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Quality Control of Geostationary Lightning Mapper Observations for Tropical Cyclone Applications



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  • Journal Title:
    Journal of Atmospheric and Oceanic Technology
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, “bar” effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC was not applied beforehand. Significance Statement The Geostationary Lightning Mapper (GLM) provides total lightning observations in tropical cyclones that can benefit forecasts of intensity change. However, nonlightning artifacts in GLM observations make interpreting lightning observations challenging for automated techniques to predict intensity change. Quality control procedures have been developed to aid the TC community in using GLM observations for statistical and pattern-matching techniques.
  • Source:
    Journal of Atmospheric and Oceanic Technology, 41(9), 889-901
  • DOI:
  • ISSN:
    0739-0572 ; 1520-0426
  • Format:
    pdf
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA19OAR4320073
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:cf27d73b71fff18727e14edc18d92a96fdb1dbeba56f6d1c54c9022d675f5988d1881c7d9f38d95b96fb36fbc725bd813e2364b5ceb4f0b56373cab9194ffed6
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  • File Type:
    Filetype[PDF - 36.25 MB ]
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