an assessment of cmaq with teom measurements over the eastern us
Post on 20-Jan-2016
25 Views
Preview:
DESCRIPTION
TRANSCRIPT
An Assessment of CMAQ with TEOM Measurements over the
Eastern US
Michael Ku, Chris Hogrefe, Kevin Civerolo, and Gopal Sistla
PM Model Performance Workshop, February 10-11, 2004, RTP, NC
Model Simulations
• MM5 – 108/36/12 km two-way nesting.
• SMOKE – 1996 CSA emission inventory.
• CMAQ – 12 km domain only; both CB-IV and RADM2; IC/BC used background values.
• Simulation Period – July 2 – August 1, 1999
TEOM Measurements
21 sites include SLAMS, USDOE, NEOPS, and SEARCH.
TEOM MeasurementsOrganization # of Sites ID used in analysis
Iowa (SLAMS) 6 1 - 6
New Jersey (SLAMS) 5 7 - 11
New York (SLAMS) 1 12
North Carolina (SLAMS) 1 13
South Carolina (SLAMS) 2 14 - 15
USDOE (PA) 2 16 - 17
SEARCH (AL & GA) 3 18 - 20
NEOPS (PA) 1 21
Total 21
20 40 60 80 100 120 140 160
20
40
60
80
100
120
140
Modeling Domain and TEOM Sites
Model Evaluation
• Examine the Model Error
• Examine the Model skill
-- Compare the spatial structures
-- Compare the temporal patterns
Statistics: Hourly Data
Parameter TEOM Observed
CMAQ
CB-IV
CMAQ
RADM2
Mean 22.28 29.99 28.05
S.D. 14.29 25.37 23.89
R 0.46 0.46
Mean Bias 7.8 5.78
RMSE 23.76 22.26
Comparison at each site
-20
-10
0
10
20
30
40
50
1 3 5 7 9 11 13 15 17 19 21
Site CB-IV RADM2
0
10
20
30
40
50
60
1 3 5 7 9 11 13 15 17 19 21
Site CB-IV RADM2
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1 3 5 7 9 11 13 15 17 19 21
Site CB-IV RADM2
Statistics: Daily Averaged Data
Parameter TEOM
observed
CMAQ
CB-IV
CMAQ
RADM2
Mean 22.23 29.99 28.05
S.D. 11.59 22.60 21.24
R 0.57 0.57
Mean Bias 7.80 5.75
RMSE 19.98 18.38
CMAQ (CB-IV) predicts slightly higher daily averaged values
than CMAQ (RADM2).
0 20 40 60 80 100 1200
20
40
60
80
100
120Daily Averaged Data
CB-IV
RA
DM
2
0 20 40 60 80 100 1200
20
40
60
80
100
120
TEOM
CM
AQ
(C
B-I
V)
CMAQ (CB-IV) underpredicted low-end and overpredicted high end of the daily averaged values.
0 20 40 60 80 100 1200
20
40
60
80
100
120
TEOM
CM
AQ
(R
AD
M2
)
CMAQ (RADM2) underpredicted low-end and overpredicted high end of the daily averaged values.
Compare Spatial Structures
-Calculate Cross-correlation coefficients of TEOM measurements and CMAQ outputs at the TEOM sites. The calculations yield a 21x21 symmetric matrix of correlation coefficients which represent the correlation of the sites with each other.
-If CMAQ produces similar correlation coefficients matrix with TEOM, the CMAQ is able to capture the TEOM measured spatial structures.
