Practices and Challenges in Applying Mesoscale Data to Air Quality Analyses
0 likes215 views
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.
Practices and Challenges in Applying Mesoscale Data to Air Quality Analyses
- 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