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Nature Geoscience, Published online: 09 January 2025; doi:10.1038/s41561-024-01613-z

Convection-permitting regional climate simulations suggest that irrigation reduces daytime urban heat stress in North America.

Abstract

In this paper, we studied the impact of solar activity, especially proton density, He++/H+ ratio and temperature of solar wind, on the geomagnetic field and thereby on earth’s climate. The verified data of these indices are collected from the official websites: wdc.kugi.kyoto-u.ac.jp and www.srl.caltech.edu/ace. Using the data values, both the indices are analyzed and studied to explore the link between solar activity and geomagnetic field. The magnetic field is irregular with negative and positive peaks and at the same time it shows the uniformity with the irregularities of solar wind plasma parameters. It has been observed that solar wind plasma has a significant influence on the intensity of magnetic field of earth and this correlation can be used for weather forecasts and climatic studies in the future.

Abstract

This study investigates the vertical electron content (VTEC) variations and depletions using two years of Global Positioning System (GPS), Total Electron Content (TEC) data from 2012 and 2013. The data, gathered at altitudes between 5° and 15° and longitudes between 34° and 48°, was specifically focused on quiet days and analyzed from nine GPS stations. Employing a spherical model and standard kriging interpolation techniques, the research explored hourly, diurnal, and seasonal fluctuations of VTEC over the two-year period. The spherical model demonstrated high efficacy in estimating data with short lag distances, effectively capturing hourly and daily VTEC fluctuations. Diurnal VTEC variations showed a consistent pattern: increasing from dawn, peaking at 1200 UT, and declining to a minimum after 1800 UT. The peak in diurnal variation was most pronounced at Debark, with similar patterns observed at other stations, reflecting consistent ionospheric behaviors due to geomagnetic conjugcy. A strong correlation was observed between the alignment of the solar terminator and magnetic meridian during equinox seasons and VTEC variation and depletion, with the most significant effects during equinoctial seasons. The study identified a distinct north-south gradient in VTEC within the region, with levels exceeding 65 TECU in the north and around 40 TECU in the south, depending on ionospheric conditions. Nighttime VTEC levels typically decreased to approximately 5 TECU. The spatial distribution analysis of TEC revealed a pronounced maximum concentration in the northeastern sector, contrasting with a minimal concentration in the southwestern sector. This research provides valuable insights into the spatial and temporal behaviors of VTEC, enhancing our understanding of ionospheric dynamics within the specified region.

Abstract

Fractal analysis of VLF electric field data obtained by using vertical antenna located at Chaumuhan, Mathura station (Lat., 27.5° N, Long., 72.68° E) has been carried out using Higuchi method for investigating the impact of moderate shallow earthquakes (M = 4.9–5.6, depth 4.44–16.7 Km) that occurred during February 1, 2016 to October 31, 2016 (excluding April 2016) on the fractal dimension of VLF data. The results of the analysis show that daily values of fractal dimension vary much above and below the monthly mean during the period of observations, 1–30 days before and 1–30 days after the onset of the quakes considered in the present study. The ranges of reductions and enhancements in fractal dimension from the monthly mean are 0.05–0.33 and 0.054–0.43 respectively while the percentage ranges of reductions and enhancements in its daily variation are 3.0–23.21 and 2.81–19.88% respectively. The observed variations in fractal dimension have also been studied in the light of other expected sources like, magnetic storms, lightning activity, local building noises, and instrumental errors which may affect the fractal dimension of the VLF data. It is noticed that the observed variations of fractal dimensions do not correspond to these spurious sources considered. Further, model describing the genesis of VLF emissions in preparatory zones of the impending seismic events and their mechanism of transmission to the observing station have also been discussed.

