REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5 METEOROLOGICAL FIELDS FOR VARIOUS AIR QUALITY MODELING APPLICATIONS

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REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5
METEOROLOGICAL FIELDSFOR VARIOUS AIR QUALITY
MODELING APPLICATIONS

• Pat Dolwick, U.S. EPA, RTP, NC, USA
• Rob Gilliam, NOAA, RTP, NC, USA
• Lara Reynolds and Allan Huffman, CSC, RTP, NC,
  USA
• 6th Annual CMAS Conference, Chapel Hill, NC,
  October 1-3, 2007

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Meteorological Model Evaluation Principles

• Evaluation goal(s)
• Move toward an understanding of how bias/error of
  the meteorological data impact the resultant AQ
  modeling
• Move away from an as is acceptance of met
  modeling data
• Assess model performance at the scales over which
  the meteorological data will ultimately be used
• National/Regional CMAQ or other grid modeling
  analyses
• Local AERMOD or other plume modeling analyses
• Two specific objectives within broader goals
• Determine if the meteorological model output
  fields represent a reasonable approximation of
  the actual meteorology that occurred.
  (Operational)
• Identify and quantify the existing biases and
  errors in the meteorological predictions in order
  to allow for a downstream assessment of how AQ
  modeling results are affected by issues.
  (Phenomenological)

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EPA 2002 MM5 Model Configuration

• 36 12 km modeling
• 36 km v.3.6.0 using land-surface modifications
  that were added in v3.6.3
• 12 km MM5 v3.7.2.
• Both domains contained 34 vertical layers with a
  38 m surface layer and a 100 mb top.
• Both sets of model runs were conducted in 5.5 day
  segments with 12 hours of overlap for spin-up
  purposes.
• Analysis nudging was utilized outside of the PBL
  for temperatures and water vapor mixing ratios,
  in all locations for wind components, using
  relatively weak nudging coefficients.
• The Atmospheric Model Evaluation Tool (AMET) was
  used to conduct the evaluation analyses
• as described in by Gilliam et al (2005).

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Operational evaluation national/regional 12km
Eastern US statistics
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Operational evaluation - precipitation 12km
Eastern US statistics
Best Case May 2002
Worst Case Oct 2002
Note scales are different between months
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Operational evaluation sample local 12 km
results in Birmingham AL Detroit MI
Temperature
Water Vapor Mixing Ratio
Wind Speed
Wind Direction
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Operational evaluation sample local 12 km
results in Birmingham AL Detroit MI
Birmingham AL (Q3 Jul-Sep)
Detroit MI (Q1 Jan - Mar)
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Phenomenological evaluation national/regionalAs
sessment of cold bias by time of day
Winter
Summer

• Observations
• Winter time cold bias is strongest at night
• Summer model overnight temperatures decrease at
  a slower rate than observed. As nocturnal layer
  is mixed, Slight warm bias rapidly gives way to
  small cool bias.

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Phenomenological evaluation national/regionalSe
asonal averages of performance aloft key sites
Spring
Fall

• Observations
• Generally, average potential temperatures, RH,
  and wind vectors are well-captured in the PBL.
• In general, differences are greatest in the
  lowest 1km.

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Meteorological Model Evaluation Conclusions (1)

• Both sets of 2002 MM5 meteorological model output
  fields (36 12km) represent a reasonable
  approximation of the actual meteorology that
  occurred during the modeling period at a national
  level. It is expected that these sets of input
  meteorological data are appropriate for use in
  regional and national air quality modeling
  simulations.
• For local scale analyses using these data, it is
  recommended that a detailed, area-specific
  evaluation be completed before using in a local
  application.
• The most troublesome aspect of meteorological
  model performance is the cold bias in surface
  temperatures during the winter of 2002,
  especially in January.
• Across the two MM5 simulations, the January cold
  bias typically averaged around 2-3 deg C. The
  effect is largest overnight which results in a
  tendency to overestimate stability in the lowest
  layers. These artifacts from the meteorological
  modeling have had a significant impact on the air
  quality results.

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Meteorological Model Evaluation Conclusions (2)

• This summary presentation represents only a small
  subset of the actual evaluation analyses
  completed.
• The 2002 MM5 model evaluation is not complete. We
  would like to do more analysis on cloud coverage,
  planetary boundary layer heights, as well as try
  to assess model performance as a function of
  meteorological regime.