Extreme Energy Events (EEE) is an extended Cosmic Rays (CRs) Observatory, composed of about 60 tracking telescopes spread over more than 10 degrees in Latitude and Longitude. We present the metrological characterization of a representative set of actually installed EEE GPS receivers, their calibration and their comparison with respect to dual-frequency receivers for timing applications, as well as plans for a transportable measurement system to calibrate the currently deployed GPS receivers. Finally, the realization of an INRIM Laboratory dedicated to EEE, aimed at hosting reference telescopes and allowing timing studies for Particle Physics/Astrophysics experiments, is presented, as well as the possibility of synchronizing already deployed telescopes utilizing White Rabbit Technique, over optical fiber links, directly with the Universal Time Coordinated time scale, as realized by INRIM (UTC(IT)).

Gravity fields derived from GPS tracking of the three Swarm satellites have shown artifacts near the geomagnetic equator, where the carrier phase tracking on the L2 frequency is unable to follow rapid ionospheric path delay changes due to a limited tracking loop bandwidth of only 0.25 Hz in the early years of the mission. Based on the knowledge of the loop filter design, an analytical approach is developed to recover the original L2 signal from the observed carrier phase through inversion of the loop transfer function. Precise orbit determination and gravity field solutions are used to assess the quality of the correction. We show that the a posteriori RMS of the ionosphere-free GPS phase observations for a reduced-dynamic orbit determination can be reduced from 3 to 2 mm while keeping up to 7% more data in the outlier screening compared to uncorrected observations. We also show that artifacts in the kinematic orbit and gravity field solution near the geomagnetic equator can be substantially reduced. The analytical correction is able to mitigate the equatorial artifacts. However, the analytical correction is not as successful compared to the down-weighting of problematic GPS data used in earlier studies. In contrast to the weighting approaches, up to 9-10% more kinematic positions can be retained for the heavily disturbed month March 2015 and also stronger signals for gravity field estimation in the equatorial regions are obtained, as can be seen in the reduced error degree variances of the gravity field estimation. The presented approach may also be applied to other low earth orbit missions, provided that the GPS receivers offer a sufficiently high data rate compared to the tracking loop bandwidth, and provided that the basic loop-filter parameters are known.

We introduce GUARDIAN, a near-real-time (NRT) ionospheric monitoring software for natural hazards warning. GUARDIAN's ultimate goal is to use NRT total electronic content (TEC) time series to (1) allow users to explore ionospheric TEC perturbations due to natural and anthropogenic events on earth, (2) automatically detect those perturbations, and (3) characterize potential natural hazards. The main goal of GUARDIAN is to provide an augmentation to existing natural hazards early warning systems (EWS). This contribution focuses mainly on objective (1): collecting GNSS measurements in NRT, computing TEC time series, and displaying them on a public website (https://guardian.jpl.nasa.gov). We validate the time series obtained in NRT using well-established post-processing methods. Furthermore, we present an inverse modeling proof of concept to obtain tsunami wave parameters from TEC time series, contributing significantly to objective (3). Note that objectives (2) and (3) are only introduced here as parts of the general architecture, and are not currently operational. In its current implementation, the GUARDIAN system uses more than 70 GNSS ground stations distributed around the Pacific Ring of Fire, and monitoring four GNSS constellations (GPS, Galileo, BDS, and GLONASS). As of today, and to the best of our knowledge, GUARDIAN is the only software available and capable of providing multi-GNSS NRT TEC time series over the Pacific region to the general public and scientific community.

In recent years, low earth orbit navigation augmentation (LEO-NA) has attracted increasing attention and is expected to become a new addition to global navigation satellite systems (GNSSs). When solving complex constellation design problems, traditional optimization algorithms often fail to achieve satisfactory results and are sensitive to parameter settings. We propose a dynamic multi-objective differential evolutionary algorithm based on elite guidance (DMODE-EG). It can select the evolutionary strategy based on the evolutionary state reflected by elite individuals and dynamically modify evolution parameters. Moreover, to achieve more uniform global coverage, we construct a two-layer Walker constellation model for LEO-NA. Then, we use the DMODE-EG algorithm to solve the corresponding multi-objective optimization problem and obtain the optimal constellation parameters. With the augmentation of this LEO-NA constellation to the BeiDou-3 system, the average position dilution of precision (PDOP) values drop to 1.2-2.0 from 1.5-5.5, and the number of visible satellites increases from 8-10 to 13-18. By contrast, some realistic LEO constellations and constellations designed by other algorithms bring weaker improvements and cannot address the problem of high PDOP values in some regions. In addition, simulation results on standard test sets verify the excellent convergence and stability of the DMODE-EG algorithm.

