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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, 2011. Motivation. - PowerPoint PPT Presentation
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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