An Object-Based Analysis of Lightning Characteristics in Pretropical Cyclogenesis Environments

The Geostationary Lightning Mapper (GLM) provides continuous, high-resolution lightning observations that enable a novel investigation of lightning attributes in pretropical cyclogenesis environments. Compositing and tracking methods from the Tracking and Object-Based Analysis of Clouds (tobac) Python package enable the consideration of lightning lifetime alongside GLM-measured area and optical energy. We first apply the object-based framework to lightning observed prior to the formation of Tropical Storm Claudette (2021), a genesis case within range of the Next Generation Weather Radar (NEXRAD) network. Collocated ground-based, dual-polarization radar observations suggest that small-area, low-energy lightning is indicative of stronger convection and updrafts, based on composite vertical radar profiles of tobac lightning features. We then apply the physical interpretations of these lightning attributes to lightning 72 h prior to genesis and within 200 km of the best track invest center for five North Atlantic disturbances. Developing disturbances include Claudette (2021), Ida (2021), Earl (2022), and Beryl (2024), which we compare against nondeveloping AL96 (2024). Small-area, low-energy lightning, previously associated with stronger updrafts and convection, is the dominant lightning mode for the four developing cases with large-area, high-energy lightning also identified at various times. Lightning activity appears to be modulated by deep-layer vertical wind shear, and electrified convection coincides with improvements in convective organization in multiple instances. While more work is needed to evaluate lightning attributes across a larger composite of disturbances, this study offers a novel characterization of lightning in pretropical cyclogenesis environments for the North Atlantic to aid in the understanding and forecasting of tropical cyclone genesis. SIGNIFICANCE STATEMENT: This study investigates lightning characteristics in five North Atlantic tropical disturbances, four of which became tropical storms, to better understand and forecast tropical storm formation. We apply a novel approach that uses advanced observations from the Geostationary Lightning Mapper and tracking abilities from the Tracking and Object-Based Analysis of Clouds Python package. This framework enables a detailed characterization of lightning that considers lightning area, optical energy, and lifetime, providing more information than previously used flash counts and rates. These expanded lightning attributes may be relevant for understanding and forecasting tropical storm formation. Future work will use this framework to assess lightning across a larger sample of tropical disturbances to evaluate lightning trends across a wider range of cases.