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The occurrence of Venusian lighting has been debated for decades. Terrestrial lightning generates whistler waves, and many whistlers have been observed in Venus's ionosphere and induced magnetosphere. Venusian lightning occurrence rates derived from these whistler observations are relatively high. However, optical flashes on Venus are exceedingly rare and Venus encounters by multiple spacecrafts have not detected lightning. These non‐detections and rare optical observations are consistent with low Venusian lightning occurrence rates, which is incompatible with the high whistler‐derived rates. We present observations of whistlers during a Parker Solar Probe Venus gravity assist and eliminate lightning as a possible source. These waves are observed at an altitude of 0.39 Venus radii on Venus' nightside with planetward propagation and are simultaneous with Langmuir waves. This provides a mechanism for whistler generation near Venus that does not require lightning, and suggests that whistler‐based lightning occurrence rates may be overestimated. Plain Language Summary Whistler waves are a type of plasma wave. These waves can be generated in several ways, including by lightning. Every lightning strike on Earth generates a whistler wave, but only some of the whistler waves in near‐Earth space are generated by lightning. Many whistler waves have been detected near Venus and have been used to argue that lightning likely occurs on Venus at a relatively high rate. However, other signatures of lightning (including flashes of light in the sky) on Venus are very rare, which indicates that Venusian lightning must occur at a very low rate. The discrepancy between these different signatures of lightning on Venus means that we do not know how often Venus actually experiences lightning. We use data from Parker Solar Probe during a Venus gravity assist to study whistler waves that occurred on the nightside of Venus, very close to the planet. We observe that these waves are traveling toward Venus, which means they could not have been generated by lightning. This shows that whistler waves can occur near Venus without being generated by lightning and indicates that the occurrence rates of Venusian lightning based on whistler wave observations might be overestimated. Key Points Whistler waves were observed at an altitude of 0.39 Venus radii on Venus's nightside with planetward Poynting vector Langmuir waves occurred simultaneously with the whistlers, suggesting electron beam driving with magnetotail origins Lightning is eliminated as a possible generation mechanism for these whistler waves

We show that atmospheric gravity waves can generate plasma ducts and irregularities in the plasmasphere using the coupled SAMI3/WACCM‐X model. We find the equatorial electron density is irregular as a function of longitude which is consistent with CRRES measurements (Clilverd et al., 2007, https://doi.org/10.1029/2007ja012416). We also find that plasma ducts can be generated for L‐shells in the range 1.5–3.0 with lifetimes of ∼ 0.5 hr; this is in line with observations of ducted VLF wave propagation with lifetimes of 0.5–2.0 hr (Clilverd et al., 2008, https://doi.org/10.1029/2007ja012602; Singh et al., 1998, https://doi.org/10.1016/s1364-6826(98)00001-7). Plain Language Summary Electromagnetic plasma waves, known as whistler waves, are observed to propagate in the ionosphere/plasmasphere system where the ionosphere is nominally defined as the partially ionized gas surrounding the earth in the altitude range 90–1,000 km and the plasmasphere is essentially the extension of the ionosphere 1,000s of km into space along closed geomagnetic field lines. Whistler wave propagation has been characterized as ducted and non‐ducted. Ducted propagation is guided along the magnetic field by density tubes in which the plasma density is lower or higher than the background plasma. However, the physical processes that generate these ducts has remained unclear. We show that these plasma ducts can be generated by atmospheric gravity waves that perturb the ionosphere and plasmasphere electron density using the coupled SAMI3/WACCM‐X model. Key Points Atmospheric gravity waves can generate plasma ducts and irregularities in the plasmasphere using the coupled SAMI3/WACCM‐X model The electron density in the equatorial plasmasphere is irregular as a function of longitude Plasma ducts can be generated for L‐shells in the range 1.5–3.0 with lifetimes of 0.5–2.0 hr

