Explore the vast range of titles available.

A new observational phenomenon, named Simultaneous Global Ionospheric Density Disturbance (SGD), is identified in GNSS total electron content (TEC) data during periods of three typical geospace disturbances: a Coronal Mass Ejection‐driven severe disturbance event, a high‐speed stream event, and a minor disturbance day with a maximum Kp of 4. SGDs occur frequently on dayside and dawn sectors, with a ∼1% TEC increase. Notably, SGDs can occur under minor solar‐geomagnetic disturbances. SGDs are likely caused by penetration electric fields (PEFs) of solar‐geomagnetic origin, as they are associated with Bz southward, increased auroral AL/AU, and solar wind pressure enhancements. These findings offer new insights into the nature of PEFs and their ionospheric impact while confirming some key earlier results obtained through alternative methods. Importantly, the accessibility of extensive GNSS networks, with at least 6,000 globally distributed receivers for ionospheric research, means that rich PEF information can be acquired, offering researchers numerous opportunities to investigate geospace electrodynamics. Plain Language Summary Electric fields of solar wind and geomagnetic disturbance origin can penetrate into the low latitude upper atmosphere, influencing the ionospheric dynamics and electron density variations. This study employs a new method that utilizes global and continuous GNSS total electron content (TEC) observations to investigate the electric field effects. The analysis focuses on three geospace disturbance events of different intensities and solar‐terrestrial conditions. The study identifies a novel phenomenon named Simultaneous Global Ionospheric Density Disturbance (SGD), primarily occurring on the sunlit portion of the Earth's ionosphere and also near dawn hours with 1% or larger amplitudes of the background TEC, or a few tenths of a TEC unit (1016 m3). The remarkable global extent of ionospheric responses to minor solar‐geomagnetic conditions is noteworthy. The solar wind magnetic field directed southward is highly correlated with most SGDs, lasting for up to 30 min. The findings present an effective approach for continuously monitoring electric field penetration and ionospheric impacts, leading to an improved understanding of space weather and its technological implications. Key Points Simultaneous global ionospheric disturbances (SGDs) are often observed even during minor solar and geomagnetic disturbances SGDs occur predominately on dayside and are related to penetration electric fields (PEFs) of solar wind and geomagnetic disturbance origin Global GNSS networks offer a novel and effective technique for continuous PEF monitoring, providing rich data sets for further study

We investigated, for the first time, the impact of the disappearing solar wind (DSW) event [26–28 December 2022] on the deep nightside ionospheric species using MAVEN data sets. An enhanced plasma density has been observed in the Martian nightside ionosphere during extreme low solar wind density and pressure periods. At a given altitude, the electron density surged by ∼2.5 times, while for ions (NO+, O2+, CO2+, C+, N+, O+, and OH+), it enhanced by > 10 times, respectively, compared to their typical average quiet‐time periods. This investigation suggests that an upward ionospheric expansion likely took place in a direct consequence to the contrasting low dynamic/magnetic pressure and relatively higher nightside ionospheric pressure (by 1–2 orders) causing an increased ionospheric density. Moreover, the day‐to‐night plasma transport may also be a contributing factor to the increased plasma density. Thus, this study offers a new insight about planetary atmosphere/ionosphere during extreme quiescent solar wind periods.

When extreme wind pressure is predicted based on the extreme value distribution theory, the sampling time–distance and sample volume of wind pressure data are important influencing factors. To discuss and analyze the influence of time–distance and sample volume on the prediction results and accuracy of extreme wind pressure, according to the wind tunnel pressure test results of low buildings, the extreme wind pressure results corresponding to different working conditions of time–distance and sample volume are calculated, and the prediction results are compared and analyzed. At the same time, the wind pressure conversion ratio variable is introduced to analyze the prediction accuracy of extreme wind pressure. According to the calculation results of the wind pressure conversion ratio under different working conditions, the empirical calculation formula of wind pressure conversion ratio concerning time–distance and sample volume is further established, and the applicability of the empirical calculation formula is verified and analyzed. The results show that a reasonable increase in time–distance and sample volume can effectively improve the prediction accuracy of extreme wind pressure. Meanwhile, the empirical calculation formula of the wind pressure conversion ratio provides a method for quantitative analysis of the impact of time–distance and sample volume change on the prediction accuracy of extreme wind pressure, and it is also a theoretical reference for the uncertainty analysis of extreme wind pressure.

