gail tonnesen, bo wang, chao-jung chien, zion wang, mohammad omary

CMAQ Evaluation Preliminary 2002 version C

WRAP 2002 Visibility Modeling:Annual CMAQ Performance Evaluation using

Preliminary 2002 version C Emissions

Gail Tonnesen, Bo Wang, Chao-Jung Chien, Zion Wang, Mohammad OmaryUniversity of California, Riverside

Zac Adelman, Andy HollandUniversity of North Carolina

Ralph Morris et al.ENVIRON Corporation Int., Novato, CA


Summary of RMC 2002 Modeling

• Annual MM5 Simulations run at the RMC in December 2003 (additional MM5 testing in progress)

• Emissions processed with SMOKE– Preliminary 2002 Scenario C used here.

• CMAQ version 4.3 (released October 2003)

• Data summaries, QA, results are posted on the RMC web page: www.cert.ucr.edu/aqm/308


MM5 Modeling Domain (36 & 12 km)

• National RPO grid– Lambert conic Projection– Center: -97o, 40o

– True lat: 33o, 45o

• MM5 domain– 36 km: (165, 129, 34)

– 12 km: (220, 199, 34)

• 24-category USGS data– 36 km: 10 min. (~19 km)

– 12 km: 5 min. (~9 km)


MM5 Physics

Physics Option Configuration Configure.user

Microphysics Reisner2 (with graupel) IMPHYS = 7

Cumulus Scheme Kain-Fritsch ICUPA = 6

PBL Pleim-Chang (ACM) IBLTYP = 7

Radiation RRTM FRAD = 4

Land-surface model Pleim-Xiu ISOIL = 3

Shallow Convection No ISHALLO = 0

Snow Cover Effect Simple snow model ISNOW = 2

Thermal Roughness Garrat IZ0TOPT = 1

Varying SST Yes ISSTVAR = 1

Time step 90 seconds (PX uses an internal timestep of 40 seconds)


Subdomains for 36/12-km Model Evaluation

1 = Pacific NW

2 = SW

3 = North

4 = Desert SW

5 = CenrapN

6 = CenrapS

7 = Great Lakes

8 = Ohio Valley

9 = SE

10 = NE

11 = MidAtlantic


Evaluation Review

• Evaluation Methodology– Synoptic Evaluation– Statistical Evaluation using METSTAT and surface data

• WS, WD, T, RH– Evaluation against upper-air obs

• Statistics:– Absolute Bias and Error, RMSE, IOA (Index of Agreement)

• Evaluation Datasets:– NCAR dataset ds472 airport surface met observations– Twice-Daily Upper-Air Profile Obs (~120 in US)

• Temperature• Moisture


METSTAT Evaluation Package

• Statistics:– Absolute Bias and Error, RMSE, IOA

• Daily and, where appropriate, hourly evaluation• Statistical Performance Benchmarks

– Based on an analysis of > 30 MM5 and RAMS runs – Not meant as a pass/fail test, but to put modeling results

into perspective Wind Speed Wind Direction Temperature Humidity RMSE 2 m/s Mean Bias 0.5m/s 10 0.5K 1g/kg Index of Agreement 0.6 0.8 0.6 Gross Error 30 2K 2g/kg


Evaluation of 36-km WRAP MM5 Results

• Model performed reasonably well for eastern subdomains, but not the west (WRAP region)– General cool moist bias in Western US– Difficulty with resolving Western US orography?

• May get better performance with higher resolution– Pleim-Xiu scheme optimized more for eastern US?

• More optimization needed for desert and rocky ground?

• MM5 performs better in winter than in summer– Weaker forcing in summer

• July 2002 Desert SW subdomain exhibits low temperature and high humidity bias

2002 MM5 Model Evaluation 12 vs. 36 km Results Chris Emery, Yiqin Jia, Sue Kemball-Cook, and Ralph Morris (ENVIRON International Corporation) & Zion Wang (UCR CE-CERT), Western Regional Air Partnership (WRAP) National RPO Meeting, May 25, 2004


WRAP 36km/12km July Wind Performance Comparison

0

20

40

60

80

100

120

0 0.5 1 1.5 2 2.5 3 3.5

Wind Speed RMSE (m/s)

Win

d D

irec

tio

n E

rro

r (d

egre

es)

Benchmark 12 km Subdomains MM5/RAMS Runs 36 km Subdomains

DesertSW

North

SWPacNW


WRAP 36km/12km July Temperature Performance Comparison

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Temperature Bias (K)

Tem

ep

ratu

re E

rro

r (K

)

Benchmark 12 km Subdomain MM5/RAMS Runs 36 km Subdomains

DesertSW

SW

North

PacNW

Desert SW

SWNorth

PacNW


WRAP 36km/12km July Humidity Performance Comparison

0

1

2

3

4

5

-3 -2 -1 0 1 2 3

Humidity Bias (g/kg)

Hu

mid

ity

Err

or

(g/k

g)

Benchmark 12km Subdomains MM5/RAMS Runs 36 km Subdomains

DesertSW

NorthSWPacNW


Additional MM5 Testing

• The RMC is continuing to test alternative MM5 configurations – to be completed at the end of 2004.

