MM5-AERMOD Tool

Roger W. Brode

U.S. EPA/OAQPS

Air Quality Modeling Group

9th Conference on Air Quality Modeling

October 9, 2008

Research Triangle Park, NC
Outline

• Problem Statement and Objective• MM5-AERMOD Tool Description• Example Test Case – Detroit• Next Steps• Summary
AERMOD Met Data Issue

Problem Statement

• Meteorological data are key inputs to air quality m

such as AERMOD

• NWS met data currently used in most cases; how

– Met observation sites may not be representative of sou

(10’s-100’s km away)

– Representativeness issue has new dimensions given A

dependence on surface characteristics: roughness, Boalbedo

– Upper air data sparsely located, especially in mountain

• Significant gaps in NWS data for calms and varia

frequency of gaps has increased with ASOS and 

• Onsite meteorological data collection is expensiv

consuming
Potential Solution

• These problems may be alleviated by u

outputs from prognostic gridded meteormodels

• Gridded meteorological models routinel

generate datasets that could be beneficuse in dispersion models like AERMOD

– Gridded met data already used for regulato

modeling with CALMET/CALPUFF for long transport applications
OBJECTIVE:

Develop MM5-AERMOD Tool th

• Provides spatially consistent AERMOD inputs

– Grid cell selected based on application/source site; overcom

sparsity of observed data

– Surface and upper-air data located in same grid cell

– Hourly values available for every grid cell

• Allows AERMOD to use parameters calculated 

MM5’s advanced atmospheric physics (e.g., he

friction velocity, PBL height)

• Uses MM5 data to calculate other parameters 

previously calculated from NWS observations (econvective velocity scale)
Surface Roughness 

Bowen Ratio 

Albedo

User Input

NWS Obs

AERMET

To AERMOD

.sfc file .pfl file

Gridded outp

MM5

MM5 – AER

Tool

To AERM

.sfc file 

Traditional AERMET Application MM5-AERMOD Too
Example Test Case – Detroit 

• Apply MM5-AERMOD Tool

– Extract 2002 MM5 12km data for Detroit 

Metropolitan Airport (DTW) grid cell

– Sub-domain of 30 x 30 grid cells around Det

extracted from MM5 data for input to tool

• Apply traditional NWS-AERMET approa

– Use 2002 DTW Integrated Surface Data (TD

and DTX upper air data

• Analyze, compare, document results

MM5 12km Grid Cell for DTW
Detroit 2002 Windroses

NWS (DTW) MM5 (12k

Cell)

At anemometer height of 10m At first half-sigma leve
Detroit 2002 MM5-NWS AERM

Comparisons – Source Descript

* Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoy

non-buoyant (NB) release, and downwash (DW) or no downwash (ND)

** Hb = building height

Source 

ID * 

Height 

(m) 

Exit Temp. 

(K) 

Exit Velocity 

(m/s) 

Diameter 

(m) Do

00NBND 0.0 ambient 0.0 0.0 None – 

10BYND 10.0 300 15.0 0.5 None – 

10BYDW 10.0 293 1.0 2.4 Squat; s

corner 

10NBND 10.0 ambient 0.0 0.0 None – 

35BYND 35.0 432 11.7 2.4 None – 

35BYDW 35.0 432 11.7 2.4 Squat; s

corner 

35BYDW2 35.0 432 11.7 2.4 Squat; s

NE corn

50NBND 50.0 ambient 0.0 0.0 None – 

100BYND 100.0 425 26.5 5.6 None – 
Detroit 2002 MM5-NWS AERM

Comparisons – Model Results (C

* Source ID is a combination of the source release height (00, 10, 35, 50, or 100), b

non-buoyant (NB) release, and downwash (DW) or no downwash (ND)

