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  • PublicationOpen Access
    Integrated UAV-Borne GPR and LiDAR for Investigating Slope Deformation Processes: The Melizzano Case Study (Southern Italy)
    Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography.
  • PublicationOpen Access
    Extreme two-phase change of ionospheric electron temperature overshoot during geomagnetic storms
    An intense surge in the equatorial electron temperature (T e) at sunrise, known as the morning T e overshoot, has been one of the defining ionospheric features since its discovery early in the Space Age. Despite decades of study, the behavior of the morning overshoot during geomagnetic storms remains poorly understood. We report a two-stage response of the morning T e overshoot to geomagnetic activity, uncovered by a neural network model. Electron temperatures show an initial enhancement during the storm's main phase, followed by a drastic depletion exceeding 1000 K and disappearance of the overshoot in the recovery phase. This two-phase change aligns with the early influence of westward prompt penetration electric field, overtaken by the development of the eastward disturbance dynamo later in the storm. These electric field changes affect vertical plasma drifts that redistribute electron densities, modifying ionospheric cooling rates. Our findings provide new insights into the dynamics of one of the most widely studied ionospheric features and showcase the potential of new-generation digital twin models of near-Earth space environment to reveal previously unrecognized physical patterns. Keywords Ionosphere, Electron temperature, Neural networks Electron temperature (Te) is a fundamental parameter characterizing the Earth's ionosphere and its coupling with the neutral atmosphere, magnetosphere and solar wind 1-3. One of the most notable features of the global Te distribution, discovered early in the Space Age, is the morning electron temperature overshoot 4,5 , which represents a rapid Te increase around the geomagnetic equator at sunrise, often in excess of 3000 K 6. It occurs due to energy exchange between newly ionized photoelectrons and ambient thermal electrons. In regions of low plasma density, each ambient electron receives a greater share of energy compared to regions of dense plasma, making this process particularly efficient. At the equator, a unique combination of low electron density due to the downward E × B drift overnight and inefficient heat removal by conduction allows the morning Te overshoot to develop around 05 h of local time (LT) 6,7. The morning overshoot typically persists until electron density increases around ∼09 LT and presents a global Te maximum during geomagnetically quiet times. Climatological aspects of the morning Te overshoot, including its dependence on altitude, seasons and solar activity, have been analyzed extensively and are generally well understood 5,6,8-11. However, few studies have investigated the dynamics of the morning overshoot during geomagnetic storms. An early theoretical study by Wang et al. 12 proposed a negative correlation of electron temperatures in the overshoot region with the geomagnetic Kp index. Using numerical modeling, they reported the strongest Te depletions at mid-latitudes and a drop of Te by a few hundred degrees around the equator. A more recent observational study by Yang et al. 13 reported multi-day oscillations in the morning overshoot and also suggested a negative correlation with the Kp index, although their study used daily-averaged electron temperature values and did not investigate shorterscale variations within geomagnetic storms.
  • PublicationOpen Access
    Bridging the Gap Between the Earth's Ionosphere and Plasmasphere
    (Washington, D.C. : American Geophysical Union, 2025-11-17) ; ; ;
    Kronberg, Elena
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    Goldstein, Jerry
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    Miyoshi, Yoshizumi
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    Buzulukova, Natalia
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    Pedatella, Nicholas
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    Haas, Bernhard
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    Wang, Dedong
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    Kasahara, Yoshiya
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    Tsuchiya, Fuminori
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    Matsuda, Shoya
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    Shinbori, Atsuki
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    Cold plasma distribution in the ionosphere-plasmasphere system governs wave-particle interactions, plasma energization and loss, and radio wave propagation. A longstanding observational gap at altitudes ∼800-8,000 km has largely prevented studying the coupled dynamics of the two regions. Here, we show that observations by JAXA's Arase mission can bridge this gap. Electron densities inferred from the upper hybrid resonance frequencies measured by Arase are highly consistent with radio occultation profiles from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission, with a median difference of ∼5%. Using the combined COSMIC-Arase data set, we provide a convenient way to reconcile the two regions in empirical models based on the analytical Chapman function inversion for scale height. Our results enable studying fundamental questions about the ionosphere-plasmasphere coupling, their transition, and life cycle of cold plasma in near-Earth space. Plain Language Summary Cold plasma in the ionosphere and plasmasphere plays a critical role in the dynamics of near-Earth space environment. The physical processes within these two regions are different but tightly coupled. Ionospheric particles fill the plasmasphere on the dayside, while the plasmaspheric particles descend into the ionosphere at night and help sustain it in the absence of photoionization. However, how exactly these populations evolve and transfer into one another remains poorly understood due to two longstanding challenges. First, the measurements in the two regions are separated by a gap at altitudes ∼800-8,000 km. Second, fundamentally different techniques are used to measure plasma densities in the plasmasphere and topside ionosphere. Here, we address both of these issues and show that two of the most prominent N e measurement approaches in the respective regions-using wave spectra and radio occultations-yield nearidentical results. Using observations by the Arase mission at altitudes 440-32,000 km, we demonstrate that it is possible to bridge the gap between the ionosphere and plasmasphere. This result enables studying the dynamics of their transition region and several other fundamental questions about the distribution and life cycle of cold plasma in geospace.
