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We demonstrate a methodology for utilizing measurements from very low frequency (VLF, 3−30 kHz) transmitters and lightning emissions to produce 3D lower electron density maps, and apply it to multiple geophysical disturbances. The D‐region lower ionosphere (60−90 km) forms the upper boundary of the Earth‐ionosphere waveguide which allows VLF radio waves to propagate to global distances. Measurements of these signals have, in many prior studies, been used to infer path‐average electron density profiles within the D region. Historically, researchers have focused on either measurements of VLF transmitters or radio atmospherics (sferics) from lightning. In this work, we build on recently published methods for each and present a method to unify the two approaches via tomography. The output of the tomographic inversion produces maps of electron density over a large portion of the United States and Gulf of Mexico. To illustrate the benefits of this unified approach, daytime and nighttime maps are compared between a sferic‐only model and the new approach suggested here. We apply the model to characterize two geophysical disturbances: solar flares and lower ionospheric changes associated with thunderstorms.

We present a tomographic imaging technique for the D‐region electron density using a set of spatially distributed very low frequency (VLF) remote sensing measurements. The D‐region ionosphere plays a critical role in many long‐range and over‐the‐horizon communication systems; however, it is unreachable by most direct measurement techniques such as balloons and satellites. Fortunately, the D region, combined with Earth's surface, forms what is known as the Earth‐Ionosphere waveguide allowing VLF and low frequency (LF) radio waves to propagate to global distances. By measuring these signals, we can estimate a path measurement of the electron density, which we assume to be a path‐averaged electron density profile of the D region. In this work, we use path‐averaged inferences from lightning‐generated radio atmospherics (sferics) with a tomographic inversion to produce 3D models of electron density over the Southeastern United States and the Gulf of Mexico. The model begins with two‐dimensional great circle path observations, each of which is parameterized so it includes vertical profile information. The tomography is then solved in two dimensions (latitude and longitude) at arbitrarily many altitude slices to construct the 3D electron density. We examine the model's performance in the synthetic case and determine that we have an expected percent error better than 10% within our area of interest. We apply our model to the 2017 “Great American Solar Eclipse” and find a clear relationship between sunlight percentage and electron density at different altitudes.

The D‐region ionosphere (6090 km) plays an important role in long‐range communication and response to solar and space weather; however, it is difficult to directly measure with currently available technology. Very low frequency (VLF) radio remote sensing is one of the more promising approaches, using the efficient reflection of VLF waves from the D‐region. A number of VLF beacons can therefore be turned into diagnostic tools. VLF remote sensing techniques are useful and can provide global coverage, but in practice have been applied to a limited area and often on only a small number of days. In this work, we expand the use of a recently introduced machine learning based approach (Gross & Cohen, 2020,https://doi.org/10.1029/2019JA027135) to observe and model the D‐region electron density using VLF transmitting beacons and receivers. We have extended the model to cover nighttime in addition to daytime, and have applied it to track D‐region waveguide parameters, h’ and, over 400 daytimes and 150 nighttimes on up to 21 transmitter‐receiver paths across the continental US. Using an exponential fit, h’ represents the height of the ionosphere andrepresents the slope of the electron density. Using this data set, we quantify diurnal, daily and seasonal variations of the D‐region ionosphere for both daytime and nighttime D‐region ionosphere. We show that our model identifies expected variations, as well as producing results in line with other previous studies. Additionally, we show that our daytime predictions exhibit a larger autocorrelation at higher time lags than our nighttime predictions, indicating a model with persistence may perform better.