** Hb = building height

24-hr Averages – Rural

100BYND 0.08 0.13 1.59 0.07 0.13 

50NBND 6.39 10.10 1.58 4.63 7.90 

35BYDW2 1.62 2.02 1.25 1.36 1.70 

35BYDW 23.16 25.93 1.12 22.37 25.41 

35BYND 2.17 2.02 0.93 2.03 1.70 

10BYDW 159.82 305.53 1.91 143.31 250.58 

10BYND 101.30 135.13 1.33 86.20 103.91 

10NBND 196.83 368.20 1.87 173.23 312.10 

00NBND 5194.18 69048.50 13.29 3825.26 27708.70 

Source ID AERMET MM5 Ratio AERMET MM5 

H1H (

μg m-3/g s-1

) MM5/NWS H2H (

μg m-3/g s-1

) 
Meteorology Comparison for 

H1H 24-hr Average Day – R

------- MM5 DATA --------

YR MN DY HR HFLUX USTAR WSPD WDIR

2 12 11 1 -3.5 0.030 1.62 226. 2 12 11 2 -2.1 0.021 1.38 223. 2 12 11 3 -0.9 0.011 1.09 221. 2 12 11 4 -0.6 0.009 0.77 220. 2 12 11 5 -0.6 0.009 0.49 217. 2 12 11 6 -0.6 0.009 0.20 216. 2 12 11 7 -0.6 0.009 0.12 27. 2 12 11 8 -0.7 0.010 0.42 33. 2 12 11 9 -0.7 0.011 0.64 39. 2 12 11 10 -0.8 0.013 0.73 45. 2 12 11 11 -0.9 0.020 0.70 52. 2 12 11 12 -0.4 0.122 0.54 65. 2 12 11 13 9.7 0.197 0.44 83. 2 12 11 14 8.4 0.185 0.38 116. 2 12 11 15 2.2 0.130 0.39 160. 2 12 11 16 -0.6 0.038 0.58 187. 2 12 11 17 -0.9 0.022 0.76 201. 2 12 11 18 -0.8 0.014 0.93 215. 2 12 11 19 -1.1 0.015 1.14 226. 2 12 11 20 -2.3 0.023 1.42 235. 2 12 11 21 -4.3 0.033 1.74 237. 2 12 11 22 -7.3 0.047 2.08 238. 2 12 11 23 -11.0 0.063 2.44 239. 2 12 11 24 -14.6 0.080 2.77 240.
Meteorology Comparison for 

H1H 24-hr Average Day – R

------- MM5 DATA -------- ------- NWS DATA 

YR MN DY HR HFLUX USTAR WSPD WDIR HFLUX USTAR WS

2 12 11 1 -3.5 0.030 1.62 226. -19.5 0.244 2.2 12 11 2 -2.1 0.021 1.38 223. -999.0 -9.000 0.2 12 11 3 -0.9 0.011 1.09 221. -999.0 -9.000 0.2 12 11 4 -0.6 0.009 0.77 220. -999.0 -9.000 0.2 12 11 5 -0.6 0.009 0.49 217. -999.0 -9.000 0.2 12 11 6 -0.6 0.009 0.20 216. -999.0 -9.000 0.2 12 11 7 -0.6 0.009 0.12 27. -999.0 -9.000 0.2 12 11 8 -0.7 0.010 0.42 33. -999.0 -9.000 0.2 12 11 9 -0.7 0.011 0.64 39. -999.0 -9.000 0.2 12 11 10 -0.8 0.013 0.73 45. 7.9 0.189 1.2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.252 2.2 12 11 12 -0.4 0.122 0.54 65. 13.7 0.196 1.2 12 11 13 9.7 0.197 0.44 83. 56.6 -9.000 0.2 12 11 14 8.4 0.185 0.38 116. 35.1 -9.000 0.2 12 11 15 2.2 0.130 0.39 160. 23.7 -9.000 0.2 12 11 16 -0.6 0.038 0.58 187. 6.4 -9.000 0.2 12 11 17 -0.9 0.022 0.76 201. -999.0 -9.000 0.2 12 11 18 -0.8 0.014 0.93 215. -999.0 -9.000 0.2 12 11 19 -1.1 0.015 1.14 226. -999.0 -9.000 0.2 12 11 20 -2.3 0.023 1.42 235. -999.0 -9.000 0.2 12 11 21 -4.3 0.033 1.74 237. -4.8 0.086 1.2 12 11 22 -7.3 0.047 2.08 238. -999.0 -9.000 0.2 12 11 23 -11.0 0.063 2.44 239. -999.0 -9.000 0.2 12 11 24 -14.6 0.080 2.77 240. -11.1 0.192 2.
Meteorology Comparison for 

