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NOx emissions produced by lightning strikes (LNOx) play an increasingly important role in atmospheric chemistry due to their abundance and prevalence in the mid-to-upper troposphere, especially in regions with decreasing trends in anthropogenic NOx emissions. Accurately quantifying LNOx emissions in time and space in chemistry transport models is challenging due to various uncertainties associated with lightning data and LNOx production rates. Currently, LNOx yield is mainly based on lightning flashes either detected by lightning detection networks or estimated by lightning parameterization schemes. However, the definition of a lightning flash varies from one network to another, confounding the use of datasets from different networks. In addition, it has long been recognized that LNOx yield is related to lightning energy levels, which varies across lightning types (e.g., cloud-to-ground, cloud-to-cloud) and exhibits geographical variations. Along with flash and stroke counts, most networks also report the associated energy aspects. Even though the registered energy values are only a tiny fraction of the sampled lightning strikes (either electromagnetic or optical) by different detection techniques, they represent the same lightning activities (if the networks are similarly purposed), thus some relationship should exist among them. Exploratory analysis of the lightning datasets from the World Wide Lightning Location Network (WWLLN) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellites have indicated that even though both networks detect total lightning, the WWLLN network is better at detecting cloud-to-ground lightning strikes, while the GLM technique is better at detecting total lightning. The energy aspects detected by the two networks display strong relationship when these energy values are aggregated over time and space, suggesting that synergies between the two datasets could be exploited to produce more realistic LNOx emissions estimate across temporal and spatial scales broader than those covered individually.

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Development and Assessment of an Energy-Based Lightning NOx Model by Synergizing Satellite and Ground-Based Lightning Datasets for Air Quality Modeling

  • 会议论文

  • 发表日期：

    2026年

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  • Daiwen Kang,
  • Yuling Wu,
  • Arastoo Pour-Biazar,
  • Rohit Mathur,
  • David Wong,
  • Wyat Appel

摘要

NOx emissions produced by lightning strikes (LNOx) play an increasingly important role in atmospheric chemistry due to their abundance and prevalence in the mid-to-upper troposphere, especially in regions with decreasing trends in anthropogenic NOx emissions. Accurately quantifying LNOx emissions in time and space in chemistry transport models is challenging due to various uncertainties associated with lightning data and LNOx production rates. Currently, LNOx yield is mainly based on lightning flashes either detected by lightning detection networks or estimated by lightning parameterization schemes. However, the definition of a lightning flash varies from one network to another, confounding the use of datasets from different networks. In addition, it has long been recognized that LNOx yield is related to lightning energy levels, which varies across lightning types (e.g., cloud-to-ground, cloud-to-cloud) and exhibits geographical variations. Along with flash and stroke counts, most networks also report the associated energy aspects. Even though the registered energy values are only a tiny fraction of the sampled lightning strikes (either electromagnetic or optical) by different detection techniques, they represent the same lightning activities (if the networks are similarly purposed), thus some relationship should exist among them. Exploratory analysis of the lightning datasets from the World Wide Lightning Location Network (WWLLN) and the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellites have indicated that even though both networks detect total lightning, the WWLLN network is better at detecting cloud-to-ground lightning strikes, while the GLM technique is better at detecting total lightning. The energy aspects detected by the two networks display strong relationship when these energy values are aggregated over time and space, suggesting that synergies between the two datasets could be exploited to produce more realistic LNOx emissions estimate across temporal and spatial scales broader than those covered individually.