Regional Haze Modeling: Recent Modeling Results for VISTAS and WRAP
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Regional Haze Modeling:Recent Modeling Results for
VISTAS and WRAP
October 27, 2003, CMAS Annual Meeting, RTP, NC
University of California, Riverside
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Modeling Team Participants
• UC Riverside: Gail Tonnesen, Zion Wang, Chao-Jung Chien, Mohammad Omary, Bo Wang
• Ralph Morris et al., ENVIRON Corporation• Zac Adelman et al., Carolina Environmental
Program• Tom Tesche et al., Alpine Geophysics• Don Olerud, BAMS
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Acknowledgments
• Western Regional Air Partnership: John Vimont, Mary Uhl, Kevin Briggs, Tom Moore,
• VISTAS: Pat Brewer, Jim Boylan, Shiela Holman
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Topics
• Model Performance Evaluation
• WRAP 1996 Model Performance Evaluation
• VISTAS 2002 Sensitivity Results
• CMAQ Benchmarks
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WRAP Modeling
• 1996 Annual Modeling
• 36 km grid for western US, 95x85x18 layers
• MM5 by Olerud et al.
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WRAP Emissions Updates
• Corrections to point sources• MOBILE6 beta for WRAP states• Monthly corrections for NH3 based on EPA/ORD
inverse modeling.• Updated non-road model• Typical fires used for results shown here• 1996 NEI for non-WRAP states
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WRAP - CMAQ revisions
• v0301, released in March 2001– Used as the base case and all sensitivity cases for
WRAP’s 309 simulations.
• v0602, released in June 2002• v4.2.2, released in March 2003• v4.3, released in Sept. 2003
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Mean Normalized Bias (Yearly)
-200
0
200
400
600
800
1000
1200
1400
1600
1800
SO4 NO3 NH4 OC EC SOIL CM PM25 RCFM PM10 Bext
(%)
v0301 v422
v43
Comparisons based on IMPROVE evaluation
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• How well does the model reproduces mean, modal, and variational characteristics ?– Using observations to normalize model error &
bias result in misleading conclusion:• if observation is very small large bias or error• if model under prediction bounded by -1• model over prediction is weighted more than under
prediction
• We used Mean Normalized Err & Bias in 309:– Poor metric for clean conditions
Model Performance Metrics
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• Use fractional error and bias:– bias and error is bounded symmetrical limits of +2
• Normalized Mean Error & Bias:– Divide the sum of the errors by the sum of the
observations.
• Coefficient of determination (R2)– explains how much of the variability in the model
predictions can be explained by the fact that they are related to ambient observation, i.e. how close the points are to the observations.
Recommended Performance Metrics
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Statistical measures used in model performance evaluation
Measure Mathematical Expression
Notation
Accuracy of unpaired peak (Au)Opeak = peak observation; Pu
peak= unpaired peak prediction within 2 grid cells of peak observation site
Accuracy of paired peak (Ap)P = paired in time and space peak prediction
Coefficient of determination
Pi = prediction at time and location i;
Oi =observation at time and location i;
=arithmetic average of Pi, i=1,2,…, N;
=arithmetic average of Oi, i=1,2,…,N
Normalized Mean Error (NME) Reported as %
Root Mean Square Error (RMSE)
Fractional Gross Error (FE)
peak
peakupeak
O
OP
peak
peak
O
OP
N
i
N
iii
N
iii
OOPP
OOPP
1 1
22
2
1
)()(
))((
N
ii
N
iii
O
OP
1
1
2
1
1
21
N
iii OP
N
N
i ii
ii
OP
OP
N 1
2
PO
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Statistical measures used in model performance evaluation
Measure Mathematical Expression
Notation
Mean Absolute Gross Error (MAGE)
Mean Normalized Gross Error (MNGE); Mean Normalized Error (MNE)
Reported as %
Mean Bias (MB)
Mean Normalized Bias (MNB) Reported as %
Mean Fractionalized Bias (Fractional Bias, MFB)
Reported as %
Normalized Mean Bias (NMB) Reported as %
N
iii OP
N 1
1
N
i i
ii
O
OP
N 1
1
N
iii OP
N 1
1
N
i i
ii
O
OP
N 1
1
N
i ii
ii
OP
OP
N 1
2
N
ii
N
iii
O
OP
1
1
)(
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Statistical measures used in model performance evaluation
• In addition…– Mean observation– Mean prediction– Standard deviation (SD) of observation– Standard deviation (SD) of prediction– Correlation variance
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Expanded Model Evaluation Software to include…• Ambient data evaluation for air quality monitoring networks:
– IMPROVE (24-Hour average PM)– CASTNet (Weekly average PM & Gas)– STN (24-Hour average PM)– AQS (Hourly Gas)– NADP (weekly total deposition)– SEARCH
• 17 statistical measures in model performance evaluation• All performance metrics can be analyzed in an automated
process for model and data selected by:· allsite_daily · onesite_daily
· allsite_yearly · onesite_monthly
· allsite_monthly · onesite_yearly
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• Facilitate model evaluation.
