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Advanced Baseline Imager Cloud-Top Trajectories and Properties of Electrified Snowfall Flash Initiation

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  • 1 aDepartment of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama
  • | 2 bNASA Marshall Space Flight Center, Huntsville, Alabama
  • | 3 cAtmospheric Sciences Program, The University of Georgia, Athens, Georgia
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Abstract

Using gridded and interpolated derived motion winds from the Advanced Baseline Imager (ABI), a Lagrangian cloud-feature tracking technique was developed to create and document trajectories associated with electrified snowfall and changes in cloud characteristics leading up to the initiation of lightning, respectively. This study implemented a thundersnow detection algorithm and defined thundersnow initiation (TSI) as the first group in a flash detected by the Geostationary Lightning Mapper when snow was occurring. A total of 10 ABI channels and four multispectral [e.g., red–green–blue (RGB)] composites were analyzed to investigate characteristics that lead up to TSI for 16  644 thundersnow (TSSN) flashes. From the 10.3-μm channel, TSI trajectories were associated with a median decrease of 12.2 K in brightness temperature (TB) 1 h prior to TSI. Decreases in the reflectance component of the 3.9-μm channel indicated that TSI trajectories were associated with ice crystal collisions and/or particle settling at cloud top. The nighttime microphysics, day cloud phase distinction, differential water vapor, and airmass RGBs were examined to evaluate the microphysical and environmental changes prior to TSI. For daytime TSI trajectories, the predominant colors associated with the day cloud phase distinction RGB transitioned from cyan to yellow/green, physically representing cloud growth and glaciation at cloud top. Gold/orange hues in the differential water vapor RGB indicated that some trajectories were associated with dry upper-level air masses prior to TSI. The analysis of ABI characteristics prior to TSI, and subsequently relating those characteristics to physical processes, inherently increases the fundamental understanding and ability to forecast TSI; thus, providing additional lead time into changes in surface conditions (i.e., snowfall rates).

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sebastian S. Harkema, sebastian.harkema@nasa.gov

Abstract

Using gridded and interpolated derived motion winds from the Advanced Baseline Imager (ABI), a Lagrangian cloud-feature tracking technique was developed to create and document trajectories associated with electrified snowfall and changes in cloud characteristics leading up to the initiation of lightning, respectively. This study implemented a thundersnow detection algorithm and defined thundersnow initiation (TSI) as the first group in a flash detected by the Geostationary Lightning Mapper when snow was occurring. A total of 10 ABI channels and four multispectral [e.g., red–green–blue (RGB)] composites were analyzed to investigate characteristics that lead up to TSI for 16  644 thundersnow (TSSN) flashes. From the 10.3-μm channel, TSI trajectories were associated with a median decrease of 12.2 K in brightness temperature (TB) 1 h prior to TSI. Decreases in the reflectance component of the 3.9-μm channel indicated that TSI trajectories were associated with ice crystal collisions and/or particle settling at cloud top. The nighttime microphysics, day cloud phase distinction, differential water vapor, and airmass RGBs were examined to evaluate the microphysical and environmental changes prior to TSI. For daytime TSI trajectories, the predominant colors associated with the day cloud phase distinction RGB transitioned from cyan to yellow/green, physically representing cloud growth and glaciation at cloud top. Gold/orange hues in the differential water vapor RGB indicated that some trajectories were associated with dry upper-level air masses prior to TSI. The analysis of ABI characteristics prior to TSI, and subsequently relating those characteristics to physical processes, inherently increases the fundamental understanding and ability to forecast TSI; thus, providing additional lead time into changes in surface conditions (i.e., snowfall rates).

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Corresponding author: Sebastian S. Harkema, sebastian.harkema@nasa.gov

Supplementary Materials

    • Supplemental Materials (ZIP 1.51 MB)
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