Equatorial Plasma Bubbles (EPBs) are large-scale depletion structures characterized by significantly reduced electron density, which frequently emerge in the low-latitude ionosphere during post-sunset hours. These dynamic plasma irregularities play a crucial role in space weather phenomena, as their evolution can induce severe amplitude and phase scintillations in radio signals, leading to disruptions in satellite communications, global navigation systems, and radar operations. Given their substantial impact on technological systems, accurate prediction of EPB evolution has become a critical challenge in both space physics research and operational space weather forecasting. To address this challenge, this study introduces a novel data-driven approach for EPB evolution prediction by leveraging the SimVP (Simpler yet Better Video Prediction) framework, an advanced deep learning architecture designed for spatiotemporal sequence forecasting. The proposed model learns the complex nonlinear dynamics of EPB structures from historical airglow image sequences, capturing both their morphological transformations and drift patterns. Through extensive experimentation, we systematically evaluate the influence of key parameters—including time resolution, input/output sequence length, and environmental noise—on prediction performance. Our findings demonstrate that an optimal configuration with a 3 min temporal resolution and a 6-frame input/output structure achieves superior predictive accuracy, as evidenced by high Structural Similarity (SSIM=0.989) and Peak Signal-to-Noise Ratio (PSNR=34.704) metrics. Further analysis reveals that the spatial complexity of EPB structures, such as bifurcation events and irregular boundary deformations, significantly affects prediction fidelity, whereas the impact of light pollution—a common issue in ground-based airglow observations—is comparatively minor. The model proposed in this paper demonstrates robust cross-station applicability. Beyond forecasting, the model also exhibits potential for reconstructing corrupted airglow data, offering a computational solution to enhance observational datasets affected by atmospheric or instrumental noise. This work not only establishes a robust, machine learning-based tool for EPB evolution analysis but also contributes to the broader development of Artificial Intelligence (AI) applications in space weather modeling and ionospheric research.

The responses of thermospheric winds at middle latitudes to the moderate geomagnetic storm of 18－19 March 2018, are examined using two ground-based Fabry-Perot Interferometer (FPI) observations from the Xinglong (XLON, 40.2°N, 117.6°E; magnetic latitude 35°N) and the Sutherland Astronomical Observatory (SAAO, 32.2°S, 20.48°E; magnetic latitude 40.7°S), combined with simulations from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The storm reached a maximum Kp index of 6, classifying it as a moderate storm. Ground-based FPI measurements provided high-resolution wind data at both stations, capturing the temporal evolution of zonal (east-west) and meridional (north-south) wind components. Meanwhile, the TIEGCM simulations offered a theoretical framework to interpret the observed disturbances and assess the model’s capability in reproducing storm-induced thermospheric dynamics. The results reveal that the response of thermospheric winds to the geomagnetic storm is more pronounced in the southern hemisphere than that in the northern hemisphere. Significant enhancements in equatorward and westward winds are observed at the SAAO station, with maximum meridional wind speeds reaching 128.4 m·s^–1 (equatorward) and maximum zonal wind speeds reaching –165.6 m·s^–1 (westward). Comparative analysis with TIEGCM simulations indicates that the model can reasonably reproduce the disturbance trends in observations, particularly in the variations of meridional winds at SAAO and zonal winds at XLON. The model successfully captured the transition from quiet-time wind patterns to storm-driven disturbances, including the shift toward westward and equatorward. However, certain quantitative discrepancies remain in the model’s predictions: the model underestimates the eastward zonal winds at SAAO and overestimates the equatorward meridional winds at XLON. Future studies could consider using multiple ground-based stations and a variety of observations, such as temperature, density, chemical composition for the study. Furthermore, investigating the role of seasonal and local time effects in modulating hemispheric asymmetries could provide deeper insights into thermospheric storm responses. Overall, this study contributes to a better understanding of the storm impacts on thermospheric winds and hemispheric differences, as well as their potential physical causes.