This paper reports on the standing whistler waves upstream of Mercury's quasi‐perpendicular bow shock. Using MESSENGER's magnetometer data, 36 wave events were identified during interplanetary coronal mass ejections (ICMEs). These elliptic or circular polarized waves were characterized by: (a) a constant phase with respect to the shock, (b) propagation along the normal direction to the shock surface, and (c) rapid damping over a few wave periods. We inferred the speed of Mercury's bow shock as ∼26 km/s and a shock width of 1.87 ion inertial length. These events were observed in 20% of the MESSENGER orbits during ICMEs. We conclude that standing whistler wave generations at Mercury are generic to ICME impacts and the low Alfvén Mach number (MA) collisionless shock, and are not affected by the absolute dimensions of the bow shock. Our results further support the theory that these waves are generated by the current in the shock. Plain Language Summary The strength of planetary bow shocks varies with the planet's heliocentric distance from the Sun. Studying the bow shocks of other planets is important for extending our understanding of collisionless‐shock physics. In the solar system, the bow shocks of Mercury are unique as they are produced by low Mach numbers and low plasma beta solar wind blowing over a small magnetized body that is 1–2 orders smaller than Earth. The standing whistler waves upstream of the bow shock of Mercury were determined through statistical analyses. Similar to the observations at Earth, these waves were rapidly damping with a proportion of the wave periods; however, the damping distance at the spacecraft frame was considerably shorter at only a few kilometers upstream in the small‐scale bow shock of Mercury. The high occurrence rate of standing whistler waves suggests that Mercury's bow shock is a natural plasma laboratory, which can be used to further investigate low MA planetary shocks during the upcoming BepiColombo mission. Key Points First survey of standing whistler waves upstream of Mercury's bow shock Standing whistler waves are common at Mercury during interplanetary coronal mass ejections Our results support the theory that current in shock generates standing whistler waves

We have proposed wave modes physics to explain the power spectrum scaling in the magnetic presence of magnetic islands of the magnetopause. In the present manuscript, a model to study the role of waves in turbulence generation by taking the powerful whistler wave and weak KAW has been presented. The corresponding power spectrum, current sheet structures, and energy dissipation at magnetopause presence of magnetic islands are also analysed and discussed its relevance with observational data. This study is carried out numerically using the pseudospectral method here, space integration for spatial integrations and finite difference method for temporal integration have been used. To understand the physics behind the field localization, we developed a semianalytical model and got the development of electron and ion scale localization. Our study enhances the understanding of the role of wave-wave interaction physics in the presence of magnetic islands and the role of multi-scale physics in turbulence generation in space plasmas.

Interchange instability is known to drive fast radial transport of particles in Jupiter's inner magnetosphere. Magnetic flux tubes associated with the interchange instability often coincide with changes in particle distributions and plasma waves, but further investigations are required to understand their detailed characteristics. We analyze representative interchange events observed by Juno, which exhibit intriguing features of particle distributions and plasma waves, including Z‐mode and whistler‐mode waves. These events occurred at an equatorial radial distance of ∼9 Jovian radii on the nightside, with Z‐mode waves observed at mid‐latitude and whistler‐mode waves near the equator. We calculate the linear growth rate of whistler‐mode and Z‐mode waves based on the observed plasma parameters and electron distributions and find that both waves can be locally generated within the interchanged flux tube. Our findings are important for understanding particle transport and generation of plasma waves in the magnetospheres of Jupiter and other planetary systems. Plain Language Summary The centrifugal interchange instability, which has been observed in rapidly rotating planets, like Saturn and Jupiter, moves cold plasmas inside of the magnetosphere further away, and transports hotter, less dense plasmas toward the inner magnetosphere. These moving flux tubes have been observed at Jupiter together with plasma waves, but their detailed characteristics are not fully understood. In the present study, we use observations from the Juno spacecraft to report multiple representative interchange events and evaluate the properties of energetic particles and plasma waves. Furthermore, we use linear theory to calculate the growth rates of Z‐mode and whistler‐mode waves during these events. Our findings reveal the typical features of plasma waves and particles during interchange events, which provide important insights into particle transport and generation of plasma waves at Jupiter and possibly other magnetized planets in our solar system and beyond. Key Points Several plasma transport events associated with interchange instability are identified alongside plasma waves using Juno observations Linear growth rate analyses indicate that waves can be locally generated during interchange events due to anisotropic electron distributions Our findings provide insights into electron transport and plasma wave dynamics during interchange events in planetary magnetospheres

