Automated detection of weather fronts using a deep learning neural network

Details

• Journal Title:
  Advances in Statistical Climatology, Meteorology and Oceanography
• Personal Author:
  Biard, James C. ; Kunkel, Kenneth E.
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; CICSP (Cooperative Institute for Climate Science)
• Description:
  Deep learning (DL) methods were used to develop an algorithm to automatically detect weather fronts in fields of atmospheric surface variables. An algorithm (DL-FRONT) for the automatic detection of fronts was developed by training a two-dimensional convolutional neural network (2-D CNN) with 5 years (2003–2007) of manually analyzed fronts and surface fields of five atmospheric variables: temperature, specific humidity, mean sea level pressure, and the two components of the wind vector. An analysis of the period 2008–2015 indicates that DL-FRONT detects nearly 90 % of the manually analyzed fronts over North America and adjacent coastal ocean areas. An analysis of fronts associated with extreme precipitation events shows that the detection rate may be substantially higher for important weather-producing fronts. Since DL-FRONT was trained on a North American dataset, its extensibility to other parts of the globe has not been tested, but the basic frontal structure of extratropical cyclones has been applied to global daily weather maps for decades. On that basis, we expect that DL-FRONT will detect most fronts, and certainly most fronts with significant weather. However, where complex terrain plays a role in frontal orientation or other characteristics, it might be less successful.
• Keywords:
  Fronts (Meteorology) Machine Learning Modelling Machine Learning
• Source:
  Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160
• DOI:
  https://doi.org/10.5194/ascmo-5-147-2019
• Document Type:
  Journal Article
• Funding:
  Grant no. NA14NES4320003
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:1af78df0cb018111f079661ec7b0577743e028cbeedea90d6d5a06761c22f947
• Download URL:
  https://repository.library.noaa.gov/view/noaa/45183/noaa_45183_DS1.pdf
• File Type:
  [PDF - 8.76 MB ]