To compare the propagation characteristics of Large-Scale Traveling Atmospheric Disturbances (LSTAD) and Large-Scale Traveling Ionospheric Disturbances (LSTID) that propagate in tandem in the thermosphere-ionosphere, this paper takes advantage of the CHAMP satellite’s ability to simultaneously observe atmospheric mass density and electron density across latitudes, studying a pair of LSTAD and LSTID events that propagated in tandem over long distances on 19 March 2002. Around 04:00－06:00 UT on 19 March, with a sudden and significant increase in the AE index, the CHAMP satellite observed the LSTAD and LSTID propagating in tandem in the Northern Hemisphere. Over the next approximately 6 h after 04:00 UT, these disturbances in atmospheric mass density and electron density propagated southward, crossed the equator, and entered the Southern Hemisphere, eventually dissipating there. On the other hand, the ground-based GNSS chain observations also confirmed the existence of the LSTID observed by the satellite. Through comparative analysis, it was found that due to the highly controlled movement of electrons by the Lorentz force while neutral particles are not constrained by it, the horizontal propagation speeds of LSTAD and LSTID along the meridian direction show significant differences. Therefore, at the same time and position on the same orbit, their phases are not the same and may even differ significantly.

Polar Mesospheric Clouds (PMCs), as ice crystal clouds formed in the middle and upper atmosphere (approximately 83 km high), have a seasonal onset that serves as an important parameter for studying the coupling processes between thermodynamics and dynamics in the polar mesosphere. Based on multi-source observational data from 1979 to 2023, the long-term evolution characteristics of the onset of PMCs in both hemispheres are systematically analyzed, and their correlations with the reversal time of stratospheric zonal mean wind and solar activity are examined. Results show that there are significant differences in the onset of PMCs between the two hemispheres: the interannual variation (with a standard deviation of 22 d) in the southern hemisphere is about twice that in the northern hemisphere (11 d), which may be related to differences in thermal and dynamic processes such as inter-hemispheric circulation modes and the intensity of gravity wave activity. In the southern hemisphere, the onset of PMCs season exhibits a very strong positive correlation with the reversal time of the stratospheric zonal mean wind, while in the northern hemisphere, although a negative correlation is observed, the approximately 60-day difference does not directly indicate a causal relationship between the two. The regulation of the onset by solar activity (Lyman-α radiation) also shows hemispheric asymmetry. In the northern hemisphere, there was a certain negative correlation with solar activity before 2011 that later weakened due to changes in the stratospheric dynamic background, whereas the southern hemisphere exhibited only a weak response. This indicates that both solar radiation effects and dynamic processes may jointly contribute. In addition, the discrepancies among multi-source data suggest that differences in detection systems and data types can introduce uncertainties in studies of the long-term variation characteristics of PMCs.

The Chang’E-7 lunar mission, scheduled for launch in 2026, has the primary scientific objective of detecting water-ice deposits within the Permanently Shadowed Regions (PSRs) at the lunar south pole. Understanding the distribution and concentration of lunar water ice is crucial for both fundamental science and future In-situ Resource Utilization (ISRU). In this study, we developed a high-fidelity model of the Chang’E-7 Lunar Neutron and Gamma-ray Spectrometer (LNGS) payload using the Geant4 toolkit (Version 10.07.p02) and established a quantitative inversion relationship between lunar surface water content and epithermal neutron count rates. The LNGS model, constructed by importing a detailed CAD model into Geant4, was rigorously validated against neutron beam calibration experiments conducted at the China Spallation Neutron Source (CSNS) Back-n facility. The results are as follows. The detector model shows excellent agreement with experimental data across the 0.4 eV to 1000 eV energy range, with a relative error of less than 6%, confirming the accuracy of the mass modeling and simulation setup. LNGS exhibits significant capability in discriminating soils with varying water content, as evidenced by both simulation and ground-based validation experiments using layered soil and water samples. Within the water-ice content range of 0.01% to 20%, simulations of Galactic Cosmic Ray (GCR) bombardment and subsequent neutron transport show that the epithermal neutron (400～700 keV) count rate decreases significantly with increasing hydrogen abundance. This relationship follows a modified Lawrence model with an exceptional coefficient of determination (R² = 0.9993). The slight parameter differences compared to the original Lawrence model are attributed to the different simulation tools, lunar regolith composition models, and distinct detector designs and energy responses between LNGS and the Lunar Prospector neutron spectrometer. This study provides a robust theoretical framework and a specific, validated inversion model for interpreting Chang’E-7 orbital neutron data, directly enabling the mapping of hydrogen concentrations from measured count rates. It establishes fundamental technical support for the development of in-situ resource utilization technologies on the Moon and paves the way for high-precision assessment of water ice resources in the lunar polar regions.