Abstract

Satellite navigation systems are used for positioning purposes, however to calculate an accurate position, it is crucial to take into account all possible sources of error. The Ionosphere is the primary cause of the positional error. There is a lot of research into first-order ionospheric error estimation and removal. Due to the growing demand for positioning precision across a wide range of applications, significant research has been done over the last two decades to ascertain the impact of second-order ionospheric error (SOIE). However, very less research has been identified that examines the relationship between SOIE and the receiver’s geographic location and total electron content (TEC). Achieving the desired millimeter/centimeter level positional accuracy in these regions requires the study of a realistic diurnal and seasonal variability of SOIE because the behavior of ionospheric TEC in equatorial and low-latitude regions (Indian region in this case) is highly dynamic. Additionally, NavIC (Navigation with Indian Constellation), an Indian satellite navigation system, uses carrier frequencies, namely L5 and new frequency S1, as opposed to GPS L1 and L2, which presents a fresh chance to investigate the effects of SOIE on these frequencies. This research may serve as a benchmark for systems like NavIC that are using L5 and new S-band frequencies for satellite signal transmission, space weather monitoring, and ionosphere abnormalities research. To comprehend various elements of its seasonal properties, this research estimates and analyses SOIE. Data from the SOIE were examined for 12 months, from May 2018 to February 2019, to analyze the diurnal and seasonal fluctuation. It has been noted that seasonal and diurnal fluctuations have a substantial impact on the SOIE. In comparison to the winter months, the SOIE levels are higher in the summer and equinoctial months. Although the SOIE peak levels are similar during the equinoctial and summer months, a higher midnight value and a slowly declining rate have been noted. At L5 frequency, there is a significant seasonal fluctuation in SOIE (–1.1 to –2.84 cm), whereas at S1 shows just a little seasonal variation (–0.1 to –0.3 cm) throughout the year. Additionally, geostationary orbit (GEO) satellites are discovered to be more suitable for the analysis of SOIE than satellites in geosynchronous orbit (GSO), and they might also be employed for ionospheric studies.

Abstract

Terrestrial scale is one of the key datum parameters in the realization of the International Terrestrial Reference Frame (ITRF), which is defined by Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR) in the latest ITRF2020. Currently, the scale of GNSS is aligned to ITRF by estimating phase center offsets (PCOs) of GNSS satellites in a global adjustment with minimum constraints to ITRF. With the proposal of space tie concept in recent years, the co-location of different techniques on the same Low Earth Orbit (LEO) spacecraft provides a possible alternative to achieve this scale datum transfer between different techniques. In this study, we investigate the potential of terrestrial scale transfer between GNSS and SLR using the co-location onboard LEO satellites. The integrated precise orbit determination of GNSS and LEO satellites is performed based on one year of onboard GPS data and SLR observations from GRACE-FO and Swarm satellites as well as a global GNSS network. Two GNSS-only solutions and four GNSS + SLR combined solutions are generated. The results indicate that the scale determined by LEO gravitational constraint in the GNSS-only solution presents an average offset of -0.35 ppb w.r.t. ITRF2020. The space-ties onboard LEO satellites fail to transfer SLR scale information to GNSS network. With the inclusion of SLR observations to LEO satellites, the scale factor of the combined solution is only changed by less than 0.05 ppb with respect to the GNSS-only solution. The small changes of a few millimeters in GPS PCO of the orbital radial direction for the combined solution also demonstrate that the GPS z-PCOs cannot inherit any SLR scale information through LEO co-locations. Meanwhile, we find that the range biases of GRACE-FO and Swarm satellites achieve a good consistency for the majority of SLR stations, since these satellites carry the same type of laser retroreflector arrays and can achieve comparable orbit accuracy. The result indicates that estimating a common range bias parameter is sufficient for GRACE-FO and Swarm when using the SLR observations from these satellites.

Nature Geoscience, Published online: 08 January 2025; doi:10.1038/s41561-024-01617-9

Multiple factors, including slab geometry and upper-plate stress state, determine the variation in slip behaviour along most megathrusts, according to a synthesis of observations of the Alaska, Hikurangi and Nankai subduction zones.

