Monitoring Space Weather: Using Automated, Accurate Neural Network Based Whistler Segmentation for Whistler Inversion

It is challenging, yet important, to measure the - ever-changing - cold electron density in the plasmasphere. The cold electron density inside and outside of the plasmapause is a key parameter for radiation belt dynamics. One indirect measurement is through finding the velocity dispersion relation exhibited by lightning induced whistlers. The main difficulty of the method comes from low signal-to-noise ratios for most of the ground-based whistler components. To provide accurate electron density and L-shell measurements, whistler components need to be detectable in the noisy background, and their characteristics need to be reliably determined. For this reason precise segmentation is needed on a spectrogram image. Here we present a fully automated way to perform such an image segmentation by leveraging the power of convolutional neural networks, a state-of-the-art method for computer vision tasks. Testing the proposed method against a manually, and semi-manually segmented whistler dataset achieved <10% relative electron density prediction error for 80% of the segmented whistler traces, while for the L-shell, the relative error is <5% for 90% of the cases. By segmenting more than 1 million additional real whistler traces from Rothera station Antarctica, logged over 9 years, seasonal changes in the average electron density were found. The variations match previously published findings, and confirm the capabilities of the image segmentation technique

The source regions of whistlers

Abstract We present a new method for identifying the source regions of lightning‐generated whistlers observed at a fixed location. In addition to the spatial distribution of causative lightning discharges, we calculate the ratio of lightning discharges transmitted into ground detectable whistlers as a function of location. Our method relies on the time of the whistlers and the time and source location of spherics from a global lightning database. We apply this method to whistlers recorded at 15 ground‐based stations in the Automatic Whistler Detector and Analyzer Network operating between 2007 and 2018 and to located lightning strokes from the World Wide Lightning Location Network database. We present the obtained maps of causative lightning and transmission rates. Our results show that the source region of whistlers corresponding to each ground station is around the magnetic conjugate point of the respective station. The size of the source region is typically less than 2,000 km in radius with a small fraction of sources extending to up to 3,500 km. The transmission ratio is maximal at the conjugate point and decreases with increasing distance from it. This conforms to the theory that whistlers detected on the ground propagated in a ducted mode through the plasmasphere, and thus, the lightning strokes of their causative spherics must cluster around the footprint of the ducts in the other hemisphere. Our method applied resolves the whistler excitation region mystery that resulted from correlation‐based analysis methods, concerning the source region of whistlers detected in Dunedin, New Zealand