The Galileo High Accuracy Service (HAS) is a GNSS augmentation that provides precise satellite corrections to users worldwide for free directly through Galileo's E6 signal. The HAS service provides free PPP corrections from the Galileo constellation and the Internet, with targeted real-time 95% positioning performance of better than 20 cm horizontal and 40 cm vertical error after 5 min of convergence time globally and shorter in Europe. The HAS initial service, under validation at the time of writing, provides these capabilities with a reduced performance (based on the current Galileo stations network). Live HAS test signals broadcasted from the Galileo satellites during summer 2022 have been decoded and analyzed. Corrections include Galileo and GPS orbit, clock, and code bias corrections, with SISRE of 10.6 cm and 11.8 cm for Galileo and GPS, respectively. Code bias corrections showed good performance as well, with rms of 0.28 ns, 0.26 ns, and 0.22 ns for Galileo C1C-C5Q, C1C-C7Q, and C1C-C6C, respectively, and 0.20 ns for GPS C1C-C2L. Float PPP positioning performance results show that the combined Galileo and GPS solution can already achieve the HAS full service accuracy performance target and is close in terms of convergence time, with 95% rms of 13.1 cm and 18.6 cm horizontally and vertically, respectively, in kinematic mode, and with a 95% convergence time of 7.5 min. The latter is expected to be improved with the inclusion of satellite phase bias and local atmospheric corrections. With these early Galileo HAS test signals, this preliminary analysis indicates that the HAS full service targets are attainable. Finally, a correction latency analysis is performed, showing that even with latency of up to 60 s, positioning can remain within the targeted HAS accuracy performance.

We study, for the first time, the physical coupling and detectability of meteotsunamis in the earth's atmosphere. We study the June 13, 2013 event off the US East Coast using Global Navigation Satellite System (GNSS) radio occultation (RO) measurements, Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures, and ground-based GNSS ionospheric total electron content (TEC) observations. Hypothesizing that meteotsunamis also generate gravity waves (GWs), similar to tsunamigenic earthquakes, we use linear GW theory to trace their dynamic coupling in the atmosphere by comparing theory with observations. We find that RO data exhibit distinct stratospheric GW activity at near-field that is captured by SABER data in the mesosphere with increased vertical wavelength. Ground-based GNSS-TEC data also detect a far-field ionospheric response 9 h later, as expected by GW theory. We conclude that RO measurements could increase understanding of meteotsunamis and how they couple with the earth's atmosphere, augmenting ground-based GNSS TEC observations.

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL's GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second  7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first  7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip. Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence.

Teunissen (J Geod 98(83):1-16, 2024) proposed the ambiguity-resolved (AR) detection theory for GNSS mixed-integer model validation. In this contribution, we study the performance of the AR detector through analysis and simulation experiments and compare it with the ambiguity-float (AF) and ambiguity-known (AK) detectors. We describe how the detectors can be implemented and how to evaluate their performance by computing the power as functions of the model misspecifications' size. We present two simulation experiments with single- and dual-frequency GPS models and demonstrate that the AR detector can provide a larger detection power than the AF detector, even if the success rate is not close to one. Then, we obtain power functions over 25 user locations with five observation models and 72 satellite geometries per location per model. We find that the AR detector increases the detection probability of ionosphere and troposphere delays by 47% and 60% on average when the success rate is larger than 97.5% and the level of significance is 0.01. We also find the AR detection power to be larger than that of the AF detector in case of multi-dimensional misspecifications.

Global ionospheric maps (GIM) are commonly used ionospheric products in high-precision Global Navigation Satellite System (GNSS) applications. To meet the increasing demand for real-time (RT) applications, the International GNSS Service (IGS) officially started a real-time service in 2013. One of the tasks of the real-time service is the calculation of real-time GIMs. However, the accuracy of current real-time GIMs is still significantly worse than that of the final GIMs, which are the most accurate ionospheric products but have a latency of several days. The IGS RT GIMs exhibit an RMSE of around 3.5-5.5 total electron content units (TECU) compared to the final GIMs. This study focuses on improving the accuracy of existing real-time GIMs through machine learning (ML) approaches, specifically convolutional neural networks (CNN) and conditional generative adversarial networks (cGAN). We apply our method to the IGS combined real-time GIMs and to Universitat Politècnica de Catalunya (UPC) GIMs. We consider over 130'000 pairs of real-time and final GIMs. Over a 3.5-month test period, the proposed approach shows promising results with a reduction of more than 30% in mean absolute error for the real-time GIMs. Especially for regions with high VTEC values, we find a significant improvement of nearly 50%. The ML-enhanced real-time GIMs also exhibit improved positioning performance for single-frequency GNSS positioning with reductions in the 3D error up to 21 cm. Overall, our proposed method demonstrates great potential in generating more accurate and refined real-time GIMs.