The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. ...In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.
Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of ...earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (Mw 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.
The current study aims at investigating and identifying the ionospheric effects of the geomagnetic storm that occurred during 17–19 March 2015. Incidentally, with SYM‐H hitting a minimum of −232 nT, ...this was the strongest storm of the current solar cycle 24. The study investigates how the storm has affected the equatorial, low‐latitude, and midlatitude ionosphere in the American and the European sectors using available ground‐based ionosonde and GPS TEC (total electron content) data. The possible effects of prompt electric field penetration is observed in both sectors during the main phase of the storm. In the American sector, the coexistence of both positive and negative ionospheric storm phases are observed at low latitudes and midlatitudes to high latitudes, respectively. The positive storm phase is mainly due to the prompt penetration electric fields. The negative storm phase in the midlatitude region is a combined effect of disturbance dynamo electric fields, the equatorward shift of the midlatitude density trough, and the equatorward compression of the plasmapause in combination with chemical compositional changes. Strong negative ionospheric storm phase is observed in both ionosonde and TEC observations during the recovery phase which also shows a strong hemispherical asymmetry. Additionally, the variation of equatorial ionization anomaly as seen through the SWARM constellation plasma measurements across different longitudes has been discussed. We, also, take a look at the performance of the IRI Real‐Time Assimilative Mapping during this storm as an ionospheric space weather tool.
Key Points
Simultaneous presence of positive and negative storms in American sector
Equatorward shift and enhancement of the midlatitude density trough in the American longitude sector
Strong hemispheric asymmetry in the negative storm phase during recovery phase
Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to ...the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.
Scintillation due to ionospheric plasma irregularities remains a challenging task for the space science community as it can severely threaten the dynamic systems relying on space-based navigation ...services. In the present paper, we probe the ionospheric current and plasma irregularity characteristics from a latitudinal arrangement of magnetometers and Global Navigation Satellite System (GNSS) stations from the equator to the far low latitude location over the Indian longitudes, during the severe space weather events of 6–10 September 2017 that are associated with the strongest and consecutive solar flares in the 24th solar cycle. The night-time influence of partial ring current signatures in ASYH and the daytime influence of the disturbances in the ionospheric E region electric currents (Diono) are highlighted during the event. The total electron content (TEC) from the latitudinal GNSS observables indicate a perturbed equatorial ionization anomaly (EIA) condition on 7 September, due to a sequence of M-class solar flares and associated prompt penetration electric fields (PPEFs), whereas the suppressed EIA on 8 September with an inverted equatorial electrojet (EEJ) suggests the driving disturbance dynamo electric current (Ddyn) corresponding to disturbance dynamo electric fields (DDEFs) penetration in the E region and additional contributions from the plausible storm-time compositional changes (O/N2) in the F-region. The concurrent analysis of the Diono and EEJ strengths help in identifying the pre-reversal effect (PRE) condition to seed the development of equatorial plasma bubbles (EPBs) during the local evening sector on the storm day. The severity of ionospheric irregularities at different latitudes is revealed from the occurrence rate of the rate of change of TEC index (ROTI) variations. Further, the investigations of the hourly maximum absolute error (MAE) and root mean square error (RMSE) of ROTI from the reference quiet days’ levels and the timestamps of ROTI peak magnitudes substantiate the severity, latitudinal time lag in the peak of irregularity, and poleward expansion of EPBs and associated scintillations. The key findings from this study strengthen the understanding of evolution and the drifting characteristics of plasma irregularities over the Indian low latitudes.
We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic ...storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator.
