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The Martian ionosphere was actively detected by Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) aboard the Mars Express. The detected echo signal of the MARSIS at an epoch is presented as a function of frequency and time delay to form an ionogram. Some MARSIS ionograms have been processed to obtain the electron density profiles of the Martian topside ionosphere. Unfortunately, more than half of the records cannot be processed with current methods due to the lack of local plasma density information at spacecraft altitude. In this work, we employ a piece-wise exponent to describe the electron density profile of the Martian topside ionosphere. The piece-wise exponent used in our method can reasonably capture the altitude structure of the Martian topside ionosphere, which has been validated with the MGS and MAVEN data. In an altitude regime of lower than 200 km, the average absolute height error of the same electron density between MGS data and fitted profiles is 0.006 km, and the average relative error is 0.008%. In an altitude regime of higher than 200 km, the average absolute height error of the same electron density between MGS data and fitted profiles is 0.55 km, and the average relative error is −0.1%. Based on the altitude structure knowledge of the Martian topside ionosphere, we put forward a new method to invert electron density profiles from MARSIS ionograms with/without local plasma density information. Compared with the previous results, the average absolute difference in the peak height of the retrieved profile is 7.38 km, within the margin of the MARSIS height resolution of 13.8 km. The average relative difference is only 3%. The application of the new method can greatly improve the utilization rate of MARSIS ionogram records.

An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is based on spatio‐temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super‐parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread‐F.

Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is still a hot issue. Due to this situation, a frequency selecting method for HF communication based on ionospheric oblique backscatter sounding is proposed in this study. After obtaining the ionograms, pattern recognition is used to separate the vertical echoes and the oblique backscatter echoes. Next, the leading edge of the oblique backscatter echoes are extracted, and then a two-dimensional electron density profile can be reconstructed. Then, with the help of ray tracing, the usable frequency range can be estimated. Finally, according to the signal-to-noise ratio reflected by the ionograms, several optimal communication frequencies can be selected. In order to verify this method, oblique ionograms are obtained through oblique sounding experiments to evaluate its accuracy. The result indicates that the usable frequency range and the selected frequencies are in accordance with the echo of the oblique ionogram, so the practicability and accuracy of the method are validated. Eventually, the maximum usable frequencies (MUFs) obtained from oblique backscatter sounding are compared with the MUFs from the oblique sounding ionogram; its Mean Absolute Percentage Error (MAPE) is 7.8% and its root mean squared error (RMSE) is 1.34 MHz.

F‐region bottom‐type scattering layers (BSLs) occurring over equatorial and low latitudes may act as the precursor of plasma bubbles, usually observed by narrow‐beam very high frequency radars. However, their spatial features remain unknown due to the radar narrow field‐of‐view. Here we report a case of localized BSL not accompanying plasma bubbles firstly observed by an all‐sky radar at low latitude. Based on radar interferometry over a large field‐of‐view, the BSL was revealed to occur over a limited area northeastward of the radar and did not cause scintillation when Global Navigation Satellite System satellites passed through. The BSL vertical displacement precisely followed the F‐layer bottom fluctuation, without obvious horizontal movement. Interestingly, the localized BSL caused weak spread‐F traces in the ionograms indicating irregularities from specific directions, which are distinct from the satellite traces or range spread F related to plasma bubble development thus could serve as a new signature of BSL in future studies.

Satellite Traces (STs) are the important ionogram signatures for the presence of upwellings in the bottom‐side ionosphere, which provide the necessary seed perturbation for the development of equatorial plasma bubbles (EPBs). In this study, a virtual ionosonde experiment is simulated to investigate the various ST signatures under the presence of shallow, deep, overhead, and off‐centered upwellings in the bottom‐side ionosphere. It is shown that STs occur at higher and lower virtual heights than the main ionogram trace for the off‐centered and overhead upwellings, respectively. The height separation between the main trace and STs increases with the deepening of overhead upwellings. Further, a proof‐of‐concept is demonstrated that multiple STs from ionograms can be used to reconstruct the spatial structure of bottom‐side upwellings, if the precise Angle‐of‐Arrival information can be resolved from the wide beam Ionosonde systems, and can have potential applications in predicting the occurrence of EPBs.

An 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.

The most important aim in interpreting an oblique ionogram is to obtain the accurate Junction Frequencies (JFs) of the ordinary (O) and extraordinary (X) mode. This requires the correct identification of O- and X-mode traces, so it is very helpful and worthy to grasp the relative position between the two modes. This paper presents a statistical and simulation study of the separation in JFs between O- and X-waves based on observed oblique ionograms over three mid-latitude paths within China and a 3D ray-tracing program. The dependence on local time, season, geomagnetic activity, O-wave JF and group path, solar activity, direction, and length of propagation is investigated. The main conclusions are as follows: (a) the separation on east–west path is susceptible to ionospheric variability, while the separation on north–south path does not show a significant correlation with local time and season; (b) a general diurnal tendency and a summer anomaly on east–west propagation are first proposed and discussed, which may be related to the diurnal variation of hmF2 above the reflection point and the strong lower layers below the reflection point; (c) the separation varies approximately as a cosine function with the propagation direction owning two maxima in the north–south direction and two minima in the east–west direction; (d) the variation patterns of the separation with the propagation length are obviously not the same in different directions. In the case of east–west propagation, the separation decreases to a minimum near ground range of 2000 km and then increases very slowly with increasing ground range, while it monotonically increases for the north–south propagation path.

A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

The description and test results of the prototype of a fast ionosonde for the vertical sounding of the ionosphere, which makes it possible to record ionograms once a second, are presented. Such a high rate of registration of ionograms is required to study the fast processes of redistribution of electron concentration during heating experiments, for registration of fast quasiperiodic and moving ionospheric disturbances in the F, E, and Es layers. The key feature of the presented development is the usage of publicly available radio-electronic components. This provided a significant reduction in the cost of creating the prototype. In the current version, the prototype is based on the software-defined radio (SDR) devices Red Pitaya SDRlab 122-16 and LimeSDR. The test results showed that the quality of the ionograms recorded using the prototype is not worse than the quality of ionograms recorded using the professional CADI ionosonde. The low cost of the components allows providing multi-position registration of ionograms for determination the dynamics of natural and artificial ionospheric disturbances in 3D region of space at a lower expenses rate, as well as to create a network of ionospheric observation points with an increased number of ionosondes.

In this study, a method is proposed to carry out automatic inversion of oblique ionograms to extract the parameters and electron density profile of the ionosphere. The proposed method adopts the quasi-parabolic segments (QPS) model to represent the ionosphere. Firstly, numerous candidate electron density profiles and corresponding vertical traces were, respectively, calculated and synthesized by adjusting the parameters of the QPS model. Then, the candidate vertical traces were transformed to oblique traces by the secant theorem and Martyn’s equivalent path theorem. On the other hand, image processing technology and characteristics of oblique echoes were adopted to automatically scale the key parameters (the maximum observable frequency and minimum group path, etc.) from oblique ionograms. The synthesized oblique traces, whose parameters were close to autoscaled parameters, were selected as the candidate traces to produce a correlation with measured oblique ionograms. Lastly, the proposed algorithm searched the best-fit synthesized oblique trace by comparing the synthesized traces with oblique ionograms. To test its feasibility, oblique ionograms were automatically scaled by the proposed method and these autoscaled parameters were compared with manual scaling results. The preliminary results show that the accuracy of autoscaled maximum observable frequency and minimum group path of the ordinary trace of the F2 layer is, respectively, about 91.98% and 86.41%, which might be accurate enough for space weather specifications. It inspires us to improve the proposed method in future studies.