Research on the ionospheric Spread-F (SF) phenomenon holds significant value in both fields as ionospheric electrodynamic research and enhanced operational applications in radio-based technologies (e.g., communication and navigation). To date, the classification of Spread-F remains largely reliant on the manual interpretation of ionograms by experts, suffering from inefficiency (~10 seconds per figure) and subjectivity. There has been no publicly available ionogram dataset classifying Frequency/Range/Mix/Strong Range SF (FSF/RSF/MSF/SSF) by either human labor or machine processing. To address this problem, we introduce the first open, expert-guided ionogram dataset that is simultaneously the most comprehensive in terms of class coverage, the largest in volume, and the most extensive in temporal span. This collection encompasses 150,000 ionograms (30,000 per class, including a “non-SF” group) spanning 14 years from 2002 to 2016, thereby capturing a diverse range of solar and geomagnetic conditions. The attached classification SA-ResNet50 model based on this dataset could be applied to further data.

Thunderstorms could cause the irregularities of electron density distributions in the ionosphere by exciting gravity waves and modifying ambient electric field (E-field). By comparing the DPS-4D ionosonde observation at 5-min resolution at Fuke Station in Hainan, China with the lightning detection data, we studied the F-layer responses to a thunderstorm on 16 August 2016. The results show that the variation in the F-layer electron density corresponded, with similar to 5-min delay, to the time-resolved lightning occurrence; the observed temporal delay likely reflects the E-field modifications associated with charge separation within thunderclouds. After the peak lightning activity, a weak spread-F appeared alongside sudden rises in plasma drift velocities. These features suggest that lightning disruptions affect ionospheric E-fields, drive E & times; B drifts, and cause irregularities in F-layer electron density via Rayleigh-Taylor and E & times; B instabilities. It is the first high-resolution ionosonde observation of thunderstorm-induced F-layer disturbances at low latitudes, providing more insights into the troposphere-ionosphere coupling.

The Digisonde Portable Sounder (DPS) ionosonde at Hainan Station (19.5°N, 109.1°E; magnetic latitude: 11°N) has been monitoring ionospheric conditions since 2002, routinely recording ionospheric plasma profiles, sporadic E layers, and Spread-F structures through ionograms. A Spatial Group-wise Enhanced ConvTransformer (SGE-ConvTransformer) is proposed in this study for spatiotemporal ionospheric prediction at the Hainan station, with emphasis on Spread-F, enabling a one-hour lead time with a 15-minute sampling resolution. The SGE module optimizes semantic feature extraction from the global spatial context, dynamically recalibrating attention to prioritize information-rich regions, such as F-layer traces, over background noise. To further improve visual clarity, a super-resolution Enhanced Deep Super-Resolution (EDSR) module is integrated to sharpen the predicted ionograms. Leveraging DPS ionosonde data from 2002 to 2015, we constructed a large-scale ionogram sequence dataset comprising 36,240 Spread-F instances and 396,931 non-Spread-F instances, which were further categorized into five distinct classes. On the 2016 test set, our model achieved an average Spread-F classification accuracy of 90.05% and a correlation coefficient of 0.8115 for the predicted F-trace. Demonstrating superior robustness under disturbance conditions, the model maintained high performance during six representative geomagnetically disturbed intervals (2023–2024), achieving a classification accuracy of up to 95.69%. Furthermore, the model's generalizability was examined by applying pre-trained weights to data from low-latitude (Brazil, Peru), mid-latitude (Irkutsk), and high-latitude (Zhigansk) stations. Quantitative Spread-F Classification Accuracy (SFCA) metrics at low latitudes and qualitative visual assessments across all regions demonstrate the morphological transferability of our approach across diverse geospatial environments.