H1H 24-hr Average Day – R

------- MM5 DATA -------- ------- NWS DATA 

YR MN DY HR HFLUX USTAR WSPD WDIR HFLUX USTAR WS

2 12 11 1 -3.5 0.030 1.62 226. -19.5 0.244 2.2 12 11 2 -2.1 0.021 1.38 223. -999.0 -9.000 0.2 12 11 3 -0.9 0.011 1.09 221. -999.0 -9.000 0.2 12 11 4 -0.6 0.009 0.77 220. -999.0 -9.000 0.2 12 11 5 -0.6 0.009 0.49 217. -999.0 -9.000 0.2 12 11 6 -0.6 0.009 0.20 216. -999.0 -9.000 0.2 12 11 7 -0.6 0.009 0.12 27. -999.0 -9.000 0.2 12 11 8 -0.7 0.010 0.42 33. -999.0 -9.000 0.2 12 11 9 -0.7 0.011 0.64 39. -999.0 -9.000 0.2 12 11 10 -0.8 0.013 0.73 45. 7.9 0.189 1.2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.252 2.2 12 11 12 -0.4 0.122 0.54 65. 13.7 0.196 1.2 12 11 13 9.7 0.197 0.44 83. 56.6 -9.000 0.2 12 11 14 8.4 0.185 0.38 116. 35.1 -9.000 0.2 12 11 15 2.2 0.130 0.39 160. 23.7 -9.000 0.2 12 11 16 -0.6 0.038 0.58 187. 6.4 -9.000 0.2 12 11 17 -0.9 0.022 0.76 201. -999.0 -9.000 0.2 12 11 18 -0.8 0.014 0.93 215. -999.0 -9.000 0.2 12 11 19 -1.1 0.015 1.14 226. -999.0 -9.000 0.2 12 11 20 -2.3 0.023 1.42 235. -999.0 -9.000 0.2 12 11 21 -4.3 0.033 1.74 237. -4.8 0.086 1.2 12 11 22 -7.3 0.047 2.08 238. -999.0 -9.000 0.2 12 11 23 -11.0 0.063 2.44 239. -999.0 -9.000 0.2 12 11 24 -14.6 0.080 2.77 240. -11.1 0.192 2.
MM5-NWS AERMOD Model Result

Using MM5 

z

0 ~ 0.3m for AERMET/

* Source ID is a combination of the source release height (00, 10, 35, 50, or 100), b

non-buoyant (NB) release, and downwash (DW) or no downwash (ND)

** Hb = building height

24-hr Averages – Rural

100BYND 0.08 0.13 1.59 0.07 0.13 

50NBND 6.39 10.10 1.58 4.63 7.90 

35BYDW2 1.62 2.02 1.25 1.36 1.70 

35BYDW 23.16 25.93 1.12 22.37 25.41 

35BYND 2.17 2.02 0.93 2.03 1.70 

10BYDW 159.82 305.53 1.91 143.31 250.58 

10BYND 101.30 135.13 1.33 86.20 103.91 

10NBND 196.83 368.20 1.87 173.23 312.10 

00NBND 5194.18 69048.50 13.29 3825.26 27708.70 

Source ID AERMET MM5 Ratio AERMET MM5 

H1H (

μg m-3/g s-1

) MM5/NWS H2H (

μg m-3/g s-1

) 
MM5-NWS AERMOD Model Result

Using AERSURFACE 

z

0 ~ 0.06m for AERM

* Source ID is a combination of the source release height (00, 10, 35, 50, or 100), bu

non-buoyant (NB) release, and downwash (DW) or no downwash (ND)

