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Directional biases in back trajectories caused by model and input data.

Kristi A Gebhart1, Bret A Schichtel, Michael G Barna

  • 1National Park Service, Air Resources Division, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins 80523, USA. gebhart@cira.colostate.edu

Journal of the Air & Waste Management Association (1995)
|December 15, 2005
PubMed
Summary

Back trajectory analyses for air quality studies showed systematic biases due to meteorological data and model choices. No single method proved superior for source attribution, highlighting the need for careful data selection.

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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Air Quality Research

Background:

  • Back trajectory analyses are crucial for source attribution in visibility and air quality studies.
  • Numerous models and meteorological datasets exist for trajectory generation.

Purpose of the Study:

  • To evaluate different back trajectory models and meteorological datasets using data from the Big Bend Regional Aerosol and Visibility Observational (BRAVO) tracer study.
  • To identify systematic biases and assess the performance of various trajectory methods for source attribution.

Main Methods:

  • Utilized tracer concentrations, particulate data, and other source attribution techniques from the BRAVO study (July-October 1999).
  • Compared results from multiple back trajectory models with different gridded meteorological datasets.

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  • Assessed the impact of meteorological data input and trajectory model mechanisms (especially vertical placement) on results.

    • Main Results:

    • Identified systematic biases between different back trajectory model and meteorological input data combinations at Big Bend National Park.
    • The choice of meteorological data significantly influenced trajectory results.
    • Different trajectory models yielded varied results primarily due to distinct vertical placement mechanisms.
    • Rawinsonde data alone were insufficient for this region.
    • Some model-data combinations failed to reproduce known tracer attributions and simulated sulfate.

    Conclusions:

    • No single back trajectory model or meteorological dataset was consistently superior for source attribution.
    • The selection of meteorological data and trajectory model significantly impacts air quality source attribution estimates.
    • Careful evaluation and selection of trajectory methods and input data are essential for accurate source attribution in air quality studies.