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Operational Evaluation and Comparison of CMAQ and REMSAD- An Annual Simulation
Brian Timin, Carey Jang, Pat Dolwick, Norm Possiel, Tom Braverman
USEPA/OAQPS
October 22, 2002
Introduction
USEPA has performed an annual simulation of CMAQ and REMSAD for a 1996 base year
An operational evaluation has been completed for both models Model performance is difficult to summarize due to the
lack of ambient PM2.5 data (from 1996) Performance varies by season and by PM2.5 component
1996 National CMAQ and REMSAD- Model Setup
CMAQ- May 2001 release w/MEBI solver REMSAD- Version 7.01 Model Setup:
– Domain: CMAQ and REMSAD: 36km, 12 layers, ~38 m surface layer
– Emissions: CMAQ and REMSAD: 1996 NEI w/adjustments, processed via SMOKE
– Meteorology: 1996 MM5– Chemistry:
CMAQ: CB-IV chemical mechanism w/ fast solver (MEBI) REMSAD: micro-CB-IV chemical mechanism
CMAQ Modeling Domain
Nationwide Modeling Domains
REMSAD Modeling Domain
CMAQ National domain is a Lambert conformal projection from 100°W, 40°NREMSAD uses a lat-long projection
Notes on Emission Inventory
Base Year 1996 NEI w/adjustments Removal of wildfires, wind blown dust, and residential
on-site incineration Removal of commercial wood-fired combustion
– Maryland and Maine PM Transport Factor
– 75% reduction in fugitive dust sources Adjusted CA NOx and VOC (non-EGU)
Notes on Emission Inventory (con’t)
Revised Temporal Data– Prescribed burning– Animal husbandry
Used results from ORD inverse modeling (monthly reductions of 20-60%)
Annual NH3 inventory reduced by ~30%
– Crop fertilization / agricultural burning CMU NH3 inventory USDA Crop Calendar
Biogenic Emissions– BEIS 3.09
Model Performance- Ambient Data Issues
PM2.5 data is collected from a variety of networks with different measurement protocols and analysis techniques
– FRM PM2.5 – IMPROVE– Urban speciation sites– CASTNET dry deposition network– CASTNET visibility network– Continuous PM2.5 and speciation monitors– NADP wet deposition network
Certain measurements are highly uncertain It is a challenge to determine how to match model output to ambient
data– “Draft” data mapping will be provided
CMAQ and REMSAD Model Performance
Completed statistical comparison against observations for 12 layer REMSAD and CMAQ
Data sources: IMPROVE network; CASTNET dry dep. Network; NADP wet deposition network; CASTNET visibility network
All comparisons paired in time/space Statistics and scatterplots for seasonal and annual averages
– Calculated performance statistics by year and season for each monitoring site
Thousands of individual numbers; only presenting gross summary
Limited data base (in 1996) makes conclusive statements re: model performance difficult
Annual Average PM2.5
IMPROVE Annual Average Performance Statistics- REMSAD
IMPROVE PM Species National East WestPM2.5 -32% -15% -49%Sulfate Ion -19% -10% -39%Nitrate Ion 5% 82% -55%Elemental Carbon 1% 23% -20%Organic Aerosols -45% -42% -47%Soil/Other 38% 225% -18%
-Modeled PM2.5 is compared to measured PM2.5 fine mass
-Organic aerosols includes a 1.4 multiplication factor
-East/West is defined by 100th meridian
-Annual mean predicted/annual mean observed
-Negative numbers are underpredictions
IMPROVE Annual Average Performance Statistics- CMAQ
-Modeled PM2.5 is compared to measured PM2.5 fine mass
-Organic aerosols includes a 1.4 multiplication factor
-Annual mean predicted/annual mean observed
-Negative numbers are underpredictions
IMPROVE PM Species National East WestPM2.5 8% 3% 16%Sulfate Ion -2% 5% -19%Nitrate Ion 200% 262% 150%Elemental Carbon 14% -2% 28%Organic Aerosols 1% -30% 26%Soil/Other 80% 204% 43%
Seasonal Average Sulfate Performance
Seasonal Average Sulfate- IMPROVEAnnual REMSAD 1996 Modeling (nrd96)
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
Improve Observations (ug/m3)
RE
MS
AD
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Sulfate- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
Improve Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
July Average Sulfate
Seasonal Average Particulate Nitrate Performance
Seasonal Average Nitrate- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Improve Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Nitrate- IMPROVEAnnual REMSAD 1996 Modeling (nrd96)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Improve Observations (ug/m3)
RE
MS
AD
Pre
dic
tio
ns
(u
g/m
3)
Summer
Fall
Spring
Winter
Seasonal Average Total Nitrate Performance