** Hb = building height

24-hr Averages – Rural

100BYND 0.06 0.13 2.01 0.06 0.13 

50NBND 6.85 10.10 1.48 4.65 7.90 

35BYDW2 1.97 2.02 1.03 1.59 1.70 

35BYDW 22.12 25.93 1.17 21.03 25.41 

35BYND 1.01 2.02 2.01 0.89 1.70 

10BYDW 182.36 305.53 1.68 157.35 250.58 

10BYND 69.61 135.13 1.94 56.21 103.91 

10NBND 153.62 368.20 2.40 147.02 312.10 

00NBND 10621.38 69048.50 6.50 10151.59 27708.70 

Source ID AERMET MM5 Ratio AERMET MM5 

H1H (

μg m-3/g s-1

) MM5/NWS H2H (

μg m-3/g s-1

) 
MM5-NWS AERMOD Model Result

Using AERSURFACE 

z

0 ~ 0.06m for AERM

NWS winds supplemented with 1-minute AS

* Source ID is a combination of the source release height (00, 10, 35, 50, or 100), b

non-buoyant (NB) release, and downwash (DW) or no downwash (ND)

** Hb = building height

24-hr Averages – Rural

100BYND 0.07 0.13 1.80 0.06 0.13 

50NBND 8.54 10.10 1.18 6.85 7.90 

35BYDW2 2.12 2.02 0.95 1.97 1.70 

35BYDW 23.22 25.93 1.12 22.59 25.41 

35BYND 1.19 2.02 1.71 1.01 1.70 

10BYDW 282.81 305.53 1.08 194.79 250.58 

10BYND 76.34 135.13 1.77 68.33 103.91 

10NBND 209.89 368.20 1.75 139.35 312.10 

00NBND 28588.16 69048.50 2.42 23844.40 27708.70 

Source ID AERMET MM5 Ratio AERMET MM5 

H1H (

μg m-3/g s-1

) MM5/NWS H2H (

μg m-3/g s-1

) 
Summary of MM5-AERMOD S

• Draft MM5-AERMOD Tool has been devel• Comparisons of MM5-AERMOD vs. traditio

AERMET approach for Detroit 2002

– Preliminary results encouraging, especially wh

AERSURFACE and 1-minute ASOS wind data 

• Basic approach is promising, but considera

remains before endorsing for regulatory mo
MM5-AERMOD Next Ste

• Conduct more detailed comparison of MM5-AERMOD Too

AERMET results, including meteorology and dispersio

n

• Conduct additional “sensitivity” analyses using meteorologi

from each approach, including:

– wider range of source types;– different options for interpolation of MM5 grid;– use of full profiles of MM5 wind and temperature vs. “degraded” subs– grid-to-grid variability across a domain; and– sensitivity to MM5 grid resolution

• Extend comparisons to other geographic areas; tentative p

Atlanta, GA, Birmingham, AL, and Baltimore, MD

• Coordinated collaboration with other researchers (e.g., UNC 

MCIP2AERMOD project with FAA)

• Assess use of multiple MM5 grids within single AERMOD a• Assess interim approach of deriving pseudo-UA data from 

processing through AERMET with site-specific or NWS sur

• “Validate” use of MM5-AERMOD data against field study da
MM5-AERMOD Next Steps (c

• Consider coupling MM5-AERMOD Tool with MM

CALPUFF Tool

• Extension of effort to support WRF model, possib

on an MCIP-to-AERMOD approach (MCIP being meteorological processor for CMAQ)

• Host invited workshop on use of gridded prognos

data with dispersion models to guide the best usscience

• As the range of options for developing met inputs 

models expands, need to develop criteria for acc

• Questions of how to disseminate this “technology

community will eventually need to be addressed
Questions?