Abstract

Vehicular attitude can be estimated using micro-electro-mechanical systems (MEMS) based magnetic, angular rate, and gravity (MARG) sensors or global navigation satellite systems (GNSS). In challenging environments external accelerations, magnetic distortions, and failure of GNSS will result in significant attitude estimation errors. We proposed a hybrid attitude estimation algorithm based on the low-cost dual-antenna GNSS/MEMS MARG sensor integration, in which the two GNSS antennas are connected to two separate low-cost receivers. Heading and pitch angles are obtained from the moving baseline spanned by the two antennas. An error state Kalman filter is built for data fusion, the filter shares the identical kinematic model but switches the measurement model according to the valid aiding sources. Six possible measurement update schemes are conditioned on the availability of GNSS-derived angles and the disturbances detected in the MARG sensor data. The accuracy degradation of attitude estimation caused by disturbances is alleviated by adjusting the measurement covariance matrix adaptively. A land vehicle-based dynamic experiment was performed to assess the proposed algorithm. Compared to the MARG sensor alone method, the root mean square errors of the proposed GNSS/MARG sensor integrated method were reduced by 38.9%, 65.8%, and 45.6% in the roll, pitch, and yaw angles, respectively.

Abstract

Atmospheric weighted mean temperature (Tm) is pivotal for converting zenith wet delay (ZWD) derived from global navigation satellite system (GNSS) signal to precipitable water vapor (PWV). Currently, most Tm models are developed based on radiosonde (RS) data or reanalysis data. These models are limited by the availability of measured surface temperature and the accuracy of input data used during modeling. Additionally, they face challenges in accounting for the diurnal effect on Tm. In this study, we innovatively use the latest AI weather model, Pangu-Weather, to estimate surface air temperature (Pangu-Ts) in 2016–2019. Compared with the measured surface temperature (RS-Ts) at RS stations, bias and root mean square error (RMSE) are − 0.75 K and 2.54 K, respectively. Subsequently, a Tm forecast model (RF-Tm) in China is developed based on this data using random forest (RF). The model only requires the input of time, 3D coordinates of stations, and predicted Pangu-Ts data to yield the forecasted Tm estimates. The validation results based on RS data show that bias of the RF-Tm is − 0.38 K and the RMSE is 2.47 K. Through comparison and validation with the Bevis model, GPT3, and the Tm forecast model (BP-Tm) developed using back propagation neural network (BPNN), RF-Tm demonstrates reductions in RMSE of 41.33%, 39.46%, and 2.76%, respectively. The mean values with theoretical RMSE and relative error of PWV derived from the RF-Tm are 0.171 mm and 0.90%. The RF-Tm proposed in this study can provide reliable and high-precision Tm estimation for real-time GNSS PWV retrieval.

Abstract

Jamming of Global Navigation Satellite System (GNSS) signals severely affects the security of critical infrastructures and applications. The localization of intentional jamming sources, jammers, is an important step in securing GNSS resilience as it provides the authorities with technical tools to prevent the jamming. However, jammers are difficult to localize in dense urban areas because the existence of multipath and non-line-of-sight propagation challenges conventional methods significantly. This challenge has not been comprehensively addressed in previous research. Motivated by this gap, a ray-tracing tool using 3-D city models is established to simulate jamming signal propagation with high precision and thereby augment the existing signal simulators, and measurements for localization are modeled by characterizing a commercial GNSS receiver under jamming conditions. Then, we propose a novel two-step strategy which consists of an ensemble subspace k-Nearest-Neighbor (KNN) as a raw-predictor and an improved gravitational searching algorithm (GSA) as a fine-optimizer. Based on this, two cloud-computing-based schemes using signal-matching and joint-localization in fine-optimizing stage are proposed. Finally, the proposed methods are evaluated in three typical urban areas, and their effectiveness and superiority over conventional least-squares method based on an empirical path-loss model are validated.

Abstract

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these issues, we propose a novel initialization for PINNs based on meta-learning to enhance their performance. In our framework, we first utilize meta-learning to train a common network initialization for a distribution of medium parameters (i.e., velocity models). This phase employs a unique training data container, comprising a support set and a query set. We use a dual-loop approach, optimizing network parameters through a bidirectional gradient update from the support set to the query set. Following this, we use the meta-trained PINN model as the initial model for a regular PINN training for a new velocity model, where the optimization of the network is jointly constrained by the physical and regularization losses. Numerical results demonstrate that, compared to the vanilla PINN with random initialization, our method achieves a much faster convergence speed, and also obtains a significant improvement in the results accuracy. Meanwhile, we showcase that our method can be integrated with existing optimal techniques to further enhance its performance.

