An Integrated Multi-Sensor Platform for Investigating Thunderstorm Electricity and High-Energy Atmospheric Phenomena

Understanding thunderstorm electricity and its coupling with high-energy atmospheric phenomena, including thunderstorm-induced muon events (TIMEs), requires coordinated observations of electrical, meteorological, and particle environments. We present an integrated multi-sensor atmospheric monitoring platform developed within the GRAPES-3 experiment at Ooty, India, linking directional cosmic-ray muon measurements with electric-field mills, lightning observations (ILLN), satellite products (INSAT-3DR), ERA5 reanalysis, and WRF and CORSIKA simulations within a common event-based framework. 

Relative to previous GRAPES-3 studies, the present platform introduces simultaneous operation of the muon telescope and the 20 Hz (50 ms sampling interval) electric-field mills, real-time integration of ILLN and INSAT-3DR into the event-analysis pipeline, and coupled WRF-CORSIKA simulations to provide independent atmospheric constraints during transient events. Platform performance is demonstrated using the 15 May 2020 thunderstorm event. Although the all-direction muon variation remained below 1σ, a directional decrease of ~0.7% was observed in the northeast direction at approximately 6σ significance. Cross-validation with independent sensors showed a concurrent ~6 kV/m electric-field enhancement, 47 lightning flashes within the telescope's field of view, and INSAT-3DR cloud-top temperatures reaching 190 K, confirming convective overshoot. Synchronization was verified through cross-correlation of 20 Hz electric-field data with 4-minute muon counting bins.

The platform provides a scalable framework for severe-weather characterization and environmental risk assessment, aligning with environmental monitoring technologies. Newly developed NaI(Tl) gamma-ray and rain detectors are undergoing commissioning and are not included here. With >25 years of observations yielding ~1200 thunderstorm-associated events, the dataset supports future automated classification—currently under development.