Jihye Park

Ionospheric disturbances caused by acoustic waves (AW) are observed by Global Navigation Satellite Systems (GNSS) measures of total electron content (TEC). AWs that propagate to the ionosphere can be caused by ground‐based explosions, including seismic events. These events often occur in remote regions like the open ocean. Identifying an AW and locating the source of the AW from GNSS TEC measurements is challenging due to several variables between events such as propagation metrics, line of sight geometry, and ionization levels. This becomes especially difficult with a sparse network and limited signal coverage, typical of remote regions. We propose here the use of a deep learning, ionospheric anomaly detection algorithm and a geolocation algorithm capable of identifying atypical ionospheric behavior after a seismic event and estimating the source location. We outline the development of this algorithm and test results over the months of May and June 2023 for five GNSS receivers in the South Pacific. Results show the successful detection of two open ocean M7.7 and M7.1 earthquakes. Three TEC measurements detected as anomalous from the M7.7 earthquake event were used to estimate the epicenter of the earthquake, resulting in a source geolocation of S, E, a discrepancy of in latitude and in longitude, . This research demonstrates the feasibility of AW detection and source geolocation from a sparse GNSS network over open ocean.

As the peak of Solar Cycle 25 approaches, space weather events such as Equatorial Plasma Bubble (EPBs) and geomagnetic storms are expected to become more frequent. While EPBs are a primary source of scintillation, geomagnetic storms can either enhance or suppress this activity depending on storm timing, intensity, and induced electric field effects, thereby causing significant ionospheric disturbances that degrade Global Navigation Satellite System (GNSS) signal reception performance. This study presents a novel, systematic evaluation of GPS + Galileo Precise Point Positioning (PPP) performance under intense ionospheric scintillation during the rising phase of Solar Cycle 25 using datasets from globally distributed stations. More than twenty months of data have been systematically analysed, with a focus on stations located in equatorial regions, which are the most affected by strong scintillation. PPP processing was performed using final products from the European Space Agency (ESA) with Multi-GNSS Experiment (MGEX) products employed as backups when ESA data were unavailable. It is shown that under severe scintillation the accuracy of the final PPP solution is severely reduced, with errors more than doubled with respect to calm days. In this respect, frequent cycle slips and anomalies in the input observations are detected. A comparative analysis of GPS-only and GPS + Galileo PPP solutions confirms that integrating Galileo not only mitigates the impact of scintillation but also improves the reliability and accuracy of positioning in challenging space weather conditions.

The Galileo High-Accuracy Service (HAS) was declared operational (initial service) in January 2023 after an intense testing phase aimed at assessing its service performance through the transmission of live HAS corrections. The HAS performance at high latitudes is evaluated by analyzing data collected at the Polish Polar Station located in Hornsund, in the Norwegian Svalbard archipelago. At such a location, the reception conditions can be affected by poor satellite geometries and ionospheric scintillation may degrade the signal quality. In this respect, data affected by scintillation events were identified and used for the performance analysis. The assessment is carried out by comparing the HAS corrections with the International GNSS Service (IGS) precise products. Moreover, the retrieved HAS corrections are used to assess the Precise Point Positioning (PPP) performance.

The progress of a typhoon can be investigated by analyzing the significant changes in precipitable water vapor (PWV) in the area of interest. In this study, PWV variations of two typhoons in 2018 were investigated based on global navigation satellite system (GNSS)–derived PWV (GNSS-PWV) for predicting typhoon paths. In the study area, GNSS stations are densely distributed like an array so that the array enables the monitoring of the variations of PWV over the region more sensibly than a conventional PWV monitoring method, which consists of the radiosonde-based PWV observations (RS-PWV). To predict a typhoon’s subsequent location, the research proposes the concept of predicted location of typhoon (PLT). PLT is calculated as follows: (1) all the GNSS-PWV in the study area during a typhoon event is estimated; (2) the top five GNSS stations showing the highest PWV are selected; (3) their two-dimensional (2D) mean position is calculated; and (4) this position indicates the PLT corresponding to the progress of the typhoon. The PLT was able to predict a typhoon’s subsequent location approximately 5 h in advance with an average distance discrepancy of 14 km. When the typhoon approached the landfall point, the PLT managed to predict the point approximately 5.3 h in advance, with an average distance discrepancy from the actual path of 13.19 km. These results imply that GNSS-PWV is promising for the prediction of the typhoon path, and it can be used as supplementary information for the current typhoon forecast.

