Air Dispersion Modeling: Meteorological Services

What is Meteorological Data in Air Dispersion Modeling

Meteorological data refers to measurements that represent atmospheric conditions influencing the transport, dispersion, and dilution of emissions at specific downwind receptor locations. These data describe both horizontal and vertical atmospheric behavior and are used as core inputs in regulatory dispersion models.

The two primary types of air dispersion modeling that rely on meteorological data are:

1. 1. Screening Modeling – Applies conservative, worst-case meteorological assumptions to estimate maximum potential impacts and typically used for preliminary project evaluations or streamlined permitting reviews.
   2. Refined Modeling – Incorporates five years of representative, site-appropriate, or one year of site-specific meteorological data to simulate realistic atmospheric conditions

Purpose of Meteorological Data for Air Dispersion Modeling

Meteorological data is essential in air dispersion modeling to simulate how atmospheric conditions disperse pollutants from a source to surrounding areas. It enables

the prediction of ground-level concentrations used to assess regulatory compliance, health impacts, and environmental risks.

Key purposes of using meteorological data in modeling include:

• • Determining Plume Behavior – Evaluates how wind and stability influence the direction, spread, and travel distance of an emission plume.
  • Calculating Dilution Rates – Assesses how quickly pollutants dilute in the atmosphere under varying wind speeds and turbulence conditions.
  • Modeling Vertical Mixing – Surface and upper-air data are used to characterize vertical temperature gradients, boundary layer development, and atmospheric mixing height.
  • Accounting for Weather Impacts – Incorporates precipitation, cloud cover, solar radiation, and other atmospheric factors that influence dispersion and concentration levels.

Key Meteorological Data & Sources

Air dispersion modeling relies on two primary meteorological datasets:

1. 1. Surface Data – Describes conditions at ground level (0 to 10 meters) and typically include:
      1. Wind speed
      2. Wind direction
      3. Temperature & Temperature Difference
      4. Humidity & Dew point
      5. Atmospheric (Barometric) pressure
      6. Cloud cover
      7. Visibility
      8. Precipitation
      9. Radiation
   2. Upper Air Data – Describes vertical atmospheric structure (usually measure via twice-daily radiosonde soundings) and typically include:
      1. Wind & temperature profiles at various elevations
      2. Atmospheric stability classifications
      3. Mixing height data which defines the layer where pollutants are actively mixed by turbulence

Other common sources of meteorological data used for air quality and dispersion modeling include:

• • National Weather Service (NWS) – Airport monitoring stations and METAR reports
  • NOAA Climate Data Online (CDO) – Archives of historical weather observations
  • EPA SCRAM – Specialized meteorological databases for dispersion modeling
  • State Agencies – meteorological datasets preprocessed for dispersion modeling, if available

NWS Meteorological Data Processing

U.S. EPA dispersion models such as AERMOD require processing of surface and upper air data to produce model-ready files. Many states provide AERMOD-ready meteorological data files for dispersion modeling analyses. However, if they are not available in your state, or if they are deemed unrepresentative of your facility, you may need to perform meteorological data processing with nearby NWS data and site-specific parameters, such as surface friction, albedo, and Bowen ratio. The U.S. EPA program AERMET is used to process data to be used in AERMOD. Each state may have specific guidelines for meteorological data processing.

Meteorological Data System Components

A typical site-specific Meteorological Data System follows a structured lifecycle to ensure raw measurements are transformed into actionable insights. The main components are as follows:

1. 1. Site Selection – Determination of the U.S. EPA approved meteorological sensor tower location(s) for representative site-specific data collection. A facility may desire site-specific data collection if the surrounding terrain causes unique meteorological conditions that are not present in nearby NWS surface stations (e.g., a deep valley that channels wind flow consistently in a direction that contradicts dominant wind directions in the area).
   2. Data Acquisition – Selection of appropriate surface and upper air monitoring stations representative of site-specific conditions.
      1. Signal Conditioning – Amplifying, buffering, and converting sensor outputs into usable analog or digital signals
      2. Recording Systems – Use of microprocessor-based digital data loggers with analog strip-chart backup (if applicable)
      3. Analog-to-Digital Conversion – Converting analog sensor signals into binary format for digital processing
      4. Data Communication – Transmission of sensor data via hardwire, telemetry, or modem systems
      5. Sampling Rates – Establishing appropriate sampling frequencies (e.g. ≥60 samples for means, ≥360 for variance per averaging period) to ensure statistical accuracy
   3. Data Processing – Application of approved averaging methods, scalar/vector wind computations, stability determinations, treatment of calms, and handling of missing data consistent with modeling guidance for use in U.S. EPA dispersion modeling analyses for compliance with National Ambient Air Quality Standards (NAAQS) and Prevention of Significant Deterioration (PSD) increment standards.
   4. Quality Assurance/Quality Control (QAQC) Procedures – Ensure that data collected meet standards of reliability and accuracy. Quality Assurance Project Plan (QAPP) should include the following:
      1. Project description – How meteorology is to be used
      2. Project organization – How data validity is supported
      3. QA objective – How QA will document validity claims
      4. Calibration method and frequency (for meteorology)
      5. Data flow – From samples to archived valid values
      6. Validation and reporting methods (for meteorology)
      7. Audits – Performance and system
      8. Preventive maintenance
      9. Procedures to implement QA objectives
      10. Management support – Corrective action and report
   5. Data Management – Secure storage and organization of multi-year datasets to ensure traceability and reproducibility during agency review
   6. Data Interpretation & Reporting – Transforming processed data into formats like METAR reports and ensuring that technical documentation include:
      1. Wind roses
      2. Stability class summaries
      3. Climate characterizations
      4. Worst-case condition analyses