Seasonal Average Total Nitrate Concentration- CASTNET
(dry dep. Network)Annual REMSAD 1996 Modeling (nrd96)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
CASTNET Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Total Nitrate Concentration- CASTNET
(dry dep. Network)Annual CMAQ 1996 Modeling (nrd96_vae2)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
CASTNET Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
January Average Particulate Nitrate
Differences in Winter Nitrate
Much of the difference in winter nitrate predictions between CMAQ and REMSAD can be traced to different implementations of the dry deposition routines
Nitrate concentrations were found to be sensitive to dry deposition of NH3, HNO3, and NO2
Improvements and adjustments are needed in both CMAQ and REMSAD, particularly in the areas of:
– Treatment of snowcover and freezing temperatures– Specification of land use and surface roughness – Treatment of soluble species when canopies are wet
January nitrate concentrations agreed to within ~25% after the dry deposition routines were made more similar to each other through a series of sensitivity runs (with REMSAD)
January Nitrate Comparison After Dry Deposition Sensitivities
Seasonal Average Organic Aerosols Performance
Seasonal Average Organic Aerosols- IMPROVEAnnual REMSAD 1996 Modeling (nrd96)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Improve Observations (ug/m3)
RE
MS
AD
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Organic Aerosols- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Improve Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
July Average Organic Aerosols
Seasonal Average Elemental Carbon Performance
Seasonal Average Elemental Carbon- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Improve Observations (ug/m3)
RE
MS
AD
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Elemental Carbon- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Improve Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
January Average Elemental Carbon
Seasonal Average Crustal/Other PM2.5 Performance
Seasonal Average Soil/Other PM2.5- IMPROVEAnnual REMSAD 1996 Modeling (nrd96)
0
1
2
3
4
5
6
0 1 2 3 4 5 6
Improve Observations (ug/m3)
RE
MS
AD
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
Seasonal Average Soil/Other PM2.5- IMPROVEAnnual CMAQ 1996 Modeling (nrd96_vae2)
0
1
2
3
4
5
6
0 1 2 3 4 5 6
Improve Observations (ug/m3)
CM
AQ
Pre
dic
tio
ns
(ug
/m3)
Summer
Fall
Spring
Winter
July Average Crustal/Other PM2.5
Winter Average NitrateCMAQ 1996 vs. Observed 2001-2002 (IMPROVE and Urban Speciation)
Qualitative comparison of spatial patterns with more recent urban speciation data
Model Performance- Summary of Individual Species
CMAQ tends to predict higher concentrations than REMSAD; especially in the West
REMSAD slightly underpredicts sulfate in the East; CMAQ slightly overpredicts sulfate
Nitrate is overpredicted in the East– Total nitrate (particulate + nitric acid) is overpredicted in all seasons
Indicates an overestimation of nitric acid
REMSAD underpredicts organic carbon; CMAQ is relatively unbiased– Large uncertainty in the primary organic inventory (no wildfires), the organic
measurements, and the secondary organic chemistry– CMAQ is predicting much more biogenic SOA; but it is using an aerosol yield approach
(AE2) Much of the biogenic SOA in REMSAD is being partitioned into the gas phase
Model Performance- Individual Species
Elemental carbon is generally unbiased– Large uncertainty in measurement of elemental carbon (EC/OC split)
IMPROVE sites have very low EC concentrations
Soil/other concentrations are overpredicted– Inventory issues
Fugitive dust, unspeciated emissions from construction, paved roads, etc. in urban areas
NADP wet concentration comparisons– Sulfate
CMAQ overpredicts in the East; REMSAD underpredicts – Nitrate
Both models overpredict in the East; REMSAD underpredicts in the West– Ammonium
REMSAD underpredicts; CMAQ slightly overpredicts in the East
Next Steps
Additional evaluation techniques can be applied– Further comparisons to more recent urban speciation data– Closer look at individual sites, days, seasons, regions
Time series plots 20% best/worst days for visibility
Plan to model 2001 base year – Significantly more ambient data available
Continue to look at PM monitoring issues and how they affect model performance evaluation
– Uncertainty in nitrate observed data– EC/OC split– Monitoring network protocol differences