• Final MM5 results will be used with final 2002 emissions inventory, beginning early 2005.


Emissions Inventory Summary

• Preliminary 2002 Scenario C based on the 1996 NEI, grown to 2002, with many updates by WRAP contractors and other RPOs.

• Processed for CMAQ using SMOKE.

• Extensive QA plots on the web page– Both SMOKE QA and post-SMOKE QA


Emissions Sources by Category & RPO

WRAP CENRAP VISTAS LADCO MANE-VU MexicoArea Pecahn - v1 Pechan - v1 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO

Point Pecahn - v1 Pecahn - v1 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO

Paved Road Dust ENVIRON ENVIRON ENVIRON ENVIRON ENVIRON BRAVO

Seasonal, Interpolation between 1996 and 2018Unpaved Road Dust ENVIRON ENVIRON ENVIRON ENVIRON ENVIRON BRAVO

Seasonal, Interpolation between 1996 and 2018On Road Mobile ENVIRON 1999 NEI 96 NEI (grown) 1999 NEI 1999 NEI BRAVO

VMT VMT VMT VMTOff Road Mobile ENVIRON 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) 96 NEI (grown) BRAVO

Biogenic VISTAS VISTAS VISTAS VISTAS VISTAS VISTAS

Ag Fires Air Sci. (2018 base) Non included in the Area included in the Area included in the Area Non

Rx Fires Air Sci. (2002) Non included in the Area included in the Area included in the Area Non

Wild Fires Air Sci. (2002) Non Non Non Non Non

SourceRegion


WRAP 2002 Annual NOx Emissions

AreaBiogenicOn RoadNon RoadRoad DustPointRx FireAg FireWildfireOffshore


2002 WRAP NOx Emissions by Source & State

0

200000

400000

600000

800000

1000000

1200000

1400000

Ariz

ona

Cal

iforn

ia

Colo

rado

Idah

o

Monta

na

Nev

ada

New

Mex

ico

North

Dak

ota

Ore

gon

South

Dak

ota

Uta

h

Was

hingto

n

Wyo

min

g

[To

ns/

Yr]

Ag Fire

Rx Fire

Wildfire

Area

Point

Nonroad

Onroad


WRAP 2002 Annual SO2 Emissions

AreaBiogenicOn RoadNon RoadRoad DustPointRx FireAg FireWildfireOffshore


2002 WRAP SO2 Emissions by Source & State

0.00E+00

5.00E+04

1.00E+05

1.50E+05

2.00E+05

2.50E+05

3.00E+05

Ariz

ona

Cal

iforn

ia

Colo

rado

Idah

o

Monta

na

Nev

ada

New

Mex

ico

North

Dak

ota

Ore

gon

South

Dak

ota

Uta

h

Was

hingto

n

Wyo

min

g

[To

ns

/Yr]

Onroad

Ag Fire

Rx Fire

Wildfire

Area

Nonroad

Point


2002 WRAP NH3 Emissions by Source Category

0.00E+00

5.00E+04

1.00E+05

1.50E+05

2.00E+05

2.50E+05

Ariz

ona

Cal

iforn

ia

Colo

rado

Idah

o

Monta

na

Nev

ada

New

Mex

ico

North

Dak

ota

Ore

gon

South

Dak

ota

Uta

h

Was

hingto

n

Wyo

min

g

To

ns

/Yr

Nonroad

Ag Fire

Rx Fire

Point

Onroad

Wildfire

Area


Emissions Summary• Preliminary 2002 version C EI Used here.• Next iteration is version D, will include:

– New EI data from other RPOs. – New NH3 EI– Fugitive Dust Model

• Final 2002 EI will include:– 2002 NEI– Reprocess in SMOKE using final MM5– Canada point source emissions.


