02

1.50E+02

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00

Observed

Mea

sure

d

handover (6,-0.5)t2y = 0.3196x + 11.866

R2 = 0.5251

0.00E+00

5.00E+01

1.00E+02

1.50E+02

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00

Observed

Mea

sure

d

handover (6,-0.5)t1y = 0.308x + 11.271

R2 = 0.4918

0.00E+00

5.00E+01

1.00E+02

1.50E+02

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00

Observed

Mea

sure

d

20

Adaptive Grid

21