Previously we found that the inner radiation belt (IRB) shrinks and stretches in solar minimum and maximum. A natural problem comes up that how solar cycle effects the near-Earth space regions including plasmasphere, IRB, ionosphere, mesosphere and lower thermosphere (MLT). We present a thorough analysis of the extent of solar cycle effect on four regions by using mesospheric and thermospheric geopotential height and temperature from SABER on TIMED, ionospheric hmF2 from Chinese Meridian Project, high-energy protons in IRB and electron density in plasmasphere from Van Allen Probes within 2013-2018 intervals. By analyzing evolutions of these quantities, we find that entire IRB, ionosphere and MLT region shrink at solar minimum and stretch at solar maximum by ~103 km, 50~102 km and 1 km scales, respectively, while plasmapause shows an opposite trend. Fourier spectra of these quantities have been investigated by Lomb–Scargle periodogram. The mid-term periodic oscillations (13.5-day, 45-day, and 52-day) have been observed in MLT region, matching well with plasmapause locations and geomagnetic indices, which have not been observed in solar EUV radiation and IRB. This may indicate that those oscillations facilitate energy exchange and mass transportation between MLT region and plasmasphere due to magnetic storms and substorms. The oscillation periods of higher energy (102.6MeV) in IRB have not been observed in MLT region except for annual variations. The impact of higher energy protons on MLT regions may not be significant, although they could penetrate deeper into MLT region. Our results reveal relationships between some quantities and solar cycle multi-scale modulation, which may provide assistance and monitors for mass transportation in the near-Earth space regions.

Solar tides play significant roles in ionosphere and thermosphere coupling. We present a thorough analysis of solar tides by using ionospheric hmF2 and foF2 data at the Hainan station (19.5 degrees N, 109.1 degrees E) of the Chinese Meridian Project within 2002-2018 intervals. The Lomb-Scargle spectral analysis reveals that, besides the diurnal and semidiurnal tides, the quaterdiurnal (6-hr) tides and quintile-diurnal (4.8-hr) tides are also significant. Moreover, the quintile-diurnal tidal component is slightly stronger than the quaterdiurnal tides. The tides exhibit distinct seasonal dependency. The amplitudes of the four analyzed tides have two maxima with similar intensities: one in March and the other in September. There is a slight difference for the tides' occurrences with two maxima in April and September. The short-term periodic oscillations may also be modulated by geomagnetic activities and the solar cycle. Diurnal tide is closely associated with the solar cycle, which exhibits positive correlations, while high-order harmonic tides (quater- and quintile-) are not significantly modulated by the solar cycle. The amplitudes of tides during the quiet time are less than those during the active period. It is indicated that geomagnetic activity may modulate solar tides.

Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. This estimation is based on the trends of density profiles and the waves/structures presented in the ionogram sequence. The IonoGAN extends the spatiotemporal information‐preserving and perception‐augmented (STIP) ability by incorporating a Local‐Global discriminator to focus on the F region in ionograms. In addition, two scientific characteristics of ionospheric natural phenomena are extracted and used as constraints in the modeling: Spread‐F Classification Accuracy (SFCA) and Absolute Value of the Correlation Coefficient for the F trace (AVCC‐F). For training, ionograms from Hainan Fuke station (19.5°N, 109.1°E, magnetic 11°N) during 2002–2015 were processed into 36,435 sequences with Spread‐F phenomena and 147,147 sequences without. To strengthen their features, Spread‐F phenomena were further classified into types of frequency, range, mix, and strong range. After the parameter training, the IonoGAN achieved SFCA and AVCC‐F converging to their optimal values: on the 2016 test set, SFCA = 90.92%, AVCC‐F = 0.6917. This modification enables the network to effectively capture the distinct features of the ionospheric F trace and the Spread‐F phenomenon during both quiet and disturbed periods.