  • PublicationRestricted
    The Retrieving of Ionospheric Upper Transition Height Over Low Latitude From C/NOFS Ion Composition Measurements
    (Hoboken, NJ : John Wiley & Sons, 2025-11-26)
    Gao, Shunzu
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    Jiang, Qihui
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    Zhan, Weijia
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    Zheng, Yuhao
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    The upper transition height (UTH), defined as the altitude where the dominant ion changes from O + to light-ions, is a key parameter in the topside ionosphere. In this study, we propose an innovative approach to retrieve ionospheric UTH over low latitudes by integrating the ion composition measurements from the C/NOFS satellite with the deep learning ionosphere model (DLIM). Compared to the direct UTH observations when the O + composition is 0.5, this approach can retrieve UTH when the O + composition ranges from 0.3 to 0.7. Our approach increases the number of available UTH measurements from approximately 92,100 to 1,075,000, which allows more comprehensive local time (LT) and magnetic latitude coverage. The proposed approach was validated using observations from the incoherent scatter radar (ISR) located in Jicamarca. The UTH between retrieved and measured from ISR shows good agreement, with the average value and standard deviation of discrepancy being 0.32 and 5.81 km, respectively. The retrieved UTH exhibits typical local time, seasonal and magnetic latitudinal dependence. Comparison results show that during low solar flux conditions, the retrieved UTH showed excellent consistency with the measured UTH with a relative error of less than 3%. Further analysis revealed a positive correlation between the UTH and the F10.7 index, which is stronger than results from directly measured UTH and ISR observations. The rate of change of retrieved UTH with respect to the F10.7 index varies with local time and can reach up 9.15 km/sfu at noontime (12:00 LT), which could be linked to the enhancement of E × B drifts. Plain Language Summary This study focuses on the upper transition height (UTH)-the altitude where light-ions begin to dominate over O + in the upper ionosphere. It is a key parameter in ionospheric physics and an important measurement for studying the ionosphere and how it connects to the plasmasphere. The C/NOFS satellite, with its unique elliptical orbit, has become an important means for observing UTH. However, due to the continuous orbital decay of the satellite, it was unable to effectively measure UTH during periods of high solar activity, and thus the satellite could only provide UTH observations from 2008 to 2010. To better study the climatological characteristics of UTH and its influencing factors, this paper proposes a method that combines an empirical model with satellite observation to retrieve UTH using ion observation data from the C/NOFS satellite. This algorithm increases the number of UTH observations from the approximately 92,100 to more than 1,075,000-an expansion by tenfold. Based on this extended data set, not only can the distribution characteristics of UTH with respect to latitude, local time, and season be obtained, but it is also possible to analyze its global variations with solar activity-an aspect that previous studies relying solely on direct observations were unable to achieve.
  • PublicationOpen Access
    Can the International Reference Ionosphere Model Predict Long-Term Trends in the Ionosphere?
    The International Reference Ionosphere (IRI) model can partially reproduce ionospheric longterm trends (LTTs), although not intentionally, provided that an appropriate effective ionospheric index is used. We evaluate how model predictions driven by a solar activity proxy (R 12) and by an effective ionospheric proxy (IG 12) can reproduce the long-term variability of the F2-layer critical frequency (foF2) compared to hourly manually-scaled data from the Rome ionosonde station (41.9°N, 12.5°E) over the period 1980-2022. After removing solar-cycle and short-term variability through empirical mode decomposition, a residual monotonic decreasing trend was identified for both measured and modeled foF2. While the R 12-driven model performs well until the late 1990s, it fails thereafter, whereas the IG 12-driven model reproduces LTTs over the entire period. The discrepancy is attributed to a post-23rd solar cycle change in the solar proxy-ionospheric response relationship, highlighting the need for effective indices and explicit time dependence in IRI. Plain Language Summary The Earth's upper atmosphere, specifically the ionosphere, is subject to long-term trends caused by external and internal drivers, such as solar activity and greenhouse gases. This study evaluates whether the International Reference Ionosphere (IRI) model can predict these trends by comparing its outputs with 43 years of data from the Rome ionosonde station between 1980 and 2022. The analysis shows that while the model driven by the standard solar index (R 12) worked well until the late 1990s, it failed to reproduce trends afterward; whereas the model using the effective ionospheric index (IG 12) remained accurate throughout the entire period. This discrepancy indicates a change in the relationship between solar proxies and the ionosphere after the 23rd solar cycle, highlighting the necessity of using effective indices and including explicit time dependence in the IRI model for accurate long-term predictions.