WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance Evaluation

WRAP Regional Modeling Center, Attribution of Haze Meeting, Denver 7/22/04

WRAP 2002 Visibility Modeling:Emission, Meteorology Inputs andCMAQ Performance Evaluation

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

WRAP Attribution of Haze Meeting, Denver, CO July 22, 2004


Summary of RMC 2002 Modeling

• Annual MM5 Simulations run at the RMC

• 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


MM5 Implications for AoH

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

• Expect some reduction in bias &error in the WRAP states, however even in the best case we will have error & bias in MM5 that must be considered when using CMAQ for source attribution.


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 EI Used here.

• Updates for final 2002 EI will include:– New EI data from other RPOs and Canada– 2002 NEI to replace grown 1996 NEI– Reprocess in SMOKE with final MM5– All final inputs ready now except Canada & MM5


CMAQ Simulations

• CMAQ v4.3• 36-km grid,

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

IMPROVE, CASTNet, NADP, STN, AIR/AQS


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.

• We completed a variety of analyses:– Compute over 20 performance metrics– Scatter-plots & time-series plots– Soccer plots– Bugle plots


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 for AoH:– 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.– Larger error & bias are observed 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.


Annual CMAQ vs IMPROVE


Spring Summer

Fall Winter


Annual CMAQ vs CASTNet


Spring Summer

Fall Winter


Annual CMAQ vs STN


Spring Summer

Fall Winter


Annual CMAQ vs NADP


Spring Summer

Fall Winter


Performance Goals and Criteria- Proposed by Jim Boylan

• Based on FE and FB 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 & EI Versions

• TSSA results are run in CMAQ v4.4 with emissions version Preliminary 2002 C

• Performance evaluation used CMAQ 4.3

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


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 v4.4


CMAQ Emissions B & C versus IMPROVE

Summer


Issues for AoH

• Is this set of Emissions/MM5/CMAQ adequate for studying AoH?

• Analysis of CMAQ performance on best & worst days still in progress: – However, we expect CMAQ will tend to over

predict lows & under predict highs.– Should we use CMAQ results unpaired in time?


Options for future work

• Continue CMAQ source apportionment with current data sets.

• Wait for new MM5 and emissions.

• Investigate other CMAQ configurations:– Unlikely to see large improvements

Documents

WRAP 2002 Visibility Modeling: Emission, Meteorology Inputs and CMAQ Performance Evaluation