We examine coupling of fluctuations in the solar wind with electromagnetic ion cyclotron (EMIC) waves in the magnetosphere using an advanced full‐wave simulation code, Petra‐M. Dipole tilt dramatically affects the coupling process. While very little wave power can reach the inner magnetosphere without tilt effects, a tilted dipole field dramatically increases the efficiency of the coupling process. Solar wind fluctuations incident at high magnetic latitude effectively reaches the ground along the field line and mode‐convert to linearly polarized field‐aligned propagating waves at the Alfvén and IIH resonances. Therefore, solar wind compressions efficiently drive linearly polarized EMIC waves when the dipole angle is tilted toward or away from the Sun‐Earth direction. Plain Language Summary The solar wind is emitted near the ecliptic planes, and solar wind pressure is one of the critical sources of wave activities in the Earth's magnetosphere. Since Earth's magnetic field is tilted to the ecliptic plane, compressed solar wind fluctuations can be incident over a wide range of magnetic latitudes depending on seasonal and diurnal variations. When the solar wind fluctuations reach Earth's magnetosphere, incoming wave energy can be linearly transferred to another wave mode, called a mode coupling. This paper examines the role of Earth's magnetic tilt on this mode coupling between solar wind fluctuation and electromagnetic ion cyclotron (EMIC) waves using an advanced full‐wave simulation code, Petra‐M. Without magnetic tilt, solar wind fluctuations incident at a low latitude are almost totally reflected. In contrast, solar wind fluctuations incident at high latitude can propagate efficiently into the inner magnetosphere and reach the ground. Compressional fluctuations can also be converted to linearly polarized, field‐aligned propagating waves. These results suggest that solar wind compressions can drive the linearly polarized EMIC waves and that the wave occurrence can have seasonal and diurnal dependence, which is expected to maximize at the maximum tilt of Earth's dipole into the Sun‐Earth direction. Key Points Wave mode coupling of solar wind fluctuation to electromagnetic ion cyclotron (EMIC) waves is examined using an advanced 2D‐full wave simulation code, Petra‐M When the dipole magnetic field is tilted, compressional waves can reach the ground near the cusp region via mode conversion The solar wind can drive linearly polarized EMIC waves via mode conversion more efficiently when the dipole field is tilted

The structure of parabolic condensers makes them susceptible to wind load because of their thin and large windward mirrors. In this paper, the wind pressure on a model of a condenser mirror (1:35) on multistorey flat roofs is analysed via pressure measurement in a wind tunnel. The mean wind-pressure distribution law of flat-roof condenser mirrors (including the change law with working conditions and the maximum distribution characteristics) and the distribution law of fluctuating and extreme wind pressure are obtained. Furthermore, by comparison with the ground-based condenser distribution law, similarities and differences between the two are obtained. Research results show that the wind-pressure distribution law of flat-roof parabolic condenser mirrors is the same as those on the ground, but the mean wind-pressure coefficient (absolute value) is generally ~30% smaller. Furthermore, the maximum effect is generally located at the windward mirror edge and the mirror is more susceptible to wind pressure in wind directions of 30° and 135°–150°. The results of this study can provide a theoretical reference for wind-resistant structure design and multistorey flat-roof condenser-related research.

Timber lounge bridges are architecturally significant ancient bridges in China. They have unique shapes and gentle structures that are particularly vulnerable to wind loads. Owing to their uniqueness, the wind pressure distribution characteristics of these bridges differ from those of ordinary buildings or bridges. Therefore, to explore these characteristics, refined, scaled, and rigid models of beam- and arch-type lounge bridges were produced using 3D printing technology. Wind pressure tests in wind tunnels were conducted for different bridge span structures and open states of lounge houses. The results showed that in most cases, the influence of the bridge span structure on the wind pressure on the lounge bridge roof was negligible. However, for a fully closed lounge house, the presence of weatherboards in the bridge span structure significantly increased the negative wind pressure on the roof. The difference in the wind pressure distribution among the three states—fully open, semi-open, and fully closed—was evident. Based on the findings, removing the upper weatherboards on the bridge house and the weatherboards for arches during high wind speeds, effectively change the direction of forces on the roof, thereby lowering the possibility of the roof being blown upwards.

Knowledge of wind effects is of great significance in structural, environmental, and architectural fields, where excessive relevance among wind pressure, building load, and natural ventilation has been formerly confirmed. Within the scope of high-rise buildings, functions of their layout, separation and height in altering wind pressure have been inquired on purpose, while a few investigations in relation to impacts of plane dimensions have been explored. This study consequently intends to ascertain wind pressure distributions on and around various squared-shaped tall buildings by the application of Computational Fluid Dynamics techniques. To start with, models established by the Common Advisory Aeronautical Research Council (CAARC) were simulated, for the purpose of correctness comparison, and reliability verification. Hereafter, wind pressure distributing on buildings was predicted under two scenarios, namely height-width (HW) and height-thickness (HT). Results evidenced that both HW ratio and HT ratio exerted great influence on wind characteristics of buildings. Positive pressure on building surface generally varied greatly, where a narrower windward tended to suffer higher wind pressures, while a larger one was corresponding to severer negative wind effects. The thickness played little influence on altering positive wind pressure. Prominently, pressure distributed on leeward surfaces showed great differences, whereas wind effects on leeward and side surface were strengthened. Likewise, both positive and negative effects around buildings were magnified by larger widths, while negative effects became feeble along the increasing building thickness.