CMAQ Simulations• CMAQ v4.3• 36-km grid,

112x148x19• Annual Run• CB4 chemistry• Evaluated using:

IMPROVE, CASTNet, NADP, STN, AIR/AQS

• BC from 2001 GEOS-CHEM global model (Jacob et al)


PM Performance Criteria

• Guidance from EPA not yet ready:– Difficult to assert that model is adequate.– Therefore, we use a variety of ad hoc

performance goals and benchmarks to display CMAQ results.


Goal of Model Evaluation

• We completed a variety of analyses:– Compute over 20 performance metrics

– Scatter-plots & time-series plots

– Soccer plots

– Bugle plots

• Goal is to decide whether we have enough confidence to use the model designing emissions control strategies:– Is this a valid application of the model?


Soccer Goal Plots

• Plot error as as a function of bias.• Ad hoc performance goal:

– 15% bias, 35% error based on O3 modeling goals.

– Too demanding for PM and clean for western conditions?

– Larger error & bias are observed can exist among different PM data methods and monitoring networks.

• Performance benchmark:– 30% bias, 70% error (2x performance goals)

– PM models can achieve this level in many cases.


CMAQ vs. IMPROVE Summary

• SO4: negative bias in summer, and positive bias in winter, good performance in spring and fall.

• NO3: large negative bias in summer, large positive bias in winter, and small bias but large error in March and October.

• OC: large negative bias in summer, small positive bias in winter.

• EC: Good performance each month.

• Coarse Mass: generally large negative bias

• Soil: Small bias most months, except large positive bias in winter

• PM2.5 and PM10: CMAQ over predicts in winter, under predicts in summer, small bias in spring and fall.


CMAQ vs. CASTNet Summary

• CMAQ performance is better for CASTNet (longer averaging period helps) but has same trend as IMPROVE: over prediction in winter and under prediction in summer.

• SO4 & NO3: large negative bias in summer, large positive bias in winter.

• In summer both SO2 and SO4 are under predicted, in winter both are over predicted (thus problem is not in partitioning)

• Total Nitrate (NO3+HNO3) is much better than aerosol nitrate performance, probably reflects errors in sampling.


CMAQ vs. STN Summary

• NO3: Large negative bias each month.

• SO4: Negative bias in winter.

• EC: Positive bias in summer.

• Generally good performance for other species, within performance benchmarks.


CMAQ vs. NADP Summary

• CMAQ over predicts wet dep for SO4, NO3 and NH4.

• Generally small positive bias but large error terms.

• Largest positive bias is in summer (opposite of other networks)


Annual Average metrics: CMAQ vs IMPROVE


Spring Summer

Fall Winter


Annual Average Metrics: CMAQ vs CASTNet


Spring Summer

Fall Winter


Annual CMAQ vs STN


Spring Summer

Fall Winter


Annual CMAQ vs NADP


Spring Summer

Fall Winter


WRAP 2002 CMAQ Pre02c RunMonthly Analysis


SO4 Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

CASTNET

STN

NADP


SO4 Monthly Error, WRAP region

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE

CASTNET

STN

NADP


NO3 Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

CASTNET

STN

NADP


NO3 Monthly Error, WRAP region

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE

CASTNET

STN

NADP


NH4 Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

CASTNET

STN

NADP


NH4 Monthly Error, WRAP region

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

CASTNET

STN

NADP


OC Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

STN


OC Monthly Error, WRAP region

0

30

60

90

120

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE

STN


EC Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

STN


EC Monthly Error, WRAP region

0

30

60

90

120

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE

STN


SOIL and CM Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE-SOIL

IMPROVE-CM


SOIL and CM Monthly Error, WRAP region

0

30

60

90

120

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE-SOIL

IMPROVE-CM


PM25 and PM10 Monthly Bias, WRAP region

-80

-60

-40

-20

0

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE-PM25

IMPROVE-PM10

STN-PM25


PM25 and PM10 Monthly Error, WRAP region

0

30

60

90

120

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

E (

%)

IMPROVE-PM25

IMPROVE-PM10

STN-PM25


Comparison of VISTAS CMAQ in VISTAS states to

WRAP CMAQ in WRAP States

CMAQ Evaluation Preliminary 2002 version CSO4 Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

CASTNET

STN

NADP

-40

-20

0

20

40

60

80

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Oct

ober

Novem

ber

Decem

ber

IMPROVECASTNetSTNSEARCHSEARCH_HNADP

VISTAS

WRAP


NO3 Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

MonthM

FB

(%

)

