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  7. Environmental Engineering
  9. Air Pollution Modelling And Control
  11. The Impact Of Altering Emission Data Precision On Compression Efficiency And Accuracy Of Simulations Of The Community Multiscale Air Quality Model

The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model

Michael S Walters1,2, David C Wong1

  • 1Atmospheric and Environmental Systems Modeling Division, Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA.

Geoscientific Model Development
|February 27, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Reducing the precision of emission data for air quality modeling significantly cuts disk space and slightly speeds up simulations. This method, tested with the Community Multiscale Air Quality (CMAQ) model, maintains model accuracy while optimizing resource use.

Area of Science:

  • Environmental science and modeling
  • Atmospheric chemistry and physics
  • Computational science and data management

Background:

  • The Community Multiscale Air Quality (CMAQ) model is crucial for air quality research and management globally.
  • CMAQ simulations generate massive datasets, requiring substantial disk space for input and output files, posing storage and management challenges.
  • Efficient disk space management, including data compression, is essential for the continuity of large-scale air quality modeling research.

Purpose of the Study:

  • To investigate a novel approach for reducing disk space requirements and accelerating runtime in CMAQ simulations.
  • To evaluate the impact of reducing emission input data precision on CMAQ model performance, including disk-space efficiency, runtime, and predictive accuracy.

Main Methods:

  • Conducted four CMAQ simulations using emission input files with reduced precision (five, four, or three significant digits) and one with full precision.

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  • Post-processed simulation output to analyze disk-space and runtime efficiencies, and model accuracy across different precision levels.
  • Compared the effectiveness of GNU Gzip (gzip) and Basic Leucine Zipper Domain (bzip2) compression utilities on altered precision files.

    • Main Results:

    • Reducing emission precision to five, four, or three significant digits, with bzip2 compression, decreased disk footprint by 6%, 25%, and 48% respectively.
    • Altered output files with bzip2 compression showed disk space reductions of 19%, 47%, and 69% for five, four, and three significant digits.
    • CMAQ runtime decreased by 2%-7% with reduced precision emission files, and model performance statistics remained negligibly impacted compared to observed data.

    Conclusions:

    • Reducing the precision of CMAQ emission data offers substantial disk space savings and slight runtime improvements.
    • This precision reduction strategy effectively optimizes computing resources with minimal impact on the model's predictive accuracy.
    • The findings suggest that precision reduction is a viable method for more efficient large-scale air quality modeling.