Accurate observation of sea surface wind fields is essential for tropical cyclone forecasting and meteorological hazard mitigation. The HY-2 series microwave scatterometer continuously measures Ku-band ocean surface winds. However, its current wind speed retrieval algorithm struggles in high wind conditions and systematically underestimates speeds during extreme events such as typhoons. To address this bias, this study utilized the HY-2 wind speed data of nine tropical cyclones between 2021 and 2022 as the data source. The Stepped Frequency Microwave Radiometer (SFMR) wind speed measurements served as the ground truth. A modeling dataset was constructed by resampling the SFMR reference data to match the 25 km spatial resolution of the HY-2 scatterometer, followed by spatiotemporal matching within a two-hour time window. The matched dataset was then randomly divided into a training set and a testing set at a 7∶3 ratio. Subsequently, the Broad Learning System (BLS) was employed to conduct the regression analysis and develop a high-wind-speed correction model. BLS employs a shallow, flat architecture in which input features are expanded into “enhanced nodes”, avoiding the deep stacks typical of conventional neural networks. This structure reduces computational cost and accelerates convergence while maintaining predictive performance. Validation results demonstrate that the corrected HY-2 wind speeds achieved a Root Mean Square Error (RMSE) of 4.47 m·s^–1, representing a 35% improvement compared to the uncorrected data. For wind speeds exceeding 25 m·s^–1, the corrected RMSE reached 6.76 m·s^–1, marking significant enhancements over the original values of 13.27 m·s^–1. Additionally, a comparative analysis using Typhoon Chanthu (in 2021) as a case study revealed that the corrected HY-2C maximum wind speed increased from 22.09 m·s^–1 to 32.73 m·s^–1, closely matching wind fields retrieved by Synthetic Aperture Radar (SAR). Further validation through wind speed profile comparisons confirmed the effectiveness of the proposed model. These results demonstrate that our correction framework markedly improves extreme-wind retrieval accuracy, yielding bias-corrected HY-2 products that are more reliable for applications, such as storm surge simulation and typhoon track forecasting.

As a key indicator of global climate change and an essential freshwater resource, the accurate acquisition of multiple physical parameters of glaciers holds significant importance for global climate change research, ecological conservation, and water resource planning. In China, glaciers are predominantly mountain glaciers distributed in high-altitude regions. Constrained by harsh environments and complex terrain, traditional in-situ detection methods fail to achieve large-scale continuous monitoring of internal glacier parameters. Satellite-borne glacier remote sensing, meanwhile, faces limitations in resolution and interference from complex ground clutter in mountainous glacier regions, and thus has yet to be operationalized. Airborne radar, with its superior spatial resolution and flexible detection capabilities, has become a critical technical tool for glacier monitoring and research. However, airborne detection of mountain glaciers still confronts challenges posed by undulating ice surfaces and complex subglacial topography: scattering clutter from the uneven ice surface interferes with radar signal interpretation and precise inversion of key parameters, while the intricate subglacial structure and scattering losses caused by ice surface topography interact with dielectric losses within the ice, impeding accurate inversion of glacier dielectric constants. To address these challenges, this study integrates airborne ultra-wideband radar detection data from mountain glaciers with the Pseudo-Spectral Time Domain (PSTD) numerical simulation method. A coupled model of ice surface-subglacial dual interface topography and dielectric parameters is established. Through two-dimensional PSTD electromagnetic simulations, the interaction mechanism between topographic scattering and ice dielectric loss is elucidated. Furthermore, an inversion method for the imaginary part of the ice layer dielectric constant in measured regions is proposed based on dynamic range analysis. For the measured data from Laohugou Glacier No. 12, iterative optimization converges the estimated imaginary part value to 6.0×10^–4. The relative error between the estimated imaginary part and the theoretical mean is 21%. Cross-validation between simulation results and theoretical models demonstrates that this method effectively improves the inversion accuracy of glacier dielectric parameters in complex terrain by decoupling the synergistic interference between topographic relief and dielectric parameters, thereby offering a viable solution for studying internal dielectric properties of glaciers.

