Transcript Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence MCIP2AERMOD: A Prototype Tool for Preparing Meteorological Inputs for AERMOD Neil Davis.

Partnership for AiR Transportation Noise and Emission Reduction
An FAA/NASA/TC-sponsored Center of Excellence
MCIP2AERMOD:
A Prototype Tool for Preparing
Meteorological Inputs for AERMOD
Neil Davis and Sarav Arunachalam
Institute for the Environment
University of North Carolina at Chapel Hill
Roger Brode
U.S. Environmental Protection Agency
Presented at the
7th Annual Models-3 CMAS Users Conference
October 6-8, 2008
Motivation
• Meteorological fields are key inputs for air quality modeling
• NWS data typically used in AERMOD modeling have some
limitations
– Observed sites may be far from source location, espl. RAOB sites
– Wind measurements at ASOS locations have large number of calm
measurements
– Gridded meteorological models potentially helpful
• For Hybrid (combining regional and local-scale) modeling, a
consistent set of meteorology for CMAQ and AERMOD simulations is
desirable
– Avoids inconsistent meteorological fields confounding differences in
AQM outputs
• EPA is exploring utilizing gridded met data and created MM5 –
AERMOD tool, which was helpful in developing this tool
• Using MCIP outputs helps using either MM5 or WRF to drive
AERMOD
– No transition needed down the road
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FAA Modeling Approach
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Approach
• Created Fortran-based utility with EDSS/Models-3 I/O API library
• Process 2002 MM5 simulations at 12-km through MCIP 2.3
• Use grid cell containing AERMOD source region for both surface and
upper air fields
– No interpolation is performed
• Make use of METCRO2D, METCRO3D, METDOT3D, and
GRIDDESC files from MCIP output
• Use all available fields directly from MCIP output as these are the
values CMAQ will be using
• Only calculate variables which are not in MCIP output
• Adjust for AERMOD time requirements (LST, some parameters
require noon LST values)
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Met Fields Directly from MCIP
• Sensible Heat Flux
Notes:
• Surface Roughness Length
• Some massaging of these
variables performed
• Surface Friction Velocity
• Wind Speed / Direction
• Temperature
• Surface Pressure
• Cloud Fraction
• Monin Obukhov Length
– Maximum / minimum
thresholds
– Units conversion
• Mechanical Mixing height is
both used directly and
calculated
– Calculated only for convective
conditions
• Convective Velocity Scale
• Convective Mixing Height
• Mechanical Mixing Height
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Fields Calculated in MCIP2AERMOD
• Mechanical Mixing Height
• Relative Humidity
• Potential Temperature gradient above convective mixing
height (VPTG), or lapse rate above mixing height
• Bowen Ratio
• Albedo
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AERMOD Study Location
• Red – Airport Location
• Blue – NWS Surface Site
• Green – RAOB Site
• T.F. Green Airport in
Providence, Rhode
Island
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Evaluation Simulations
• Developed AERMOD simulations for several pollutants using both
AERMET and MCIP2AERMOD meteorological outputs
– Benzene, Formaldehyde, Primary EC, PM2.5
– Will focus on PEC in this presentation
• Emissions inputs created using the FAA EDMS model to provide
hourly emissions estimates of aircraft activity at airport
• 2002 NWS values were processed through AERMET with constant
surface characteristics in time and space
– Midday Albedo 0.5
– Daytime Bowen ratio 1.0
– Surface Roughness 0.1
• Receptors were placed at the center of every census tract within a
50-km radius as well as at routine AQ monitor locations
• Our evaluation will look at comparing both meteorology fields as
well as the AERMOD concentrations from both simulations
• Diurnal plots are calculated using averages of annual data
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Evaluation of met. fields (1 of 4)
Surface Temperature
• Very high correlation between NWS and MCIP data
• Diurnal and monthly patterns match very well
• MCIP is slightly cooler overall
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Evaluation of met. fields (2 of 4)
Mechanical Mixing Height
• MCIP produces lower mixing heights at night than NWS, but
higher mixing heights in general
• MCIP also produces higher mixing heights during summer months
• High correlation, but MCIP results seem to fall into discrete bins
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Evaluation of met. fields (3 of 4)
Wind Speed
• Shows stronger winds with NWS data, both diurnal and seasonal
• NWS data is grouped into threshold values
• High correlation overall
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Evaluation of met. fields (4 of 4)
Wind Rose
• NWS has more calms
•MCIP has fewer high wind values
•Directionally MCIP shows more south westerly flow
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Meteorology comparison
• Good agreement across most variables
• Comparison of vertical data unavailable, due to lack of
data in the NWS simulation
– AERMET only calculates the lowest level values when onsite
data is not included
• Common meteorological parameters (i.e., Temp,
pressure, winds) show more agreement than other
parameters
• MCIP precipitation and clouds show large discrepancies
compared to NWS values (not presented here)
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Evaluation of AERMOD outputs for PEC (1 of 5)
• Only slight differences can be seen here
•Most perceivable changes occur away from the airport, deceptive
due to log scale
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Evaluation of AERMOD outputs for PEC (2 of 5)
Zoomed-in domain
• Significant changes in airport vicinity
• MCIP-based AERMOD shows higher concentrations
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Evaluation of AERMOD outputs for PEC (3 of 5)
Annual Average
Absolute Difference
• Maximum change of 0.1 ug/m3
• Largest changes to the North of the airport
• MCIP shown to have larger concentrations
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Evaluation of AERMOD outputs for PEC (4 of 5)
Comparison of Monthly Means
• MCIP data always higher
• Largest differences in the winter months
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Evaluation of AERMOD outputs for PEC (5 of 5)
Additional comparisons
• NWS has more lower concentration values
• MCIP has higher maximum concentrations
• Median, 25th and 75th percentiles are similar
• Good correlation overall
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Discussion
• New prototype tool developed to use gridded meteorology (from
either MM5 or WRF) for AERMOD
• Evaluation of tool performed for AERMOD study of T.F. Green
(Providence) airport emissions of several pollutants
• Comparison of meteorological fields showed reasonable agreement
for most variables
– Only limited comparison of upper air data was possible
• MCIP2AERMOD meteorology lead to higher concentrations
throughout the domain for PEC
• Despite magnitude differences, correlations were high between
model simulations
• Evaluation of outputs for other pollutants showed similar patterns
• Evaluation of AERMOD outputs with RIDEM field study at T.F. Green
is ongoing
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Future Work
• Complete evaluation of AERMOD inputs and outputs
using RIDEM field study data for 2005
• Explore sensitivity of AERMOD to different physics
options in MM5 (or WRF)
• Additional tests in MCIP2AERMOD
– Include only noon time Bowen ratio
– Investigate interpolation
– Allow user to override surface parameters
• Set AERMET surface fields to be closer in agreement to
the MCIP values and reevaluate
• Develop hybrid calculations of CMAQ and AERMOD
using consistent meteorology
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Acknowledgments
This work was funded by the FAA,
under Grant No.03-C-NE-MIT, Amendment No. 027
(w/ UNC-CH Subaward No. 5710002072)
06-C-NE-MIT, Amendment No. 002
(w/ UNC-CH Subaward No. 5710002208)
07-C-NE-UNC, Amendment No. 001
The Local Air Quality project is managed by Mohan Gupta.
Any opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the views of
the FAA, NASA or Transport Canada.
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