Abstract

The development of optical clocks has experienced significant acceleration in recent years, positioning them as one of the most promising quantum optical sensors for next-generation gravimetric missions (NGGMs). This study investigates the feasibility of retrieving the temporal gravity field via improved optical clocks through a closed-loop simulation. It evaluates optical clock capabilities in temporal gravity field inversion by considering the clock noise characteristics, designing satellite formations, and simulating the performance of optical clocks. The results indicate that optical clocks exhibit higher sensitivity to low-degree gravity field signals. However, when the optical clock noise level drops below 1 × 10−19 \(/\sqrt{\uptau }\) (τ being the averaging time in seconds) in the satellite-to-ground (SG) mode or below 1 × 10−20 \(/\sqrt{\uptau }\) in the satellite-to-satellite (SS) mode, atmospheric and oceanic (AO) errors become the dominant source of error. At this noise level, optical clocks can detect time-variable gravity signals up to approximately degree 30. Compared to existing gravity measurement missions such as GRACE-FO, optical clocks exhibit consistent results in detecting signals below degree 20. If the orbital altitude is reduced to 250 km, the performance of optical clocks across all degrees aligns with the results of GRACE-FO. Furthermore, the study reveals that lowering the orbital altitude of satellite-based optical clocks from 485 to 250 km improves results by an average of 33%. Switching from the SS mode to the SG mode results in an average improvement of 51%, while each order-of-magnitude improvement in clock precision enhances results by an average of 60%. In summary, these findings highlight the tremendous potential of optical clock technology in determining Earth’s temporal gravity field and provide crucial technological support for NGGMs.

Abstract

The accuracy of satellite clock bias (SCB) directly affects the precision and reliability of positioning in Global Navigation Satellite System. Through precise clock bias prediction, positioning errors can be effectively reduced, and the overall reliability of the system can be improved. This paper proposes a deep learning model for SCB prediction based on the fusion of the Beluga Whale Optimization (BWO), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism. The CNN is utilized to extract the spatiotemporal characteristic information from the clock bias sequence, while the BiGRU fully extracts relevant features through forward and backward propagation. The introduction of an attention mechanism aims to preserve essential features within the clock bias sequence to enhance feature extraction by both CNN and BiGRU networks. Additionally, the BWO is employed to optimize parameter selection in order to improve model accuracy. Experimental verification demonstrates that for the BeiDou Navigation Satellite System’s hydrogen-maser atomic clocks, the predicted clock bias for 6 h, 3 days, and 15 days are 0.078 ns, 0.475 ns, and 2.130 ns respectively, superior to the CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, GRU, LSTM, BP, Kalman filter and ARIMA models.

On January 1st, 2024, a moment magnitude (Mw) 7.5 earthquake occurred on an active reverse fault in the northern part of Noto Peninsula, being one of the largest intraplate events recorded in Japan. In previous s...

Abstract

In this paper, we review the theoretical studies of the electromagnetic and other non-seismic phenomena accompanying earthquakes. This field of geophysical research is at the interception of several sciences: electrodynamics, solid-state physics, fracture mechanics, seismology, acoustic-gravity waves, magnetohydrodynamics, ionospheric plasma, etc. In order to make physics of these phenomena as transparent as possible, we use a simplified way of deriving some theoretical results and restrict our analysis to order-of-magnitude estimates. The main emphasis is on those theoretical models which give not only a qualitative, but also a quantitative, description of the observed phenomena. After some introductory material, the review is begun with an analysis of the causes of local changes in the rock conductivity occasionally observed before earthquake occurrence. The mechanisms of electrical conductivity in dry and wet rocks, including the electrokinetic effect, are discussed here. In the next section, the theories explaining the generation of low-frequency electromagnetic perturbations resulting from the rock fracture are covered. Two possible mechanisms of the coseismic electromagnetic response to the propagation of seismic waves are studied theoretically. Hereafter, we deal with atmospheric phenomena, which can be related to seismic events. Here we discuss models describing the effect of pre-seismic changes in radon activity on atmospheric conductivity and examine hypotheses explaining abnormal changes in the atmospheric electric field and in infrared radiation from the Earth, which are occasionally observed on Earth and from space over seismically active regions. In the next section, we review several physical mechanisms of ionospheric perturbations associated with seismic activity. Among them are acoustic-gravity waves resulting from the propagation of seismic waves and tsunamis and ionospheric perturbations caused by vertical acoustic resonance in the atmosphere. In the remainder of this paper, we discuss whether variations in radon activity and vertical seismogenic currents in the atmosphere can affect the ionosphere.