The solar cycle includes multi-scale variations in the near-Earth space regions including plasmasphere, inner radiation belt (IRB), ionosphere, mesosphere and lower thermosphere (MLT). We present a thorough analysis of the extent of solar cycle effect on those four regions by using mesospheric and thermospheric geopotential height and temperature from SABER on TIMED, ionospheric hmF2 from Chinese Meridian Project, high-energy protons in IRB and electron density in plasmasphere from Van Allen Probes within 2013-2018 intervals. By analyzing evolutions of these quantities, we find that entire IRB, ionosphere and MLT region shrink at solar minimum and stretch at solar maximum by similar to 103, 50-102, and 1 km scales, respectively, while plasmapause shows an opposite trend. Fourier spectra of these quantities have been investigated by Lomb-Scargle periodogram. The mid-term periodic oscillations (13.5-day, 45-day, and 52-day) have been observed in MLT region, matching well with plasmapause locations and geomagnetic indices, which have not been observed in solar EUV radiation and IRB. This may indicate that those oscillations facilitate energy exchange and mass transportation between MLT region and plasmasphere due to magnetic storms and substorms. The oscillation periods of higher energy (102.6 MeV) in IRB have not been observed in MLT region except for annual variations. The impact of higher energy protons on MLT regions may not be significant, although they could penetrate deeper into MLT region. Our results reveal relationships between some quantities and solar cycle multi-scale modulation, which may provide assistance and monitors for mass transportation in the near-Earth space regions.

Based on the Chinese Meridian Project (CMP), the International Meridian Circle Program (IMCP) organizes a comprehensive ground-based monitoring network along the 120°E -60°W Great Meridian Circle to track the propagation and evolution of space weather events.IMCP has more than ten ionosonde along the Meridian Circle at different latitudes to monitor the ionosphere, supporting Chinese researchers in studies on ionospheric statistical characteristic and events.The DPS-4D Ionosonde at Hainan Fuke station (19.5°N, 109.1°E) has been observing the ionosphere for 20 years, with this data we manually classified the SF as 4 types and studied the variation features.Furthermore, with deep learning method, we made a detection (accuracy≥95%) & classification (average accuracy=95%) model and a shortimpending prediction model for the SF in the ionogram over this station.The models are based on image characteristics, no matter what the ionogram data formats or the auto-models in ionosonde.

AbstractAn intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.

Bistatic radar system has attracted lots of studies on earth observation while lacking of data. An indoor bistatic system built in Laboratory of Target Microwave Properties (LAMP) was introduced, and the preliminary bistatic imaging experiments of a metallic cuboid by Inverse SAR (ISAR) was implemented. The characteristics of edge diffraction and specular reflection of electromagnetic wave could be observed clearly on the bistatic images, that highly consistent with the theory of electromagnetic wave. The results demonstrated the capability of this indoor bistatic system, promising to provide more bistatic data for further researches.

On 15 January 2022, the submarine volcano on the southwest Pacific island of Tonga violently erupted. Thus far, the ionospheric oscillation features caused by the volcanic eruption have not been identified. Here, observations from the Super Dual Auroral Radar Network radars and digisondes were employed to analyze ionospheric oscillations in the Northern Hemisphere caused by the volcanic eruption in Tonga. Due to the magnetic field conjugate effect, the ionospheric oscillations were observed much earlier than the arrival of surface air pressure waves, and the maximum negative line‐of‐sight (LOS) velocity of the ionospheric oscillations exceeded 100 m/s in the F layer. After the surface air pressure waves arrived, the maximum LOS velocity in the E layer approached 150 m/s. A maximum upward displacement of 100 km was observed in the ionosphere. This work provides a new perspective for understanding the strong ionospheric oscillation caused by geological hazards observed on Earth.

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Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications.

