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We report our analysis of ionospheric disturbances from the 15 January 2022 Tonga volcano eruption, using GPS data from the International GNSS Service network and ionosonde data in the Australian sector. Wave fluctuations with amplitudes of ∼1 TECU and altitude variations of ∼100 km were observed in the GPS and ionosonde data, respectively. In near‐field region around Tonga shortly after the eruption, our analysis reveals that the ionospheric disturbances had an azimuthally anisotropic velocity profile, with a peculiar minimum in southwestward direction. Close resemblance is identified between the velocity profile of near‐field ionospheric disturbances and the Tonga tsunami, suggesting a coupling between water and atmospheric waves. In far‐field, the disturbances propagated at ∼300 m/s, circling the globe for at least three days and possibly until 21 January 2022, in agreement with several previous reports of the event. Arrival times of ionospheric disturbances observed by GPS receivers and ionosondes provide consistent picture. Plain Language Summary Massive eruption of the Hunga‐Tonga volcano on 15 January 2022 generated tsunami waves in the ocean and shock waves in the Earth's atmosphere. The shock waves from the eruption also propagated upward into space and reached the Earth's ionosphere, creating some ripples. We detected these ionospheric ripples with the help of radio frequency signals transmitted from ground stations and from GPS satellites. The scientific measurement data were recorded by ionosonde stations around Australia, and by network of GPS receiver stations that are distributed internationally. While several previous research works have examined the global nature of the Tonga disturbances, here we contrasted the disturbance profiles in both near‐field and far‐field. Far away from the Tonga volcano, the ionospheric disturbances propagated uniformly with velocity close to 300 m/s in all directions around the globe. This pattern is consistent with previous reports. Near the Tonga volcano, however, these ionospheric ripples spread out unevenly, showing complex patterns whereby the wave speed varied with direction. The uneven ripple patterns in the ionosphere around Tonga were found to be correlated with the uneven tsunami wavefront within the Tonga basin. Key Points We investigated near‐field and far‐field traveling ionospheric disturbance (TID) from the 15 January 2022 Tonga volcano eruption using GPS total electron content and ionosondes TID velocity profile in near‐field around Tonga shows directional asymmetry, likely connected to the anisotropy of tsunami waves In far‐field, TID velocity profile isotropizes in all directions, approaching 300 m/s Lamb wave speed reported previously by others

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication and technology (NICT) has changed several times in the past, resulting in large differences in h'Es before and after the change. We propose a simple method to calibrate h'Es. We used the data of ionosonde observations at four stations, i.e., Wakkanai (45.16°N, 141.75°E), Tokyo (35.71°N, 139.49°E), Yamagawa (31.20°N, 130.62°E), Okinawa (26.68°N, 128.15°E) to calibrate the latest ionosondes VIPIR2, which were installed in May 2017. We carried out the analysis by applying the double-reflection method to the original ionogram images between 2017 and 2021. By developing an automated image detecting algorithm, we were able to process a large amount of data and achieve a calibration with high accuracy. As a result, it was found that the current VIPIR2 data had an offset of 26–28 km. Graphical abstract

We report thermospheric exospheric temperature and composition responses on the 15 January 2022 Tonga volcanic eruption. The temperature and composition profiles are inversed from three ionosonde (MHJ45, EG931, FF051) observed electron density profiles (∼150–200 km) using our new method (Li, Ren, et al., 2023, https://doi.org/10.1029/2022ja030988). The retrieved exospheric temperatures all showed obvious eruption‐induced perturbations, with maximum disturbance magnitude of ∼200 K at MHJ45 and ∼100 K at EG931 and FF051. The temperature variations were related to eruption‐excited thermospheric waves and their propagation with different speeds. While column ∑O/N2 had no evident changes similar to temperatures, which were basically consistent with GOLD observations. In comparison, higher thermospheric O/N2 has larger eruption‐related changes, maybe due to the exponential increase of thermospheric wave amplitudes with height. The application of our inversion method, combined with continuous observations and global coverage of ionosonde data, provide a possibility to further investigate thermospheric responses to different geophysical conditions. Plain Language Summary Extreme volcanic eruptions and resulted tsunami at 04:14:45 UT on 15 January 2022 generated a series of atmospheric waves, which can propagate out globally and up into the thermosphere. The ionosphere responses on this eruption, relative to thermosphere, have been reported a lot due to the large amounts of ionospheric observations. Here, we used the new method proposed by Li, Ren, et al. (2023), https://doi.org/10.1029/2022ja030988 to inverse daytime thermospheric parameters (neutral temperature and composition) from ionospheric electron density profiles (∼150–200 km). We selected ionosonde data at three stations (MHJ45, EG931, FF051) to verify the thermospheric responses during this eruption. The retrieved temperature at three stations showed the obvious eruption‐induced perturbations, but ∑O/N2 not, which were basically consistent with GOLD observations. However, O/N2 in higher thermosphere had larger eruption‐related changes. The comparison with GOLD observations and observed F2 layer peak electron densities verified the credibility of our inversion method again. Thus, the application of the method to the continuous and high‐covering ionosonde data provides a possibility to further investigate thermospheric responses to different geophysical conditions. Key Points Inversed exospheric temperatures showed obvious eruption‐induced perturbations on the 15 January 2022 Tonga eruption ∑O/N2 had no evident eruption‐induced changes similar to the temperature, neither in our inversion data nor in GOLD observations Ionosonde can expand the understanding of thermospheric responses to different geophysical conditions by our inversion method

