High Solar Activity Years Research Articles

Evaluation of the performance of F-layer peak height models used in IRI-2016 over the Indian equatorial and low latitudes

The performance of different height models (BSE-1979, AMTB-2013, and SHU-2015) in the International Reference Ionosphere (IRI-2016) is evaluated over the Indian equatorial and low latitude sectors. The impact of deviations in the modelled hmF2 on the estimation of electron density profiles and the Total Electron Content (TEC) is examined using the three different hmF2 methods in IRI-2016. For this study digisonde observations over Trivandrum (8.5 °N, 76.9 °E) and Ahmedabad (23.0 °N, 72.5 °E) along with GPS observations over Colombo (6.8 °N, 79.8 °E) and Ahmedabad during a high solar activity year 2014 are considered. A comparison of electron density profiles between digisonde and IRI-2016 at both the equatorial station, Trivandrum, and the low latitude station, Ahmedabad, revealed significant deviations with higher deviations in the post sunset hours at the equator. The three height methods have shown significant differences in their predictions of hmF2 at both locations. At the equator, the SHU-2015 method shows better estimates closer to the measurement values, than the other two methods except at Pre-Reversal Enhancement (PRE) time, during which, the AMTB-2013 method shows better performance. At the low latitude station, the newly added methods (SHU-2015 and AMTB-2013) do not indicate any improvements in estimating the hmF2. The availability of three different height methods in IRI-2016 is exploited to understand the modifications if any, in the ionospheric height structure and TEC for different hmF2 methods and the corresponding results are presented. As the variations in hmF2 are used to derive vertical drift on many occasions in the absence of incoherent scatter radar, this study is significant in assessing the relative efficacy of the three height models and hence useful to understand various ionospheric processes over the low/equatorial latitudes.

Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.

Equatorial plasma bubble intensities across longitudinal sectors of the globe using GNSS observations

We present the first investigation of Equatorial Plasma Bubble (EPB) intensities across longitudinal sectors of the globe using observations from global navigation satellite system (GNSS) receivers. GNSS data from a total of 93 receiver stations located within ±20 degrees of the geomagnetic equator across the globe were used. The data covered periods of years 2014 and 2019 which are respectively years of high and low solar activity in solar cycle 24. We define a parameter known as the Standard deviation of Residual TEC (SRT) to characterize the EPB intensities. The EPB occurrence was defined by day-night differences of the rate of change of TEC index (ROTI). We observed a high correlation (r ∼ 0.80) between the magnitudes of the SRT and ROTI during the EPB occurrence, but the correlation is low (r ∼ 0.37) during non occurrence of EPB. The EPB intensities are greater during seasons with high occurrence rates. The EPB intensities and occurrence rates are also greater during the high solar activity. We found that the post-sunset intensities are greatest in the Atlantic region, followed by the African region, then the American, Australian, Asian, and Pacific regions in that order. The post-midnight intensities are greatest in the African region, followed by the Atlantic, American, Australian, Asian, and Pacific regions in that order.

Statistical Analysis of Equatorial Plasma Bubbles Climatology and Multi‐Day Periodicity Using GOLD Observations

AbstractThis study develops a new Bubble Index to quantify the intensity of 2‐D postsunset equatorial plasma bubbles (EPBs) in the American/Atlantic sector, using Global‐scale Observations of the Limb and Disk (GOLD) nighttime data. A climatology and day‐to‐day variability analysis of EPBs is conducted based on the newly‐derived Bubble Index with the following results: (a) EPBs show considerable seasonal and solar activity dependence, with stronger (weaker) intensity around December (June) solstice and high (low) solar activity years. (b) EPBs exhibit opposite geomagnetic activity dependencies during different storm phases: EPBs are intensified concurrently with an increasing Kp, but are suppressed with high Kp occurring 3–6 hr earlier. (c) For the first time, we found that EPBs' day‐to‐day variation exhibited quasi‐3‐day and quasi‐6‐day periods. A coordinated analysis of Ionospheric Connection Explorer (ICON) winds and ionosonde data suggests that this multi‐day periodicity was related to the planetary wave modulation through the wind‐driven dynamo.

