Spatial and Temporal Ionospheric Monitoring Using Broadband Sferic Measurements

The D region of the ionosphere (60–90 km altitude) is highly variable on timescales from fractions of a second to many hours, and on spatial scales up to many hundreds of kilometers. Very low frequency (VLF) and low‐frequency (LF) (3–30 kHz and 30–300 kHz) radio waves are guided to global distances by reflections from the ground and the D region. Therefore, information about its current state is encoded in received VLF/LF signals. VLF transmitters have been used in the past for D region studies, with ionospheric disturbances manifesting as perturbations in amplitude and/or phase. The return stroke of lightning is an impulsive VLF radiator, but unlike VLF transmitters, lightning events are distributed broadly in space allowing for much greater spatial coverage of the D region compared to VLF transmitter‐based remote sensing in addition to the broadband spectral advantage over the narrowband transmitters. The challenge is that individual lightning‐generated waveforms, or “sferics,” vary due to the lightning current parameters and uncertainty in the time/location information, in addition to D region ionospheric variability. These factors make it difficult to utilize the VLF/LF emissions from lightning in a straightforward manner. We describe a technique to recover the time domain and amplitude/phase spectra for both Bϕ and Br with high fidelity and consider the utility of our technique with ambient and varied ionospheric conditions. We demonstrate a technique to simulate sferics and infer a parameterized ionosphere with the Wait and Spies parameters (h ′ and β) offering all of the tools needed for a global measurement. Key Points With the described algorithm, we can adjust for the natural variability of lightning sources and for errors in geolocation By comparison to a propagation model, we can produce globally applicable ionospheric inferences with no feature constraints We describe a new approach for removal of narrowband navy transmitters from the data record which outperforms notch filtering