Coastal hazards, such as a tsunamis and storm surges, are a critical threat to coastal communities that lead to significant loss of lives and properties. To mitigate their impact, event-driven water level changes should be properly monitored. A tide gauge is one of the conventional water level measurement sensors. Still, alternative measurement systems can be needed to compensate for the role of tide gauge for contingency (e.g., broken and absence, etc.). Global Navigation Satellite System (GNSS) is an emerging water level measurement sensor that processes multipath signals reflected by the water surface that is referred to as GNSS-Reflectometry (GNSS-R). In this study, we adopted the GNSS-R technique to monitor tsunamis and storm surges by analyzing event-driven water level changes. To detect the extreme change of water level, enhanced GNSS-R data processing methods were applied which included the utilization of multi-band GNSS signals, determination of optimal processing window, and Kalman filtering for height rate determination. The impact of coastal hazards on water level retrievals was assessed by computing the confidence level of retrieval (CLR) that was computed based on probability of dominant peak representing the roughness of the water surface. The proposed approach was validated by two tsunami events, induced by 2012 Haida Gwaii earthquake and 2015 Chile earthquake, and two storm surge events, induced by 2017 Hurricane Harvey and occurred in Alaska in 2019. The proposed method successfully retrieved the water levels during the storm surge in both cases with the high correlation coefficients with the nearby tide gauge, 0.944, 0.933, 0.987, and 0.957, respectively. In addition, CLRs of four events are distinctive to the type of coastal events. It is confirmed that the tsunami causes the CLR deduction, while for the storm surges, GNSS-R keep high CLR during the event. These results are possibly used as an indicator of each event in terms of storm surge level and tsunami arrival time. This study shows that the proposed approach of GNSS-R based water level retrieval is feasible to monitor coastal hazards that are tsunamis and storm surges, and it can be a promising tool for investigating the coastal hazards to mitigate their impact and for a better decision making.

Natural and manmade explosive events occurring on or below the Earth’s surface can be measured remotely in different ways and different places from the ionosphere.

The ionospheric response to explosions which occur at or below the Earth's surface has been noted since the first detonations of nuclear devices during the early period of aboveground testing. Acoustic gravity waves and traveling ionospheric disturbances were detected in association with test explosions carried out by the Union of Soviet Socialist Republics in Novaya Zemlya in 1961. While research in this area has continued, the standards accepted by the Comprehensive Nuclear Test Ban Treaty for detection and confirmation of nuclear explosions have been based on (1) seismic, (2) hydroacoustic, (3) infrasound, and (4) radionuclide monitoring from ground detectors. We suggest that ionospheric sensing offers a complementary methodology that may allow for robust confirmation of explosive events. One method of ionospheric monitoring of explosive events is analysis of total electron content (TEC), available by processing data from Global Navigation Satellite System (GNSS) receivers distributed globally on land masses. Traveling ionospheric disturbances observed by their signature in TEC have been used to detect and confirm mine collapses, mine blasts, earthquakes, volcanic eruptions, and meteorite strikes as well as underground nuclear tests. While an integrated measurement like TEC is not as sensitive to small‐amplitude density perturbations as other methods, the existence of large networks of continuously operating GNSS stations makes this an intriguing new monitoring asset. We report on the current capabilities for detection of explosions via the ionosphere, the outstanding challenges, and prospects for future developments that have potential to augment the current standards for detection and confirmation of nuclear detonations. By leveraging the worldwide network of GNSS receivers, robust confirmation of impulsive events is possible. This methodology complements existing technologies approved by the Comprehensive Test Ban Treaty. The article outlines background theory, new approaches to monitoring of explosions, and challenges that remain to be addressed.