Abstract

Plasma flows with enhanced dynamic pressure, known as magnetosheath jets, are often found downstream of collisionless shocks. As they propagate through the magnetosheath, they interact with the surrounding plasma, shaping its properties, and potentially becoming geoeffective upon reaching the magnetopause. In recent years (since 2016), new research has produced vital results that have significantly enhanced our understanding on many aspects of jets. In this review, we summarise and discuss these findings. Spacecraft and ground-based observations, as well as global and local simulations, have contributed greatly to our understanding of the causes and effects of magnetosheath jets. First, we discuss recent findings on jet occurrence and formation, including in other planetary environments. New insights into jet properties and evolution are then examined using observations and simulations. Finally, we review the impact of jets upon interaction with the magnetopause and subsequent consequences for the magnetosphere-ionosphere system. We conclude with an outlook and assessment on future challenges. This includes an overview on future space missions that may prove crucial in tackling the outstanding open questions on jets in the terrestrial magnetosheath as well as other planetary and shock environments.

Abstract

Lateral inhomogeneity in the Earth’s mantle affects the tidal response. The current study reformulates the expressions for estimating the lateral inhomogeneity effects on tidal gravity with respect to the unperturbed Earth and supplements some critical derivation process to enhance the methodology. The effects of lateral inhomogeneity are calculated using several real Earth models. By considering the collective contributions of seismic wave velocity disturbances and density disturbance, the global theoretical changes of semidiurnal gravimetric factor are obtained, which vary from − 0.22 to 0.22% compared to those in a layered Earth model, about 1/2 of the ellipticity’s effect. The gravity changes caused by lateral-inhomogeneous disturbances are also computed and turn out to be up to 0.16% compared to the changes caused by tide-generating potential. The current study compares the influences of lateral inhomogeneity with rotation and ocean tide loading. The results indicate that the rotation and ellipticity on tidal gravity are the most dominant factors, the ocean tide loading is the moderate one, and the lateral inhomogeneity in the mantle has the least significant influence. Moreover, an anti-correlation between the effective elastic thickness and gravimetric factor change caused by lateral inhomogeneity is found, implying that it is difficult to generate tidal response at regions with high rigidity. We argue that the gravimetric factor change can be used as an effective indicator for lithospheric strength.

Abstract

Currently, the susceptibility of Global Navigation Satellite System (GNSS) signals underscores the importance of accurate GNSS time spoofing detection as a critical research area. Traditional spoofing detection methods have limitations in applicability, while the current learning-based algorithms are only applicable to the judgment of collected data, which is difficult to apply to real-time detection. In this paper, a real-time spoofing detection framework based on feature processing is proposed. The approach involves feature integration and correlation coefficient screening on each epoch of multi-satellite data. Additionally, special standardization strategy is employed to enhance the feasibility of real-time application. In the experimental phase, apart from utilizing the open dataset, an experimental platform is developed to generate dual-system data for experimentation purposes. Compared with the traditional clock difference detection method, this algorithm improves the detection performance by about 25%. Furthermore, the framework proposed can improve the detection F1 score of basic machine learning models and greatly reduce the computation time by more than ten times. On most datasets, models incorporating the framework achieved F1 scores of more than 99% and average response times of less than 10 μs. In summary, this study provides an effective intelligent solution for the application of real-time receiver spoofing detection.