Deep convective clouds and lightning activity during thunderstorms imprint Infrasonic (>3 mili Hertz) oscillations in the ionospheric density or Traveling Ionospheric Disturbances (TIDs). The wave characteristics of these oscillations and the coupling mechanisms remain a subject of investigation, noting that the coupling energetics may alter the spectral and propagation characteristics. Moreover, the availability of numerous convective dynamics time scales makes the oscillation detection time uncertain. To study these aspects, the present work examines the spatial-temporal lightning flash rate during a severe thunderstorm (cloud top temperature < -80 ºC) from the GOES16 infrared channel and the total electron content of the ionosphere from the GNSS network over the tropical Southern hemisphere. The study finds TIDs amplification above the deep convective clouds. The strongest amplification occurs at the earliest, at 9 minutes, from the most intense lightning flash rate and propagates at the most probable speed of 1000 m/s. In contrast to the spectral peak of the active storm, which is 1.2 mHz, the spectral peak of TIDs is 4.8 mHz. The results highlight the magnitude of coupling energetics to determine the wave propagation characteristics of infrasonic TIDs.

The ionosphere is characterised by a large number of disturbances generated in response to a wide range of phenomena, including natural hazards, space weather and man-made events. Such disturbances are known as travelling ionospheric disturbances (TID). Identification of the origin of TID, especially in real or near-real-time (NRT), is an extremely difficult task, and it is one of the most interesting scientific questions. In this paper we present, for the first time, an approach for an automatic and NRT-compatible detection and recognition of the source of ionospheric disturbances in time series of total electron content (TEC) measured by the Global Navigation Satellite Systems (GNSS) method. The main idea is 1) to analyse main characteristics (such as spatio-temporal features and frequency content) of TID generated by known sources, and 2) in NRT, to rapidly examine TID’s, and, based on this information, recognize their source. Currently, our database contains TEC data series with response to earthquakes, volcanic eruptions, tornadoes, explosions, rocket launches and equatorial plasma bubbles. Our developments are important for the future assessment of natural hazards from the ionosphere, and also for NRT Space Weather nowcast and applications. Also, our work presents important information about the physical properties of TID of different origins.

We present a near-real-time (NRT) scenario of analysis of ionospheric response to the 15 January 2022 Hunga Tonga-Hunga Ha’apai eruption by using GNSS data. We introduce a new method to determine instantaneous velocities using an interferometric approach and using the time derivative of the total electron content (TEC). Moreover, for the first time, we propose a novel method that automatically estimates the propagation velocity of disturbances from near-real-time travel-time diagrams. By using our new methods, we analyzed the dynamics of co-volcanic ionospheric disturbances generated by the Hunga-Tonga eruption, and we estimated the first propagation velocity to be ~800-950 m/s, which subsequently decreased to ~600 m/s. We demonstrate that our approach can be used to detect, analyze and identify the complexity of a natural hazard event. Also, it is important to note that our new methods can perform at a low spatial resolution and 30-sec cadence data.

Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times, and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform dataset show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.

On 15 January 2022, the Hunga Tonga-Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground-based receivers of Global Navigation Satellite System (GNSS) to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long-lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least 5 explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in TNT equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.

Earthquakes are known to generate ionospheric disturbances that are commonly referred to as co-seismic travelling ionospheric disturbances (CTID). In this work, for the first time, we present a novel method that enables to automatically detect CTID in ionospheric GNSS-data, and to determine their spatio-temporal characteristics (velocity and azimuth of propagation) in near-real time (NRT), i.e., less than 15 minutes after an earthquake. The obtained instantaneous velocities allow us to understand the evolution of CTID and to estimate the location of the CTID source in NRT. Furthermore, also for the first time, we developed a concept of real-time travel-time diagrams that aid to verify the correlation with the source and to estimate additionally the propagation speed of the observed CTID. We apply our methods to the Mw7.4 Sanriku earthquake of 09/03/2011 and the Mw9.0 Tohoku earthquake of 11/03/2011, and we make a NRT analysis of the dynamics of CTID driven by these seismic events. We show that the best results are achieved with high-rate 1Hz data. While the first tests are made on CTID, our method is also applicable for detection and determining of spatio-temporal characteristics of other travelling ionospheric disturbances that often occur in the ionosphere driven by many geophysical phenomena.