IMPROVE

CASTNET

STN

NADP

-200

-150

-100

-50

0

50

100

150

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Octobe

r

Novem

ber

Decem

ber

IMPROVECASTNetSTNSEARCHSEARCH_HNADP

VISTAS

WRAP


OC Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

STN

-120

-100

-80

-60

-40

-20

0

20

40

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Oct

ober

Novem

ber

Decem

ber

IMPROVE STN SEARCH SEARCH_H


EC Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE

STN

-120

-100

-80

-60

-40

-20

0

20

40

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Oct

ober

Novem

ber

Decem

ber

IMPROVE STN SEARCH SEARCH_H


SOIL and CM Monthly Bias, WRAP region

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Month

MF

B (

%)

IMPROVE-SOIL

IMPROVE-CM

-50

0

50

100

150

200

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Octo

ber

Novem

ber

Decem

ber

Soil-IMPSoil-SECM-IMP


Summary of WRAP & VISTAS

• VISTAS Sulfate performance much better:– Southeast SO4 levels are much higher than

WRAP

• WRAP EC performance is better:– Order of EC reversed for IMPROVE and STN.

• Coarse mass lower in WRAP

• Similar performance for other species.


Performance Goals and Criteria- Proposed by Jim Boylan

• Based on MFE and MFB calculations

• Vary as a function of species concentrations– Goals: FE +50% and FB ±30%

– Criteria: FE +75% and FB ±60%

– Less abundant species should have less stringent performance goals and criteria


Performance Goals and Criteria- Proposed by Jim Boylan

• PM Performance Goals

• Proposed PM Performance Criteria

501503/75.0

)(5.0

mg

CC mo

eFE

301703/5.0

)(5.0

mg

CC mo

eFB

751253/75.0

)(5.0

mg

CC mo

eFE

601403/5.0

)(5.0

mg

CC mo

eFB


Monthly SO4 Fractional Bias

SO4

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10

Average Concentration (ug/m3)

Fra

ctio

nal

Bia

s (%

)

IMPROVE

CASTNet

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly SO4 Fractional Error

SO4

0

50

100

150

200

0 2 4 6 8 10


Fra

ctio

na

l E

rro

r (%

)

IMPROVE

CASTNet

STN

Goal

Criteria


Monthly NO3 Fractional Bias

NO3

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10


Fra

ctio

na

l B

ias

(%

)

IMPROVE

CASTNet

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly NO3 Fractional Error

NO3

0

50

100

150

200

0 2 4 6 8 10


Fra

cti

on

al E

rro

r (%

)

IMPROVE

CASTNet

STN

Goal

Criteria


Monthly NH4 Fractional Bias

NH4

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8


Fra

ctio

na

l B

ias

(%

)

CASTNet

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly NH4 Fractional Error

NH4

0

50

100

150

200

0 2 4 6 8


Fra

ctio

na

l E

rro

r (%

)

CASTNet

STN

Goal

Criteria


Monthly OC Fractional Bias

OC

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10 12 14 16


Fra

ctio

nal

Bia

s (%

)

IMPROVE

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly OC Fractional Error

OC

0

50

100

150

200

0 2 4 6 8 10 12 14 16


Fra

cti

on

al E

rro

r (%

)

IMPROVE

STN

Goal

Criteria


Monthly EC Fractional Bias

EC

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8


Fra

ctio

na

l B

ias

(%

)

IMPROVE

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly EC Fractional Error

EC

0

50

100

150

200

0 2 4 6 8


Fra

cti

on

al E

rro

r (%

)

IMPROVE

STN

Goal

Criteria


Monthly PM25 Fractional Bias

PM25

-200

-150

-100

-50

0

50

100

150

200

0 8 16 24 32 40


Fra

ctio

na

l B

ias

(%

)

IMPROVE

STN

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria


Monthly PM25 Fractional Error

PM25

0

50

100

150

200

0 8 16 24 32 40


Fra

cti

on

al E

rro

r (%

)

IMPROVE

STN

Goal

Criteria


CMAQ Versions & EI Versions

• Performance evaluation used CMAQ 4.3

• Previous CMAQ runs used CMAQ 4.3 with Preliminary 2002 B emissions (no fires)

• January & July test case using CMAQ v4.4beta with emissions version Preliminary 2002 C


CMAQ v4.3 & v4.4 versus IMPROVE

July


CMAQ Ozone Performance

• CMAQ v4.3 Mean fractional bias (no filter)January +25% MFBJuly –20% mean MFB

• Slightly worse January O3 performance in CMAQ v4.4beta


CMAQ Emissions B & C versus IMPROVE

Summer


2002 CMAQ Model Performance for Best and Worst 20% Days

• Observed and estimated extinction (Bext) calculations at each WRAP IMPROVE sites– Site-specific f(RH) adjustment factors– Rank days by observed total extinction (Mm-1)

