Transcript Evaluate CMAQ PM2.5 simulations with TEOM measurements

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 Measurements
Organization
Iowa (SLAMS)
New Jersey (SLAMS)
New York (SLAMS)
North Carolina (SLAMS)
South Carolina (SLAMS)
USDOE (PA)
SEARCH (AL & GA)
NEOPS (PA)
Total
# of Sites
6
5
1
1
2
2
3
1
21
ID used in analysis
1-6
7 - 11
12
13
14 - 15
16 - 17
18 - 20
21
Modeling Domain and TEOM Sites
140
120
100
80
60
40
20
20
40
60
80
100
120
140
160
Model Evaluation
• Examine the Model Error
• Examine the Model skill
-- Compare the spatial structures
-- Compare the temporal patterns
Statistics: Hourly Data
Parameter
Mean
22.28
CMAQ
CB-IV
29.99
S.D.
14.29
25.37
23.89
R
0.46
0.46
Mean Bias
7.8
5.78
23.76
22.26
RMSE
TEOM
Observed
CMAQ
RADM2
28.05
Comparison at each site
Statistics: Daily Averaged Data
Parameter
Mean
TEOM
observed
22.23
CMAQ
CB-IV
29.99
CMAQ
RADM2
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
Daily Averaged Data
120
100
RADM2
80
60
40
20
0
0
20
40
60
CB-IV
80
100
120
CMAQ (CB-IV) predicts slightly higher daily averaged values
than CMAQ (RADM2).
120
CMAQ (CB-IV)
100
80
60
40
20
0
0
20
40
60
TEOM
80
100
120
CMAQ (CB-IV) underpredicted low-end and overpredicted
high end of the daily averaged values.
120
CMAQ (RADM2)
100
80
60
40
20
0
0
20
40
60
TEOM
80
100
120
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.
site
15
17
19
21
0.6
-0.2
00.6
.4
-0.2
0.6
2 0.8
1
3
0.8
5
7
0.4 0
0.2
0.6
0.4
0.2
0.3
0.2
0.1
0
0
0
4
0.4
0.
4
0.2
-0.2
0
0 .4 0.6
site
0
0.6
0.4
0
0.8
0.8
0.6 0.6
0.2 0.4
0
0
9
-0
.2
0
0
-0.1
.4
00.2
6
0.8
0
13
0.6
8
0.4
0
0
11
0
0.8
0.4
0.4 0.60.2
-0.2
0.4
0.8
0.6
0.4
0.
0.6 2
site
-0.2
0.
6
0
0.2.4
7
0.
8
0.5
0.4
0
5
0
0.4
0.6
-0.
2
-0.2
3
0
9
10
0.6
0.7
0
1
0.1
0
12
.8
0.6 0
0.2
0.4
0.2
-0.2
-0
.2
0.8
0
2
0.8
-0.2
0.8
0.4
0.4
0.3
0.2
14
0
.
04
0.2
0.4
0.8
0.4
0.8
4
-0.2
0.8
0.6
0.5
0.6
6
0.6
0.8
0.6 0.4
0.2
16
-0.2
0.6
0.2
0.4
0.2
8
0.8
0.4
0
0.6
0.2
0.6
0
0.2 0.4
0.4
0
0
0.8
0.2
-0.2
10
0.8
0.8
0.6
0.2
-0.2
0.
2
18
0.4
0.2
0
12
0.4
0.6
0.7
0.4
0
8
0.
0.2
0
2
-0.
-0.2
14
0.4 0.2 0.6
0.4
6 8 0.2
0.40.0.
0.2
20
0.8
0.4 0.2
0
-0.2
0.8
16
0.2
0.4
0.4
0.8
0.06.8
0.6
0.2
-0.2
-0.2
0.2
0
18
0.2
0.8
0
0.4
0
CB-IV
0.4
0.2
20
TEOM
0.60.4
11
13
-0.1
15
17
19
21
-0.2
site
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
ug/m3
150
R = .62
100
TEOM
CB-IV
RADM2
site: 7
50
0
2
6
10
14
18
22
26
30
ug/m3
150
site: 10
R = .61
100
50
0
2
6
10
14
18
22
26
30
ug/m3
150
site: 16
R = .78
100
50
0
2
6
10
14
18
22
Day
Hourly time series: Examples of good comparison
26
30
ug/m3
80
TEOM
CB-IV
RADM2
R = -.05
60
40
site: 2
20
0
2
6
10
14
18
22
26
30
ug/m3
100
R = .3
80
site: 13
60
40
20
0
2
6
10
14
18
22
26
30
ug/m3
150
R = .2
100
site: 19
50
0
2
6
10
14
18
22
Day
Hourly time series: Examples of poor comparison
26
30
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.
35
TEOM
CMAQ
30
ug/m3
25
20
15
10
5
0
2
6
Iowa Region
10
14
18
Day
22
26
30
80
TEOM
CMAQ
70
60
ug/m3
50
40
30
20
10
0
2
6
10
Northeast Region
14
18
Day
22
26
30
60
TEOM
CMAQ
50
ug/m3
40
30
20
10
0
2
6
SEARCH
10
14
18
Day
22
26
30
Diurnal variation
50
ug/m3
site = 7
40
30
20
TEOM
CB-IV
RADM2
0
3
6
9
12
15
18
21
ug/m3
50
site = 10
40
30
20
0
3
6
9
12
15
18
21
ug/m3
50
site = 16
40
30
20
0
3
6
9
12
15
18
21
Hour
Diurnal variation: Examples of good hourly time series comparison.
Diurnal variation
ug/m3
30
TEOM
CB-IV
RADM2
20
site = 2
10
0
0
3
6
9
12
15
18
21
ug/m3
40
30
20
site = 13
0
3
6
9
12
15
18
21
ug/m3
60
40
20
0
site = 19
0
3
6
9
12
15
18
21
Hour
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.