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 3 5 7 9 11 13 15 17 19 21
2
4
6
8
10
12
14
16
18
20
site
site
TEOM
0
0
0
0
0
0
0.2
0.2
0.2
0.2
0.20.2
0.2
0.2
0.2
0.2
0.20.2
0.20.2
0.2
0.2
0.2
0.4
0.4
0.4 0.4
0.4
0.4
0.4
0.6
0.6
0.6
0.6
0.6
0.8 0.8
0.8
0.8
0.6
0.6
-0.2 -0.2
-0.2
-0.2
0.4
0.4
0.40.4
0.4
0.4
-0.2
-0.2
-0.2-0.2
0.4
0.4
0.6
0.8
0.8
0.6
0.4
0.4
-0.2
-0.2
0
0
0.4
0.4
0.6
0.6
0.8
0
0
0.8
0.6
0.8
0.8
0.80.8
0.6
0.6
0.80
0 -0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 3 5 7 9 11 13 15 17 19 21
2
4
6
8
10
12
14
16
18
20
site
site
CB-IV0
0
00
0
00
0.2
0.2
0.2
0.2
0.2
0.20.2
0.2
0.2
0.2 0.2
0.20.2
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.6
0.6
0.6
0.6
0.6
0.6
0.80.8
0.80.8
0.8
0.8
0.8
0.4
0.4
0.4
0.4
0.4
0.40.4 0.6
0.6
-0.2
-0.2
-0.2-0.2
0.60
0
0
00.
4
0.4 0.4
0000
-0.2
0
0
0
0
0.8
0.6
0.6
0.8
-0.2
-0.2
-0.2
0.6
0.6
0.6
0.8
0.8
0.8-0.2
The similarity of the two contour plots indicates that CMAQ (CB-IV) is able to capture the spatial pattern of the TEOM measured data
Compare Temporal Patterns
• Hourly time series
• Synoptic components
• Diurnal variation
Hourly time series: Examples of good comparison
2 6 10 14 18 22 26 300
50
100
150
ug
/m3
TEOM CB-IVRADM2
2 6 10 14 18 22 26 300
50
100
150
ug
/m3
2 6 10 14 18 22 26 300
50
100
150
Day
ug
/m3
R = .62
R = .61
site: 7
site: 10
R = .78 site: 16
Hourly time series: Examples of poor comparison
2 6 10 14 18 22 26 300
20
40
60
80
ug
/m3
TEOM CB-IVRADM2
2 6 10 14 18 22 26 300
20
40
60
80
100
ug
/m3
2 6 10 14 18 22 26 300
50
100
150
Day
ug
/m3
R = -.05 site: 2
R = .3 site: 13
R = .2 site: 19
Examine the Synoptic Components
• KZ filter is used to extract the Synoptic Components from TEOM measurements and CMAQ predicted data.
• Compare the Synoptic Components for data averaged over three regions: Iowa, Northeast, and SEARCH.
Iowa Region
2 6 10 14 18 22 26 300
5
10
15
20
25
30
35
Day
ug
/m3
TEOMCMAQ
Northeast Region
2 6 10 14 18 22 26 300
10
20
30
40
50
60
70
80
Day
ug
/m3
TEOMCMAQ
SEARCH
2 6 10 14 18 22 26 300
10
20
30
40
50
60
Day
ug
/m3
TEOMCMAQ
0 3 6 9 12 15 18 2120
30
40
50Diurnal variation
ug
/m3
0 3 6 9 12 15 18 2120
30
40
50
ug
/m3
TEOM CB-IVRADM2
0 3 6 9 12 15 18 2120
30
40
50
Hour
ug
/m3
site = 7
site = 10
site = 16
Diurnal variation: Examples of good hourly time series comparison.
0 3 6 9 12 15 18 210
10
20
30Diurnal variation
ug
/m3
TEOM CB-IVRADM2
0 3 6 9 12 15 18 2120
30
40
ug
/m3
0 3 6 9 12 15 18 210
20
40
60
Hour
ug
/m3
site = 2
site = 13
site = 19
Diurnal variation: Examples of poor hourly time series comparison.
SUMMARY• CMAQ overpredicted TEOM measurements at high end
and underpredicted at low end.• CMAQ captured the spatial pattern of the TEOM
measurements.• TEOM measurements and CMAQ predictions show no
typical diurnal variation. • CMAQ performed well in capturing the average synoptic
temporal pattern in the northeast region, but failed to capture the temporal pattern in the other two regions.
• Analysis should be expanded to include PM speciation data.
top related