Thunderstorm-Induced Muon Events During the Spring 2020 Convective Season: A Multi-Sensor Analysis Using Satellite, Lightning, Electric Field, and Reanalysis Observations

Thunderstorm-Induced Muon Events (TIMEs) provide a unique probe of the coupling between atmospheric electricity, convective storm dynamics, and high-energy secondary cosmic rays. Although individual TIMEs have been investigated previously, the broader meteorological characteristics of the associated thunderstorms remain poorly understood. In this study, we investigate TIMEs observed during the Spring 2020 convective season over southern India using a multi-sensor framework that combines observations from the GRAPES-3 muon telescope, INSAT-3DR satellite, the Indian Lightning Location Network (ILLN), and ERA5 reanalysis. For each event, key thunderstorm properties, including lightning flash rate, convective cell area, cloud-top brightness temperature, and cloud microphysical parameters, were systematically analyzed.

The analyzed events exhibit substantial variability and can be classified into two distinct groups with contrasting storm characteristics. One group is associated with larger convective cells, enhanced lightning activity, colder cloud tops, and higher ice-water content, indicative of stronger mixed-phase microphysical processes and more intense storm electrification. The second group is characterized by comparatively weaker convection, reduced lightning activity, warmer cloud tops, and less pronounced microphysical signatures. These systematic differences demonstrate that TIMEs occur across a broad spectrum of convective environments while remaining closely linked to thunderstorm intensity and cloud microphysics. By integrating cosmic-ray observations with satellite, lightning, and atmospheric reanalysis datasets, this study provides a comprehensive characterization of the thunderstorms associated with TIMEs, enabling relationships between particle flux variations, storm structure, electrification, and cloud evolution to be identified more effectively than with any individual dataset alone. The results demonstrate the value of multi-sensor observations for advancing our understanding of thunderstorm evolution, atmospheric electrification, and the high-energy processes underlying TIMEs.