The equatorial plasma bubbles (EPBs) are depleted plasma density regions in the ionosphere occurring during the post-sunset hours, associated with the signal fading and scintillation signatures in ...the trans-ionospheric radio signals. Severe scintillations may critically affect the performance of dynamic systems relying on global navigation satellite system (GNSS)-based services. Furthermore, the occurrence of scintillations in the equatorial and low latitudes can be triggered or inhibited during space weather events. In the present study, the possible presence of the EPBs during the geomagnetic storm periods under the 25th solar cycle is investigated using the GNSS-derived total electron content (TEC) depletion characteristics at a low-latitude equatorial ionization anomaly location, i.e., KL University, Guntur (Geographic 16°26′N, 80°37′E and dip 22°32′) in India. The detrended TEC with a specific window size is used to capture the characteristic depletion signatures, indicating the possible presence of the EPBs. Moreover, the TEC depletions, amplitude (S4) and phase scintillation (σφ) indices from multi-constellation GNSS signals are probed to verify the vulnerability of the signals towards the scintillation effects over the region. Observations confirm that all GNSS constellations witness TEC depletions between 15:00 UT and 18:00 UT, which is in good agreement with the recorded scintillation indices. We report characteristic depletion depths (22 to 45 TECU) and depletion times (28 to 48 min) across different constellations confirming the triggering of EPBs during the geomagnetic storm event on 23 April 2023. Unlikely, but the other storm events evidently inhibited TEC depletion, confirming suppressed EPBs. The results suggest that TEC depletions from the traditional geodetic GNSS stations could be used to substantiate the EPB characteristics for developing regional as well as global scintillation mitigation strategies.
The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived vertical ...total electron content (VTEC) from International GNSS Service, magnetic field data, geomagnetic indices, global ionospheric maps, thermospheric mass density (TMD), and O/N 2 ratio measurement. Strong-ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration during the storm recovery phase and following after. First, daytime VTEC (9:00-20:00 UT) presented variations of >15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00-24:00 and 00:00-05:00 UT) VTEC presented low values (5<TECU<7) in mid-latitude region after SSC event during the main phase, followed by high values (>8 TECU) in the recovery phase. Second, the ionospheric values during the storm main phase and following after, at low- and mid-latitudes, caused the equatorial ionization anomaly to expand due to prompt penetration electric field. Furthermore, VTEC enhancements are likely to occur few hours after the SSC of 25 August 2018, while enhancements of TMD and O/N 2 ratio started to appear later on 26 and 27 of August 2018.
Continuous monitoring of ionospheric behavior and subsequent development or improvement of models for the prediction of its parameters with consistent accuracy remains an ongoing challenge. In this ...sense, an integrated approach by combining the signal extraction technique Singular Spectrum Analysis (SSA) with Autoregressive Moving Average (ARMA) is presented in this work to predict the ionospheric Total Electron Content (TEC) values that are responsible for causing ionospheric delays in the trans-ionospheric signal propagation associated with satellite-based communication, navigation, and timing applications. In general, SSA is a nonparametric spectral estimation procedure that decomposes the signals into interpretable and physically significant components. The observed TEC from two Global Positioning System (GPS) stations across the low latitude Saudi Arabian region are considered during the year 2017 that falls in the descending phase of solar cycle-24. The performance of the proposed hybrid model is evaluated by comparing with the sole estimation from the ARMA model and the observed GPS–TEC dataset for two different geomagnetic conditions: a) the regular geomagnetically quiet period of 15 to 29 December, 2017 (
Ap
<
24
and
Dst
>
−
30
nT) and b) the geomagnetic storm period from 7 to 9 September, 2017 (
Dst
min
=
−
142
nT). The corresponding average Precision, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed SSA–ARMA model predictions are 1.79 TECU, 1.23 TECU, and 13.02%. In contrast, the respective values in the exclusive ARMA model are 2.01 TECU, 1.37 TECU, 14.42% at Oman station. The corresponding values for Magna station are 0.92 TECU, 0.61 TECU, and 10.76% (SSA–ARMA) and 1.01 TECU, 0.75 TECU, and 11.33% (ARMA). The results show an improved computational efficiency with minor improvement in the TEC predictions with the proposed SSA–ARMA method compared to the sole employment of the ARMA model by disregarding the extraneous components.
PDF
