Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

On the Cloud to Ground Lightning Flash Current Estimates of the Lightning Detection Sensor Network (LDSN)

Version 1 : Received: 8 April 2024 / Approved: 9 April 2024 / Online: 9 April 2024 (08:04:33 CEST)

How to cite: Degala, V.; Arun, S.; Taori, A.; Rao, G.S.; Pawar, S.; Mallikarjun, M. On the Cloud to Ground Lightning Flash Current Estimates of the Lightning Detection Sensor Network (LDSN). Preprints 2024, 2024040613. https://doi.org/10.20944/preprints202404.0613.v1 Degala, V.; Arun, S.; Taori, A.; Rao, G.S.; Pawar, S.; Mallikarjun, M. On the Cloud to Ground Lightning Flash Current Estimates of the Lightning Detection Sensor Network (LDSN). Preprints 2024, 2024040613. https://doi.org/10.20944/preprints202404.0613.v1

Abstract

The atmospheric lightning is one of the natural disasters which need to be considered in the sustainable development. The National Remote Sensing Centre (NRSC) has installed 46 lightning detection sensors (LDS) across the country. These sensors work in radio frequencies having their detection range up to 1000 km with more than 98% detection efficiency within 300 km range. The network is put in a way that sensors have about 50% sensor-to-sensor overlap in their detection ranges to enable high geolocation accuracy and maintain redundancy. The NRSC-LDS network (LDSN) estimates the current associated with each cloud to ground (CG) lightning flash which can be utilized for the risk assessment. The current estimates from one network to the other need to be inter-calibrated and harmonized. Present study compares the LDSN current estimates with Lightning Location Network (LLN) estimates and results are discussed with their impact on the estimation of vulnerable zones. A case study of lightning-induced fire event is presented to showcase the importance of the currents associated with lightning for future planning.

Keywords

climate; lightning; society; environment

Subject

Environmental and Earth Sciences, Sustainable Science and Technology

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