In the ionogram obtained by sweep frequency radar named ionosonde, the spread echoes in the radio waves indicate the ionospheric plasma irregularities which can scatter, refract and reflect radio wave. The spread echoes are called ionospheric Spread F (SF). SF is considered to have different types: Range SF, Frequency SF, Mixed SF, and Branch SF. Recently some authors, using data from Asian and American continents, found an independent type of spread F named strong range SF (SSF) in low latitude ionosphere, and also found no BSF at low latitude. In this paper, with data from Digisonde at Acsension IS station (7.9°S, 345.6Έ Magnetic 2.3°S) in the low latitude region in Atlantic Ocean in 2004, the occurrence features of SF, especially SSF, were statistically studied. The results showed that there were RSF, SSF, FSF and MSF with occurrences of 5.2%, 23.6%, 26.5% and 44.7%, respectively. The SSF have high occurrence from January to March and from September to December, and have low occurrence from April to August. The SSF accounted for about 30% of all SF in summer and equinoxes, and about 5% of all SF in winter. The SSF mainly occurred at LT 21:00–23:00 in which the ionospheric scintillation just has high occurrence, which suggest that the SSF is associated with Equatorial plasma bubbles. The monthly occurrence variation and the appearance period of SSF were much different from other types of SF. Our results show that the SSF can take place in low latitude ionosphere not only over Asian and American continents, but also over Atlantic Ocean.

A sounding rocket experiment undertaken by the Chinese Meridian Project from a low latitude station on Hainan Island (19.5°N, 109.1°E), China, measured the DC electric field during 05:45–05:52 LT on April 5, 2013. The data observed using a set of electric field double probes, as part of the rocket’s scientific payload, revealed the special profile of how the vectors of the DC electric field vary with altitude between 130 and 190 km. During the experiment, the vertical electric field was downward, and the maximum vertical electric field was nearly 5.1 mV/m near the altitude of 176 km. The zonal electric field was eastward and slightly less than 0.6 mV/m. The plasma drift velocity was estimated from the E×B motion, and the zonal drift velocity was eastward and of the order of 100 m/s. The zonal wind velocity was also estimated using the drift velocity near the maximum density height in the F1-region, and it was found to be nearly 120 m/s. This work constituted the first in situ measurement of the DC electric field conducted within the F1-region (between 130 and 190 km) in the East Asian Sector.

In recent years , Beijing has encountered serious air pollution problem .Atmospheric pollutants diffusion has a great correlation with the wind speed and the underlying surface roughness .Therefore,the 1993—2011 China ground international exchange station wind data are used to analyse the characteristics of the change of wind speed and direction in the ground .The results show that from 1993 to 2011 in Beijing,the average wind speed decreases .Since 2006 wind speed is significantly changed ,the analysis shows that the frequency of north wind decreased may be a mainly reason .On this basis,we use the 2007—2011 ALOS PALSAR and night light data to extract and analyse the land expansion of Beijing .To analyze the relationship between wind speed and direction change of Beijing with surface roughness .The results show that the city expansion causes the increase of roughness ,drags the north wind ,leeds to decreased of wind speed and changes the wind field in Beijing city .

We report an overturning-like structure of the thermospheric sodium layer (TSL) in the altitude region of ∼100–120km observed by a sodium lidar at Haikou (20.0°N), China, on July 29, 2012. The overturning-like sodium layer was first seen as upwelling from the top of the sodium layer (∼102km) to an altitude of ∼118km from 14:55 to 15:50 UT and then descending gradually from its apex with a speed of 3.5km/h. The ionospheric observations from the COSMIC radio occultation and three ionosondes exhibited abrupt perturbations in the radio occultation (RO) SNR profiles and spread Es in the ionograms, respectively, indicating the existence of complex Es around Haikou. On the other hand, VHF radars located at Sanya (18.4°N, 220km away from Haikou) and Fuke (19.5°N, 130km away from Haikou) both recorded strong E region field-aligned irregularity (FAI) echoes altitude-extended structure covering altitudes of 100–140km, which are well correlated with the overturning-like structure of the thermospheric sodium layer. The good agreement between occurrence time of sodium layer (and FAI) structure and of complex Es could indicate a close correlation between them. One possibility is that the chemical reaction in the course of the complex Es (with altitude-deformed structure) could produce sufficient sodium atoms and thus lead to the formation of sodium layer upwelling structure. Correspondingly, FAI altitude-extended structure could be generated through the gradient drift instability in the altitude-extend (deformed) Es which provide plasma density gradients to support the instability development.