• BTot = BSO4 + BNO3 + BOC + BEC + BSoil + BCM + BRay

• Examine performance at each site for each component of extinction average across Worst and Best 20% days


bNO3 (US)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

bSO4 (US)

0

10

20

30

40

50

0 10 20 30 40 50

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

bOC (US)

0

80

160

240

320

400

0 80 160 240 320 400

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

bEC (US)

0

40

80

120

160

0 40 80 120 160

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

Kalmiopsis

Kalmiopsis

Model Performance for Average

of Worst 20% Days at

WRAP IMPROVE

Sites

Preliminary 2002 CMAQ Simulation

SO4

NO3

OC

EC


bCM (US)

0

5

10

15

20

25

0 5 10 15 20 25

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

bSOIL (US)

0

4

8

12

16

20

24

28

32

36

40

0 4 8 12 16 20 24 28 32 36 40

Obs (bEXT 1/Mm)

CM

AQ

(b

EX

T 1

/Mm

)

Kalmiopsis

Kalmiopsis

Phoenix

Saguro

Model Performance for Average of Worst 20% Days at WRAP IMPROVE Sites


Coarse Matter (CM) and Soil


bSO4 (US)

0

3

6

9

12

15

0 3 6 9 12 15

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

bNO3 (US)

0

2

4

6

8

10

12

14

0 5 10 15

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

bOC (US)

0

5

10

15

20

25

0 5 10 15 20 25

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

bEC (US)

0

3

6

9

12

15

0 3 6 9 12 15

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

Model Performance

for Average of Best 20%

Days at WRAP IMPROVE Sites


SO4

NO3

OC

EC


bCM (US)

0

3

6

9

12

0 3 6 9 12

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

bSOIL (US)

0

2

4

6

8

10

0 2 4 6 8 10

Obs (bEXT 1/Mm)

CM

AQ

(bE

XT

1/M

m)

Model Performance for Average of Best 20% Days at WRAP IMPROVE Sites


Coarse Matter (CM) and Soil


0

5

10

15

20

25

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

0

5

10

15

20

25

30

35

40

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

Grand Canyon NP, AZ

Chiricahua NM, AZ

Extinction (Mm-1) model performance

for average of Worst 20%

observed days

Grand Canyon

Chiricahua


0

5

10

15

20

25

30

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

0

5

10

15

20

25

30

35

40

45

50

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

Bandelier NM, NM

Rocky Mtn. NP, CO



observed days

Bandelier NM

Rocky Mountain NP


0

10

20

30

40

50

60

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

0

10

20

30

40

50

60

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

Yellowstone NP, WY

Glacier NP, MT



observed days

Yellowstone

Glacier


0

10

20

30

40

50

60

70

80

90

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

0

100

200

300

400

500

600

700

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

Mount Ranier NP, WA

Kalmiopsis, OR

Extinction (Mm-1) model

performance for average of Worst

20% observed days

Mount Ranier

Kalmiopsis


0

20

40

60

80

100

120

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

0

10

20

30

40

50

60

70

80

90

100

Obs CMAQ

bEX

T (

1/M

m) bCM

bSOILbECbOCbNO3bSO4

Point Reyes, CA

San Gorgoinio, CA


performance for average of Worst

20% observed days

Point Reyes

San Gorgonio


0

2

4

6

8

10

12

Obs CMAQ

bEX

T (

1/M

m)

bCMbSOILbECbOCbNO3bSO4

0

1

2

3

4

5

6

7

8

9

10

Obs CMAQ

bEX

T (

1/M

m)

bCMbSOILbECbOCbNO3bSO4

Yellowstone NP, WY

Grand Canyon NP, AZ


performance for average of Best 20% observed

days

Grand Canyon

Yellowstone


Conclusions

• Positive bias in winter, negative bias in summer.

• CMAQ meets “benchmark” goals for most species and networks.

• Disagreements among different monitoring networks.

• Negative bias for summer O3 might contribute to negative PM bias.


Next Steps• Analysis of CMAQ performance on best & worst days

still in progress: – However, we expect CMAQ will tend to over predict lows

& under predict highs.– Recommend using CMAQ results unpaired in time for each

month or season.

• Is this set of Emissions/MM5/CMAQ adequate for developing emissions control strategies?

• Will we get performance improvements with new MM5 and new EI?

• New NH3 EI and might improve performance.

gail tonnesen, bo wang, chao-jung chien, zion wang, mohammad omary

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