This document discusses the practices and challenges of applying mesoscale meteorological data to air quality analyses. It provides an overview of common air quality models and their meteorological data requirements. While mesoscale data is widely used, there are challenges to address differences in perspectives between meteorology and air quality modeling, issues of model compatibility and data availability, and validating data quality for typical air quality applications. Overall, mesoscale data plays an important role in air quality modeling, though continued development is needed to better meet the needs of the air quality modeling community.

1. Prepared by:
Weiping Dai, Ph.D., P.E., C.M.
Tao Yang
BREEZE Software
12700 Park Central Drive | Suite 2100 | Dallas, TX 75251
+1 (972) 661-8881 | breeze-software.com
Practices and Challenges
in Applying Mesoscale
Data to Air Quality
Analyses

2. Practices and Challenges in
Applying Mesoscale Data to Air
Quality Analyses
Weiping Dai, Ph.D., P.E., C.M.
Tao Yang
February 28, 2008
trinityconsultants.com
First US-China Symposium on
Meteorology: Mesoscale Meteorology
and Data Assimilation (2008)

3. Air Quality Modeling
 Evaluate actual or potential impacts on air
quality due to the transport and dispersion
of air pollutants (plumes): Regulatory
Requirements, Engineering Assessments,
Emergency Response

4. Structure of a Dispersion Model
For Each Source
Physical Height
Pollutant Emission Rate
Coordinates
Stack Diameter
Stack Gas Velocity
Stack Gas Temperature
Dimensions Used to
Characterize Building
Wake Effects
Meteorology
Pasquill Stability Class
Wind Direction
Mixing Height
Ambient Temperature
Wind Speed
For Each Receptor
Coordinates
Groundlevel Elevation
Height Above Ground
Simulation of
Atmospheric Physics
Estimate of Air Pollutant
Impacts at Receptors

5. Air Quality Modeling
 Attempt to simulate the response of
plumes to atmospheric conditions
 Wind speed and direction
 Turbulence
 Precipitation
 Temperature
 Stability
 And more …

6. Typical Met Data Requirements
Data Type Parameters
Surface
Meteorological
Data
Wind speed, wind direction,
temperature, cloud cover, ceiling
height, surface pressure, and
relative humidity
Upper Air Data Wind speed, wind direction,
temperature, pressure, and
elevation
Precipitation Data Precipitation rate and
precipitation type code

7. Common Air Quality Models
 Gaussian steady-state models (Near-field)
 U.S. EPA Models – AERMET/AERMOD
 Lagrangian non-steady-state puff models
(Long-range)
 CALMET/CALPUFF
 Eulerian photochemical models
(Regional)
 CMAQ, CAMx
 All these models can utilize mesoscale
meteorological data.

8. AERMET/AERMOD
 AERMET/AERMOD is the new
generation regulatory model in the
U.S.; reflects the state-of-science
theories and practices in planetary
boundary layer.
 It’s also recommended by China SEPA for
Environmental Impact Assessment (EIA).
 Availability of representative met data
is a major issue in AERMOD
application.

9. CALMET/CALPUFF
 CALMET/CALPUFF modeling system
is recommended for long-range
dispersion analyses (50 – 500 km).
 Meteorological data preprocessor
CALMET produces diagnostic data
based on mesoscale data and
observations.
 Prognostic meso data (e.g., MM5)
 Discrete observation data (surface, upper
air, overwater, precipitation)

10. Regional Models – CMAQ/CAMx
 Eulerian photochemical dispersion
models allow for an integrated “one-
atmosphere” assessment with multi-
scales ranging from urban to regional.
 Utilize mesoscale data after certain re-
grid treatment to drive the transport and
dispersion of pollutants.
 Objective analysis or “diagnostic”
approach is highly discouraged.

11. Apply Mesoscale Data in Air
Quality Models
Modeling scale categories:
 In meteorological community:
 Global, synoptic, mesoscale (alpha, beta,
gamma) and turbulence scales
 In air quality modeling community:
 Regional, local, and turbulence scales
(Several kilometers to hundreds of
kilometers)
 Most applications can be covered by
mesoscale data

12. Apply Mesoscale Data in Air
Quality Models
 Mesoscale meteorological models
play an important role in AQMs
 Dynamic (Prognostic) model
(e.g. MM5, WRF)
 Data assimilation model
(e.g. FDDA)
 Diagnostic model
(e.g. CALMET)

13. Apply Mesoscale Data in Air
Quality Models

14.  Diagnostic models:
Pros:
 Easy to use
 No accumulation of errors
Cons:
 Restrained by the observation resolution
 Variable consistency issues
Pros and Cons of Mesoscale Data in
AQM Applications

15.  Dynamic (Prognostic) models:
Pros:
 Finer resolution without high resolution
observations
 Variable consistency
 Sensitive experiments can be carried out
Cons:
 Accumulation of errors
 Level of effort in time/cost
Pros and Cons of Mesoscale Data in
AQM Applications

16.  Data Assimilation Models:
 Four-dimensional data assimilation
(FDDA) utilized the observations to
correct the growth of the errors in
dynamical models.
 FDDA has shown good potential in
improving the performance of the
mesoscale model output data.
Pros and Cons of Mesoscale Data in
AQM Applications

17. Challenges of Applying Mesoscale
Data in AQMs
 Different Perspectives:
For AQM applications (e.g.,
permitting):
 Weak dynamics (light winds, stable
atmosphere, and moderate to shallow
mixing depth)
For pure meteorology (e.g.,
forecasting):
 Severe weather (low pressure systems,
high pressure gradient, and strong
upward motion of the air)

18. Model Compatibility and
Data Availability
 Compatibility to AQMs:
 Data assimilation, model numeric,
physical parameterizations, surface
characteristics, and shallow cloud issues
 Absence of representative and good-
quality meteorological data could be
the weak link in air quality modeling.

19. Conclusions
 Mesoscale data has been widely used
by the Air Quality Modeling community.
 Development of mesoscale models
should consider the special needs and
requirements of AQMs.
 Mesoscale data quality should also be
validated for typical AQM applications.

20. Thanks!
Questions?
Contact Info
Weiping Dai, Principal Consultant
Trinity Consultants
972-661-8100 (phone)
972-385-9203 (fax)
wdai@trinityconsultants.com