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FrontFinder AI: Efficient Identification of Frontal Boundaries over the Continental United States and NOAA’s Unified Surface Analysis Domain Using the UNET3+ Model Architecture



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  • Journal Title:
    Artificial Intelligence for the Earth Systems
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    FrontFinder artificial intelligence (AI) is a novel machine learning algorithm trained to detect cold, warm, stationary, and occluded fronts and drylines. Fronts are associated with many high-impact weather events around the globe. Frontal analysis is still primarily done by human forecasters, often implementing their own rules and criteria for determining front positions. Such techniques result in multiple solutions by different forecasters when given identical sets of data. Numerous studies have attempted to automate frontal analysis through numerical frontal analysis. In recent years, machine learning algorithms have gained more popularity in meteorology due to their ability to learn complex relationships. Our algorithm was able to reproduce three-quarters of forecaster-drawn fronts over CONUS and NOAA’s unified surface analysis domain on independent testing datasets. We applied permutation studies, an explainable artificial intelligence method, to identify the importance of each variable for each front type. The permutation studies showed that the most “important” variables for detecting fronts are consistent with observed processes in the evolution of frontal boundaries. We applied the model to an extratropical cyclone over the central United States to see how the model handles the occlusion process, with results showing that the model can resolve the early stages of occluded fronts wrapping around cyclone centers. While our algorithm is not intended to replace human forecasters, the model can streamline operational workflows by providing efficient frontal boundary identification guidance. FrontFinder has been deployed operationally at NOAA’s Weather Prediction Center.
  • Source:
    Artificial Intelligence for the Earth Systems, 4(1)
  • DOI:
  • ISSN:
    2769-7525
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA20OAR4590347
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:979dc5e031cb618d95f1eb8889a237548f7e2dca1bf7a13fcfd6013ddf2637374792363b0f6c77d922ef70abe63867cc3cdbd15a731b7565a7ea8134e3ffd8e0
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  • File Type:
    Filetype[PDF - 37.90 MB ]
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