We used ionosonde and GPS receivers during March-April in 2004-2005 and 2011-2015 to investigate the latitudinal variation of equatorial plasma bubble (EPB) occurrence rate in cases of strong and weak pre-reversal enhancement (PRE). The ionosonde at Chumphon in Thailand was used to estimate the PRE strength. Ten GPS receivers in Southeast Asia, ranging from magnetic latitude (ML) of 4.4°S to 21.6°S, were used to investigate the latitudinal variation of EPB occurrence rate. In the case of strong PRE, the EPB occurrence rates decrease from 38.9% to 34.9% at ML of 4.4°S-7.2°S. Continuously, the occurrence rate increases and reaches the peak (44%) at ML of 9.3°S; afterward, the occurrence rate rapidly decreases and reaches below 5% at ML of 21.6°S. In the case of weak PRE, the occurrence rate decreases from 21.8% at ML of 4.4°S, seems constant (15.3%-16%) at ML of 8.2°S-12.1°S, and reaches less than 5% at ML of 16.1°S. Generally, the EPB occurrence rate and its latitudinal extension in case of strong PRE are higher than that in case of weak PRE. Interestingly, we found that the latitudinal occurrence rate peak of the EPB in case of strong is farther than that in case of weak PRE.

The height of F2 peak (hmF2) is an essential ionospheric parameter and its variations can reflect both the earth magnetic and solar activities. Therefore, reliable prediction of hmF2 is important for the study of space, such as solar wind and extreme weather events. However, most current models are unable to forecast the variation of the ionosphere effectively since real-time measurements are required as model inputs. In this study, a new Australian regional hmF2 forecast model was developed by using ionosonde measurements and the bidirectional Long Short-Term Memory (bi-LSTM) method. The hmF2 value in the next hour can be predicted using the data from the past five hours at the same location. The inputs chosen from a location of interest include month of the year, local time (LT), K p , F 10 . 7 and hmF2 as an independent variable vector. The independent variable vectors in the immediate past five hours are considered as an independent variable set, which is used as an input of the new Australian regional hmF2 forecast model developed for the prediction of hmF2 in the hour to come. The performance of the new model developed is evaluated by comparing with those from other popular models, such as the AMTB, Shubin, ANN and LSTM models. Results showed that: (1) the new model can substantially outperform all the other four models. (2) Compared to the LSTM model, the new model is proven to be more robust and rapidly convergent. The mew model also outperforms that of the ANN model by around 30%. (3) the minimum sample number for the bi-LSTM method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000). (4) Compared to the Shubin model, the bi-LSTM method can effectively forecast the hmF2 values up to 5 h. This research is a first attempt at using the deep learning-based method for the application of the ionospheric prediction.

The height of the peak electron density (hmF2) and the critical frequency of the F2 layer (foF2) are very important in the research of ionospheric electrodynamics and high frequency (HF) wireless communication. In the article, we validated the hmF2/foF2 model values of the latest version of the International Reference Ionosphere (IRI-2016) with observations from three ionosonde stations which belong to low, middle, and high latitudes (i.e., Sanya, Beijing and Mohe) over China during a high solar activity year (2014, F10.7 = 145.9 sfu) and a low solar activity year (2016, F10.7 = 88.7 sfu). Among them, foF2 model values can be obtained through the International Radio Consulting Committee (CCIR) model or the International Union of Radio Science (URSI) model, both of which have the “F-peak storm model” on or ‘off’ options; hmF2 model values can be obtained through Bilitza-Sheikh-Eyfrig (BSE-1979), Altadill-Magdaleno-Torta-Blanch (AMTB-2013), or SHUbin (SHU-2015) model. The IRI-2016 hmF2/foF2 model values were evaluated by root mean square (RMS) values and mean absolute relative error (MARE). The results show that for the foF2 parameter, the performance of IRI-2016 can be improved by choosing “F-peak storm model” on option in geomagnetic-disturbed days. Whether in high or low solar activity years, for foF2, the IRI-2016 options of CCIR have better prediction ability than IRI-2016 options of URSI in low and high latitudes over China, and the IRI-2016 options of URSI have better prediction ability than IRI-2016 options of URSI in middle latitudes. For hmF2, the IRI-2016 option of SHU-2015 has better prediction ability than the IRI-2016 options of AMTB-2013 and BSE-1949 in high latitudes over China, the IRI-2016 options of SHU-2015 and BSE-1979 have better prediction ability than IRI-2016 options of AMTB-2013 in mid and low latitudes over China.

Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site and then combined to determine the most accurate GPS-RO technique for two thresholds on sporadic-E intensity: no lower limit and fbEs ≥3 MHz. Overall, the YuS4 method shows the closest agreement with ionosonde measurements for total Es occurrence rates without a lower limit on intensity, while the phase-based Chu technique shows the closest agreement for fbEs ≥3 MHz. This analysis demonstrates that the variation in GPS-RO derived sporadic-E occurrence rates is due to varying thresholds on the sporadic-E intensities in terms of fbEs.

A novel method was developed based on the amplitude data of the EM waves measured by Digisondes to calculate and investigate the relative ionospheric absorption changes. The effect of 13 solar flares (>C8) that occurred from 4 to 10 September 2017 were studied at three European Digisonde stations (Juliusruh (54.63°N, 13.37°E), Průhonice (49.98°N, 14.55°E) and San Vito (40.6°N, 17.8°E)). The present study compares the results of the amplitude method with the absorption changes measured by the Finnish Riometer Network and determined by the NOAA D-RAP model during the same events. The X-class flares caused 1.5–2.5 dB of attenuation at 30–32.5 MHz based on the riometer data, while the absorption changes were between 10 and 15 dB in the 2.5–4.5 MHz frequency range according to the amplitude data. The impact caused by energetic particles after the solar flares are clearly seen in the riometer data, while among the Digisonde stations it can be observed only at Juliusruh in some certain cases. Comparing the results of the amplitude method with the D-RAP model it seems evident that the observed absorption values almost always exceed the values given by the model both at 2.5 MHz and at 4 MHz during the investigated period. According to the comparison between the riometer data with the D-RAP, generally, the model underestimates the absorption values obtained from the riometers during solar flares except at the highest latitude stations, while D-RAP overestimates the impact during the particle events.

The occurrence of equatorial spread F (ESF) has the potential to detrimentally impact space-based technological systems. This study investigates the utility of ionosondes in forecasting the incidence of post-sunset ESF in the zonal direction, utilizing observational data obtained from four ionosondes located near the magnetic Equator in Southeast Asia. Data were collected during the equinox seasons (March–April and September–October) between 2003 and 2020. To establish a relationship between the probability of post-sunset ESF occurrence and the evening vertical plasma drift (v), a logistic regression model was employed. Post-sunset ESF occurrence is defined as the presence of ESF during the time window between 19:00 and 21:00 LT, while v is derived from the average time derivative of virtual heights during the interval from 18:30 to 19:00 LT. Results indicate that the probability of post-sunset ESF occurrence approaches zero, signifying that ESF is unlikely to develop when v is negative. Conversely, when v exceeds 30 m/s, the probability of post-sunset ESF occurrence surpasses 0.87, indicating that ESF occurs almost invariably. The likelihood of post-sunset ESF occurrence reaches 1 when v equals or exceeds 40 m/s. Utilizing this model, the study determined that a single ionosonde positioned at the Equator can effectively forecast the incidence of post-sunset ESF up to a longitudinal distance of 30° from its location. The accuracy of ionosondes in predicting post-sunset ESF occurrence above their respective locations is approximately 0.80, with a 10% decrease in accuracy when forecasting ESF occurrence at longitudinal distances of 30°. In conclusion, this study enhances our understanding of the link between the evening vertical plasma drift and the manifestation of post-sunset ESF by leveraging ionosonde data. Furthermore, it provides valuable insights into the recommended coverage range of ionosondes for predicting post-sunset ESF occurrence in the zonal direction, which can be employed to fortify regional space weather services.

A new three-dimensional computerized ionospheric tomography model (VRS-ML-CIT) was developed in this study combining virtual reference station (VRS) and machine learning (ML) technology. Compared to the traditional VRS technology, the fitting and interpolation method generates virtual observations at each epoch. The ML technology was employed in this study to model both temporal and spatial variations of virtual observation in VRSs. Simultaneously, these ML-based VRSs are built associatively with unilluminated voxels (voxels without real observations) rather than relying solely on real reference stations, which can effectively reduce the proportion of unilluminated voxels, ensure the inversion efficiency, and improve the accuracy of virtual observations. We validate VRS-ML-CIT using observations of 153 GPS and two ionosonde stations in Europe. The results show that the test accuracy of the virtual observation is about 0.8 TECU, which offers an improvement of 40% over the previous non-machine learning VRS method. With the addition of virtual observations, the proportion of unilluminated voxels in VRS-ML-CIT declined from 30 to 12% on average. In comparison with the CIT result derived with only real observations (OBS-CIT), the error of the estimated slant total electron content in the proposed method reaches the same level for the illuminated voxels, even exceeding that of OBS-CIT in some periods. Moreover, the latitude–altitude maps and profiles of the IED from unilluminated voxels demonstrate the excellent performance of the proposed method.