• Benefit from shared development of tool.
• Share monitoring data.
• UCR software available at website:
www.cert.ucr.edu/aqm
Community Model Evaluation Tool?
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SO4, Monthly Statistical Measures
-50
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12
Month
%
Normalized Mean Error (%)
Mean Normalized Gross Error (%)
Mean Normalized Bias (%)
Mean Fractionalized Bias (%)
Normalized Mean Bias (%)
WRAP 1996 Evaluation, CMAQ v4.3
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NO3, Monthly Statistical Measures
-200
0
200
400
600
800
1000
1200
1 2 3 4 5 6 7 8 9 10 11 12
Month
%
Normalized Mean Error (%)
Mean Normalized Gross Error (%)
Mean Normalized Bias (%)
Mean Fractionalized Bias (%)
Normalized Mean Bias (%)
WRAP 1996 Evaluation, CMAQ v4.3
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OC, Monthly Statistical Measures
-50
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
Month
%
Normalized Mean Error (%)
Mean Normalized Gross Error (%)
Mean Normalized Bias (%)
Mean Fractionalized Bias (%)
Normalized Mean Bias (%)
WRAP 1996 Evaluation, CMAQ v4.3
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EC, Monthly Statistical Measures
-100
-50
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
Month
%
Normalized Mean Error (%)
Mean Normalized Gross Error (%)
Mean Normalized Bias (%)
Mean Fractionalized Bias (%)
Normalized Mean Bias (%)
WRAP 1996 Evaluation, CMAQ v4.3
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WRAP 1996 cases in progress
• New fugitive dust emissions model
• New NH3 emissions model
• Actual Prescribed & Ag burning emissions
• 2002 annuals simulations being developed.
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VISTAS Model 12 km Domain
• 34 L MM5 by Olerud
• 1999 NEI
• CMAQ v3
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VISTAS Sensitivity Cases
• 3 Episodes: Jan 2002, July 1999, July 2001• Sensitivity Cases
– MM5 MRF and ETA-MY, – PBL height, Kz_min, Layer collapsing– CB4-2002– SAPRC99– CMAQ-AIM– GEO-CHEM for BC– NH3 emissions
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VISTAS Key Findings
• NO3 over predictions in winter, under predictions in summer.– Thorton et al N2O5 had small benefit, July
MNB increased from –50% to –45%
• SO4 performance reasonably good• Problems with PBL height
– Kz_min = 1 improved performance– Investigating PBL height corrections
• Minor differences in 19 vs 34 layers
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Benchmarks
• Athlon MP 2000 (1.66 GHz)
• Opteron 246 (2.0 GHz)– 32 bit code– 64 bit code
• Compare 1, 4 and 8 CPUs.
• Ported CMAQ to the 64 bit SuSE– Pointers & memory allocation for 64 bit
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Test Case for benchmarks
• VISTAS 12 km domain– 168 x 177 x 19 layers
• Benchmarks for CMAQ 4.3• One day simulation, CB4, MEBI• Single CPU run time hour:minutes
– Athlon 2 GHz: 14:10– Opteron 32bit 2 GHz: 12:49– Opteron 64 bit 2 GHz: 10:57
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Clock Time by CPU Type
0
200
400
600
800
1000
1200
1 2 3 4 5 6 7 8
number CPUs
Tim
e (m
inu
tes)
Athlon 1.66 GHzOpteron-32Opteron-64Athlon 2.13 GHz
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Parallel Scaling by CPU type
0.125
0.150
0.175
0.200
0.225
0.250
0.275
0.300
0.325
2 4 6 8
number CPUs
Sc
ali
ng
Ra
tio
AthlonOpteron-32Opteron-64Perfect Scaling
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Parallel Scaling by CPU Type
1.00
1.10
1.20
1.30
1.40
1.50
1.60
1.70
1.80
1 2 3 4 5 6 7 8
number CPUs
actual/perfect
Athlon MPOpteron-32Opteron-64
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Optimal Cost Configuration
• Small cluster < 8 CPUs use Athlon
• Large cluster >16 CPUs use Opterons?
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Conclusions
• Major Improvements in WRAP 1996 Model
• WRAP 2002 annual modeling underway
• VISTAS Sensitivity Studies– still have problems in NO3– Need better NH3 inventory– Need more attention to PBL heights in MM5
• Community model evaluation tool?