Interchange instability is known to drive fast radial transport of electrons and ions in Jupiter's inner and middle magnetosphere. In this study, we conduct a statistical survey to evaluate the properties of energetic particles and plasma waves during interchange events using Juno data from 2016 to 2023. We present representative examples of interchange events followed by a statistical analysis of the spatial distribution, duration and spatial extent. Our survey indicates that interchange instability is predominant at M‐shells from 6 to 26, peaking near 17 with an average duration of minutes and a corresponding M‐shell width of <∼0.05. During interchange events, the associated plasma waves, such as whistler‐mode, Z‐mode, and electron cyclotron harmonic waves exhibit a distinct preferential location. These findings provide valuable insights into particle transport and the source region of plasma waves in the Jovian magnetosphere, as well as in other magnetized planets within and beyond our solar system. Plain Language Summary The radial transport of plasma around a magnetized planet is crucial for understanding the underlying magnetospheric dynamics. Jupiter's magnetospheric dynamics are primarily dominated by the rapid rotation and plasma source from Io. This rapid rotation drives the interchange instability, where hot, low‐density plasma is moved toward the inner magnetosphere. During this process, the inward moving flux tube builds up magnetic pressure, potentially leading to the trapping of particles alongside plasma waves. In this study, we present several typical examples of interchange events, and conduct a statistical analysis to explore their spatial distribution, duration and spatial extent, as well as the typical features of the associated plasma waves. This survey provides insights into mass transport, the source of these plasma waves in Jupiter's magnetosphere, with potential implications for other magnetized planets within and beyond our solar system. Key Points Our statistical survey indicates that interchange events occur over L (or M)‐shells ∼6–26 at Jupiter with a peak occurrence rate at M ∼ 17 During interchange events, various types of plasma waves are intensified, each exhibiting a distinct preferential location The duration and the corresponding spatial extent of interchange events are analyzed for multiple events

We report the recent observation of a density duct structure in the equatorial plasmasphere, identified using data from the NASA Van Allen Probes (RBSP). The structure consists of a low‐density duct located above a high‐density duct, forming a “double duct” system capable of simultaneously trapping and guiding multiple whistler‐mode waves at distinct frequencies. Simulations based on the electron‐MHD model confirm that such density double ducts enable efficient confinement and propagation of whistler‐mode waves along the background magnetic field.

Magnetic reconnection is a crucial physical process in laboratory and astrophysical plasmas. Plasma waves are believed to provide the dissipation mechanism in magnetic reconnection. In this paper we analyze the properties of low‐frequency waves in a magnetotail reconnection diffusion region with a small guide field and high β. Using the k‐filtering method on the magnetic field data measured by Cluster spacecraft, we found that low‐frequency waves in the diffusion region were highly oblique propagating mode. We compared the measured dispersion relation with theoretical ones calculated using the linear (hot) two‐fluid and Vlasov‐Maxwell theory. It is found that the observed waves in the diffusion region (with high plasma β) follow the dispersion relation of the Alfvén‐Whistler wave mode. Comparisons with previous simulations and observational results are also discussed.

The automatic detection of shapes or patterns represented by signals captured from spacecraft data is essential to revealing interesting phenomena. A signal processing approach is generally used to extract useful information from observation data. In this paper, we propose an image analysis approach to process image datasets produced via plasma wave observations by the Arase satellite. The dataset consists of 31,380 PNG files generated from the dynamic power spectra of magnetic wave field data gathered from a one-year observation period from March 2017 to March 2018. We implemented an automatic detection system using image analysis to classify the various types of lightning whistlers according to the Arase whistler map. We successfully detected a large number of whistler traces induced by lightning strikes and recorded their corresponding times and frequencies. The various shapes of the lightning whistlers indicate different very-low-frequency propagations and provide important clues concerning the geospace electron density profile.

The recognition algorithm of the lightning whistler wave, based on intelligent speech, is the key technology to break the bottleneck of massive data and study the temporal and spatial variation rules of the lightning whistler wave. However, its recognition effect depends on the hyperparameters determined by manual experiments repeatedly, which takes a great deal of time and cannot guarantee the best recognition effect of the model. Therefore, we proposed the lightning whistler wave recognition algorithm based on grey wolf optimization (GWO). In this paper, the GWO algorithm is used to automatically find the best value of hyperparameters of Long Short-Term Memory (LSTM) in their limited searching space. Here we consider the number of hidden units (hu) and learning rate (lr) as the hyperparameters to be optimized, and the spatial coordinate (hu, lr) as the grey wolf position. By the end of the GWO process, we obtain the position of the wolf king α with the optimal hu and lr searched by the GWO algorithm. Then we use the optimal hu and lr to configure LSTM and perform supervised learning on the train set to obtain the final lightning whistler wave speech recognition model. Through experimental verification, the recognition model based on the GWO not only overcomes the uncertainty of the traditional model relying on manual finetuning of parameters and realizes the mechanism of automatic search and acquisition of hyperparameters, but also its recognition effect improves by about 2% in accuracy, F1score, and other metrics compared with the model trained by manually setting hyperparameters.