It is well known that tsunamis can produce gravity waves that propagate up to the ionosphere generating disturbed electron densities in the E and F regions. These ionospheric disturbances can be studied in detail using ionospheric total electron content (TEC) measurements collected by continuously operating ground-based receivers from the Global Navigation Satellite Systems (GNSS). Here, we present results using a new approach, named VARION (Variometric Approach for Real-Time Ionosphere Observation), and estimate slant TEC (sTEC) variations in a real-time scenario. Using the VARION algorithm we compute TEC variations at 56 GPS receivers in Hawaii as induced by the 2012 Haida Gwaii tsunami event. We observe TEC perturbations with amplitudes of up to 0.25 TEC units and traveling ionospheric perturbations (TIDs) moving away from the earthquake epicenter at an approximate speed of 316 m/s. We perform a wavelet analysis to analyze localized variations of power in the TEC time series and we find perturbation periods consistent with a tsunami typical deep ocean period. Finally, we present comparisons with the real-time tsunami MOST (Method of Splitting Tsunami) model produced by the NOAA Center for Tsunami Research and we observe variations in TEC that correlate in time and space with the tsunami waves.

In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 Falcon 9 rocket launch from Vandenberg Air Force Base in California. VARION is a real-time Global Navigation Satellites Systems (GNSS)-based algorithm that can be used to detect various ionospheric disturbances associated with natural hazards, such as tsunamis and earthquakes. A noise reduction algorithm was applied to the VARION-GEO solutions to remove the satellite-dependent noise term. Our analysis showed that the interactions of the exhaust plume with the ionospheric plasma depleted the total electron content (TEC) to a level comparable with nighttime TEC values. During this event, the geometry of the satellite-receiver link is such that GEO satellites measured the depleted plasma hole before any GPS satellites. We estimated that the ionosphere relaxed back to a pre-perturbed state after about 3 h, and the hole propagated with a mean speed of about 600 m/s over a region of 700 km in radius. We conclude that the VARION-GEO approach can provide important ionospheric TEC real-time measurements, which are not affected by the motion of the ionospheric pierce points (IPPs). Furthermore, the VARION-GEO measurements experience a steady noise level throughout the entire observation period, making this technique particularly useful to augment and enhance the capabilities of well-established GNSS-based ionosphere remote sensing techniques and future ionospheric-based early warning systems.

The goal of this article is the illustration of the new functionalities of the VADASE (Variometric Approach for Displacements Analysis Stand-alone Engine) processing approach. VADASE was presented in previous works as an approach able to estimate in real time the velocities and displacements in a global reference frame (ITRF), using high-rate (1 Hz or more) carrier phase observations and broadcast products (orbits, clocks) collected by a stand-alone GNSS receiver, achieving a displacements accuracy within 1–2 cm (usually better) over intervals up to a few minutes. It has been well known since the very first implementation and testing of VADASE that the estimated displacements might be impacted by two different effects: spurious spikes in the velocities due to outliers (consequently, displacements, obtained through velocities integration, are severely corrupted) and trends in the displacements time series, mainly due to broadcast orbit and clock errors. Two strategies are herein introduced, respectively based on Leave-One-Out cross-validation (VADASE-LOO) for a receiver autonomous outlier detection, and on a network augmentation strategy to filter common trends out (A-VADASE); they are combined (first, VADASE-LOO; second, A-VADASE) for a complete solution. Moreover, starting from this VADASE improved solution, an additional strategy is proposed to estimate in real time the overall coseismic displacement occurring at each GNSS receiver. New VADASE advances are successfully applied to the GPS data collected during the recent three strong earthquakes that occurred in Central Italy on 24 August and 26 and 30 October 2016, and the results are herein presented and discussed. The VADASE real-time estimated coseismic displacements are compared to the static ones derived from the daily solutions obtained within the standard post-processing procedure by the Istituto Nazionale di Geofisica e Vulcanologia.

To numerically simulate earthquake‐induced ionospheric disturbances, we extend the Wave Perturbation‐Global Ionosphere‐Thermosphere Model, which was originally developed for tsunami‐ionosphere coupling via gravity waves, to the case of earthquake‐ionosphere coupling via acoustic‐gravity waves. The new Wave Perturbation‐Global Ionosphere‐Thermosphere Model represents epicentral crustal movements by a point source specified with the ground motion data from seismic measurements. The model then solves for the neutral atmospheric perturbations generated by spherical acoustic‐gravity waves and the resulting ionospheric plasma perturbations over the epicentral area. We apply the model to simulate the near‐field ionospheric disturbances during two major earthquake events: the 2011 Tohoku‐Oki, Japan, and the 2015 Illapel, Chile, events. To validate the results, we extract receiver‐to‐satellite total electron content perturbations from the simulations and compare them to the corresponding slant total electron content perturbations from Global Positioning System observations. We find good agreement on magnitudes and arrival times between the simulations and observations.

This study presents a new methodology to automatically classify perturbations in the lower ionosphere using GNSS radio occultation (RO) observations collected using Spire’s constellation of CubeSats. This methodology combines signal processing techniques with semi-supervised machine learning by applying spectral clustering in a metric space of wavelet spectra. A “bottom-up” algorithm was applied to extract E layer information directly from Spire’s high-rate (50 Hz) GNSS-RO profiles by subtracting the effect of the F layers. This processing algorithm has been implemented in our ground segment to operationally produce high rate sTEC profiles with a vertical resolution of better than 100 m. The key idea behind the semi-supervised classification is to produce a database of labeled clusters that can be used to classify new unlabeled data by determining which cluster it belongs to. A dataset of more than 12,000 GNSS-RO profiles collected in 2019 containing sTEC perturbations is used to find the initial clusters. This dataset is used to represent the climatology of ionospheric perturbations, such as MSTIDs and sporadic Es. The wavelet power spectrum (WPS) is computed for these profiles, and a metric space is defined using the Earth mover’s distance (EMD) between the WPS. A self-tuning spectral clustering algorithm is used to cluster the profiles in this metric space. These clusters are used as a reference database of perturbations to classify new sTEC profiles by finding the cluster of the closest profile of the clustered dataset in the EMD metric space. This new methodology is used to construct an automated system to monitor ionospheric perturbations on a global scale.