This review systematically summarizes recent advances in filmwise and dropwise condensation under microgravity, elucidating two-phase heat transfer mechanisms and gravitational influence patterns. For condensing heat transfer in tubes, gravity-independent criterion numbers (Bond number, Froude number, etc.) are used to determine whether gravity affects heat transfer, and the effect of gravity can be attenuated by increasing the mass flow rate of the vapor and reducing the tube diameter. In microgravity environments, dropwise condensation can be achieved through vapor flow acceleration combined with functional surfaces and air-blowing techniques for condensate removal. Current experimental studies on microgravity condensation remain limited due to challenges in obtaining sustained microgravity conditions. The paper emphasizes the necessity of conducting long-term condensation experiments utilizing orbital platforms like the China Space Station and International Space Station to address data reproducibility issues, investigate gravity-dependent mechanisms, and provide theoretical foundations for space two-phase thermal management systems.

The temperature field during solidification has an important influence on the microstructure and properties of the material. Due to the difference of heat convection in space and ground environment, natural convection driven by gravity plays an important role in heat transfer in ground environment. However, in space, the microgravity environment almost eliminates the influence of gravity-dominated natural convection, which will lead to certain differences in the heat transfer characteristics between space and ground, resulting in differences in the temperature field distribution in the material experimental furnace. As a result, the temperature field obtained on the ground is different from that in space under the same temperature control conditions, thus affecting the equivalence of experimental conditions between space and ground materials. The heat transfer characteristics obtained from ground experiments cannot be directly applied to space experiments. This mismatch has a major impact on the space materials experiments. In order to obtain the heat transfer characteristics under microgravity conditions, a three-dimensional numerical model of heat transfer in the high temperature material experimental rack of the space station is established. In the modeling process, reasonable simplification is carried out according to the actual physical conditions, some minor heat transfer factors which have little influence on the overall temperature field are ignored. The temperature field simulation of the ground experiment and the space experiment was carried out respectively, and the temperature distribution of the sample box was obtained. The temperature obtained by simulation was compared with the measured temperature. Through comprehensive analysis of the changes of heat transfer parameters in the space microgravity environment and the normal gravity environment on the ground, the heat transfer law similar to the space condition was obtained. The research results provide a new way to predict the space temperature field distribution based on the ground experiment results of high temperature materials experiment rack and have important guiding significance for the future research of space materials.

Currently, there are relatively few spaceborne methods for detecting near-space atmospheric wind fields, and the Fabry-Perot Interferometer (FPI) is one of the more important and widely used detection techniques. To address the gap in China’s space-based FPI wind sensing capabilities, the National Space Science Center developed a spaceborne FPI wind interferometer. This paper mainly introduces this instrument’s optical design, structural design, thermal control design, optical simulation, and result analysis. First, the optical design is discussed based on the wideband detection requirements, and the imaging system’s image quality is evaluated. Then, based on optical simulation data, wind speed inversion and accuracy analysis of the spaceborne FPI instrument are conducted. The wind speed errors at the 557.7 nm and 762.0 nm bands are –1.722 m·s^–1 and –2.3672 m·s^–1, respectively, indicating that the spaceborne instrument design meets the wind measurement requirements. Then, the key points of the instrument’s structural design and the thermal control solution for the imaging part are presented, along with a translational filter switching device driven by a trapezoidal lead screw and a micro gear stepping motor or micro linear motor. The paper also explores the relationship between the temperature control accuracy of the instrument’s core components (the etalon) and wind measurement errors. A combined active and passive design is adopted to minimize the impact of temperature fluctuations on the results, which is verified with simulation results.