Detection and geo-location of Sferics onboard Lightning Nano-Satellite (LiNSAT)

This paper presents architecture of a lightning detector onboard Lightning Nano-Satellite (LiNSAT) in low-earth-orbit (LEO) and results of two terrestrial measurement campaigns to geo-locate and discriminate lightning types in the presence of noise sources. The LiNSAT will be launched with a three-satellite constellation to utilize a Time-of-Arrival (TOA) technique. Our main scientific objective is to investigate lightning events by the observation of VHF electro-magnetic signals (Sferics) and to derive the signatures of lightning. One of the important parameters is the lightning ash rate, which can be used as a proxy for locating severe weather activity. Another objective is to discriminate the discharges of lightning events evaluated by the inherent features and to differentiate cloud discharges from ground discharges that can be used to predict convective storm development. We conducted two measurement campaigns; one for artificial lightning produced in high voltage chambers and the second for natural lightning recorded at urban environments. We focused mainly on the received time series including noisy features and narrowband carriers to extract characteristic parameters. One of the major challenges of using a nano-satellite for such a scientific payload is to integrate the lightning experiment antenna, receiver and data acquisition unit into the small nano-satellite structure. The optimization in this mission is to use one of the lightning antennae integrated into the gravity gradient boom (GGB) that increases the sensitivity and directional capability of the satellite toward the nadir direction. The lightning detector onboard has to perform tasks like determination of pulse-width, pulse-count, pulse rise/fall time etc; additionally detection of narrowband carriers and artifacts from the satellite itself (EMC). The model will work in experimental modes for the detection of lightning events by employing preset criteria. After successful detection of lightning t- ansients, the dumped data will be post-processed on the ground to whiten, de-chirp and classify signals in time and frequency domains.