U.S. flag An official website of the United States government.
Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

i

Remote Measurement of Active Whitecaps Using Deep Learning



Select the Download button to view the document
Please click the download button to view the document.

Details

  • Journal Title:
    Journal of Atmospheric and Oceanic Technology
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Whitecaps generated by wave breaking and air entrainment can be classified as active (stage A) or residual (stage B). Measurement of each stage individually is essential for accurate parameterization of air–sea interaction processes, but conventional methods used for separation in visible images are subjective. In this study, this problem is solved using a pipeline for active whitecap fraction measurement. In this pipeline, a new horizon detection method is developed to stabilize and rectify images, and a deep learning model based on U-Net is trained and validated to identify and extract active whitecaps. The model demonstrates robust prediction accuracy even when images are contaminated by sun glint. The model is applied to 48 h of video footage collected during a cruise in Gulf of Mexico. It is determined that, as a function of wind speed, the active whitecap fraction has significant variability and disparity compared to previous research. This finding indicates that secondary factors should be considered for accurate whitecap parameterization. This is explored using a random forest, which indicates that sea surface temperature, swell, and wave age are important to the active whitecap fraction. The precise impact of sea surface temperature is further explored using analyses of variance (ANOVA), which suggest it has a positive correlation with the active whitecap fraction.
  • Source:
    Journal of Atmospheric and Oceanic Technology, 42(7), 827-842
  • DOI:
  • ISSN:
    0739-0572 ; 1520-0426
  • Format:
    pdf
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:593358fdb38f5b9bdec7114906689e5428c465c7b669a6904fc787744cd92dba88532473eeb822f75873ad69e557c63ec9eb6dfc49e5ae45d7bbbd1a9930552f
  • Download URL:
  • File Type:
    Filetype[PDF - 27.91 MB ]
PREVIOUS
ON THIS PAGE
Details
The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
BACK TO TOP