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The Definition of GOES Infrared Lightning Initiation Interest Fields

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  • 1 Department of Meteorology and Space Systems Academic Group, Graduate School of Engineering and Applied Sciences, Naval Postgraduate School, Monterey, California
  • | 2 Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama
  • | 3 Earth Systems Science Center, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama
  • | 4 Department of Meteorology and Space Systems Academic Group, Graduate School of Engineering and Applied Sciences, Naval Postgraduate School, Monterey, California
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Abstract

Within cumulus cloud fields that develop in conditionally unstable air masses, only a fraction of the cumuli may eventually develop into deep convection. Identifying which of these convective clouds is most likely to generate lightning often starts with little more than a qualitative visual satellite analysis. The goal of this study is to identify the observed satellite infrared (IR) signatures associated with growing cumulus clouds prior to the first lightning strike, or lightning initiation (LI). This study quantifies the behavior of 10 Geostationary Operational Environmental Satellite-12 (GOES-12) IR fields of interest in the 1 h in advance of LI. A total of 172 lightning-producing storms, which occurred during the 2009 convective season, are manually tracked and studied over four regions: northern Alabama, central Oklahoma, the Kennedy Space Center, and Washington, D.C. Four-dimensional and cloud-to-ground lightning array data provide a total cloud lightning picture (in-cloud, cloud-to-cloud, cloud-to-air, and cloud-to-ground) and thus precise LI points for each storm in both time and space. Statistical significance tests are conducted on observed trends for each of the 10 LI fields to determine the unique information each field provides in terms of behavior prior to LI. Eight out of 10 LI fields exhibited useful information at least 15 min in advance of LI, with 35 min being the average. Statistical tests on these eight fields are compared for separate large geographical areas. Median IR temperatures and 3.9-μm reflectance values are then determined for all 172 events as an outcome, which may be valuable when implementing a LI prediction algorithm into real-time satellite-based systems.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. Email: johnm@nsstc.uah.edu

Abstract

Within cumulus cloud fields that develop in conditionally unstable air masses, only a fraction of the cumuli may eventually develop into deep convection. Identifying which of these convective clouds is most likely to generate lightning often starts with little more than a qualitative visual satellite analysis. The goal of this study is to identify the observed satellite infrared (IR) signatures associated with growing cumulus clouds prior to the first lightning strike, or lightning initiation (LI). This study quantifies the behavior of 10 Geostationary Operational Environmental Satellite-12 (GOES-12) IR fields of interest in the 1 h in advance of LI. A total of 172 lightning-producing storms, which occurred during the 2009 convective season, are manually tracked and studied over four regions: northern Alabama, central Oklahoma, the Kennedy Space Center, and Washington, D.C. Four-dimensional and cloud-to-ground lightning array data provide a total cloud lightning picture (in-cloud, cloud-to-cloud, cloud-to-air, and cloud-to-ground) and thus precise LI points for each storm in both time and space. Statistical significance tests are conducted on observed trends for each of the 10 LI fields to determine the unique information each field provides in terms of behavior prior to LI. Eight out of 10 LI fields exhibited useful information at least 15 min in advance of LI, with 35 min being the average. Statistical tests on these eight fields are compared for separate large geographical areas. Median IR temperatures and 3.9-μm reflectance values are then determined for all 172 events as an outcome, which may be valuable when implementing a LI prediction algorithm into real-time satellite-based systems.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. Email: johnm@nsstc.uah.edu

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