We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct the electron flux variation during 19 enhancement and 7 depletion events and demonstrate the feature attribution analysis called SHAP (SHapley Additive exPlanations) on the superposed epoch results for the first time. We find that the intensity and duration of the substorm sequence following an initial dropout determine the overall enhancement or depletion of electron fluxes, while the solar wind pressure drives the initial dropout in both types of events. Further statistical results from a data set with 71 events confirm this and show a significant correlation between the resulting flux levels and the average AL index, indicating that the observed “depletion” event can be more accurately described as a “non‐enhancement” event. Our novel SHAP‐Enhanced Superposed Epoch Analysis (SHESEA) method can offer insight in various physical systems. Plain Language Summary This study examines the responses of relativistic electrons in Earth's radiation belt to various solar wind and geomagnetic disturbances, identifying key influencing factors. We first adopt an explainable machine learning method to understand the importance of different features during 19 enhancement and 7 depletion events. Our results directly reveal that an increase in solar wind dynamic pressure contributes to a sudden decrease in electron fluxes. Additionally, we find that the strength and duration of subsequent substorms determine whether the electron flux increases or decreases. Guided by the importance of these features as determined by our machine learning model, we carry out a statistical analysis, showing a significant correlation between the flux level and the average AL index. Our method offers advantages over traditional superposed epoch analysis since it directly shows the determining factors. Key Points We use a machine learning feature attribution method to identify key drivers in radiation belt enhancement and depletion events The electron flux depletion, loss, or enhancement is driven by the competition between solar wind Psw and cumulative strength of substorms The average AL index following the pressure maximum has a significant correlation with the resulting flux level

This research simulates the behavior of a tornado on a double-slope roof using the Ward tornado generator and the turbulence model. The effects of different ground roughness, slope angle, and wind field position on the tornado load characteristics of gable roofs were studied. The tornado-generating device established the tornado field under various working conditions, and the simulation results were compared with the experimental data to verify the reliability of the simulation results. The wind pressure distribution of gable roofs with four different slope angles was analyzed to find the most unfavorable roof condition of the tornado field. The gable roof’s aerodynamic and wind pressure characteristics at various places in the tornado field were explored by comparing the wind pressure coefficients at five distinct positions on smooth and rough ground. The lift-drag and wind pressure coefficients of five kinds of ground roughness were calculated to determine the influence of different ground roughness on the aerodynamic force and partial pressure distribution of the gable roof. The ground roughness reduces the vortex ratio because the ground roughness reduces the maximum tangential wind speed and the radius of the vortex core. Therefore, the gable roof’s suction increases as the updraft increases.

Understanding and forecasting outer radiation belt electron flux dropouts is one of the top concerns in space physics. By constructing Support Vector Machine (SVM) models to predict storm‐time dropouts for both relativistic and ultra‐relativistic electrons over L = 4.0–6.0, we investigate the nonlinear correlations between various driving factors (model inputs) and dropouts (model output) and rank their relative importance. Only time series of geomagnetic indices and solar wind parameters are adopted as model inputs. A comparison of the performance of the SVM models that uses only one driving factor at a time enables us to identify the most informative parameter and its optimal length of time history. Its accuracy and the ability to correctly predict dropouts identifies the SYM‐H index as the governing factor at L = 4.0–4.5, while solar wind parameters dominate the dropouts at higher L‐shells (L = 6.0). Our SVM model also gives good prediction of dropouts during completely out‐of‐sample storms. Plain Language Summary The outer belt relativistic and ultra‐relativistic electrons, also known as “killer” electrons due to their deleterious effects on satellites, can exhibit fast and significant losses (also called dropouts), which can result from the combined effects of various physical processes. This study aims to identify the critical driving factors controlling dropouts using a machine‐learning approach, which enables us to extract physical insights by isolating different drivers, and ranking their importance by comparing the model performance. Our study adopts a unique way to relate the inputs to dropouts in a nonlinear way compared to the traditional statistical method. We construct Support Vector Machine models using a time series of geomagnetic indices and solar wind parameters as inputs to predict storm‐time dropouts based on 5‐year Van Allen Probes observations. Our results demonstrate that the SYM‐H index is the most informative input at L = 4.0–4.5, suggesting the dominant effects of the ring current in the inner magnetosphere. Solar wind pressure and density are regarded as the governing factor at L = 6.0, indicating the important impacts of solar wind drivers at higher L‐shells. Our SVM models give good predictions of dropouts during completely unseen storms, which are crucial for the understanding and forecasting of outer belt electron flux dropouts. Key Points We investigate the critical driving factors controlling dropouts by constructing dropout prediction models using Support Vector Machines (SVMs) The most informative (critical) inputs controlling dropouts are SYM‐H at L ≤ 4.5 and solar wind drivers at L = 6.0 with mixed impact in between Our ultimate best SVM models can capture the observed relativistic and ultra‐relativistic dropouts during completely unseen storm events