Statistical Study of the Ionospheric Slab Thickness at Beijing Midlatitude Station

The ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2) is known as the ionospheric equivalent slab thickness (EST, also known as τ), and it is a crucial indicator of the ionosphere. Using TEC and NmF2 data from the years 2010 to 2017, this work conducts a comprehensive statistical analysis of the ionospheric slab thickness in Beijing, which is in the midlatitude of East Asia. The outcomes show that the τ have different diurnal variations at different seasons for high/low-solar-activity years. On the whole, daytime τ significantly greater than nighttime τ in summer, and it is the opposite for the τ in winter regardless of the solar cycle, whereas the τ during equinox shows different morphology for high/low-solar-activity years. Specifically, daytime τ is larger than nighttime τ during equinox in years of high-solar activity, while the opposite situation applies for the τ during equinox in years of low-solar activity. Moreover, the pre-sunrise and post-sunset peaks are most pronounced during winter for low-solar-activity years. In summer, there is a great increase in τ during the morning hours when compared with other seasons. Furthermore, the τ decreases with the solar activity during nighttime, whereas it seems there is no correlation between daytime τ and solar activity. This paper explained the primary diurnal variations in τ across different seasons during high-/low-solar-activity years by analyzing relative fluctuations of TEC and NmF2 throughout the corresponding period. In addition, based on the disturbance index (DI), which is calculated by instantaneous τ and its corresponding median, this paper found that the storm-time τ might increase when compared with its median value during the daytime, while it may both increase and decrease during the nighttime, especially around dawn and dusk hours. To further analyze the physical mechanism, an example on 2 October 2013 is also presented. The results indicate that the positive disturbance of τ during the main phase of a geomagnetic storm might be caused by the prompt penetration electric field and neutral wind during the storm, and the τ increases during the early recovery phase might be due to the disturbance dynamo electric field as well as the neutral wind during the storm. Moreover, there is a negative disturbance of τ in the recovery phase during the most disturbed sunrise hours, and it might be due to the electric field reversal, neutral wind or other factors during this period. This paper notes the differences of τ in midlatitude between different longitudinal sectors from the related climatology and storm-time behavior, as it would be helpful to improve the current understanding of τ at midlatitudes in East Asia.

Influence of Solar Activity on Precise Orbit Prediction of LEO Satellites

The perturbations of low earth orbit (LEO) satellites operating in the orbit of 300 ∼ 2000 km are complicated. In particular, the atmospheric drag force and solar radiation pressure force change rapidly over a short period of time due to solar activities. Using spaceborne global positioning system (GPS) data of the CHAMP, GRACE and SWARM satellites from 2002 to 2020, this paper studies in depth the influence of solar activity on LEO satellites’ precise orbit prediction by performing a series of orbit prediction experiments. The quality of GPS data is more susceptible to being influenced by solar activity during years when this activity is high and the changes in dynamic parameters are consistent with those of solar activity. The effects of solar activity on LEO orbit prediction accuracy are analyzed by comparing the predicted orbits with the precise ones. During years of high solar activity, the average root-mean-squares prediction errors at 10, 20, and 30 minutes are 0.15, 0.20, and 0.26 m, respectively, which are larger than the corresponding values in low-solar-activity years by 59%, 63%, and 68%, respectively. These results demonstrate that solar activity has a great influence on the orbit prediction accuracy, especially during high-solar-activity years. We should strengthen the real-time monitoring of solar activity and geomagnetic activity, and formulate corresponding orbit prediction strategies for the active solar period.

Space weather and hypertensive crises at high Latitudes (in Yakutsk)

The space weather impact on the health status of people with cardiovascular diseases is quite significant and it has been shown by many researchers. To examine this relationship at high latitudes, we made a Yakutsk ambulance electronic database analysis. The significant coincidences of geomagnetic disturbance indicators (Kp-index - a global geophysical disturbance index) and the increased blood pressure emergency calls number have been determined during the high solar activity years. The blood pressure increase cases analysis shows that patients with primary essential arterial hypertension (AH) seek medical help in 57%. Furthermore, 7% of AH patients experienced a hypertensive crisis (HC) with an increase in systolic blood pressure (SBP) from 180 mm Hg, diastolic blood pressure (DBP) from 120 mm Hg. The medical data analysis and the space weather data (Kp-index) comparison demonstrate a significant coincidence, which became the main base for the conclusion that heliogeophysical disturbance effects on the cardiovascular system state of a person living at high latitudes.

A Low‐Latitude Three‐Dimensional Ionospheric Electron Density Model Based on Radio Occultation Data Using Artificial Neural Networks With Prior Knowledge

AbstractThe accurate estimation of electron density in the ionosphere is crucial for various applications including remote sensing systems, communication, satellite positioning, and navigation. Previous ionospheric models using artificial neural networks (ANN) are only data‐driven, whose effects are entirely affected by observational data. In this paper, we utilize the results of International Reference Ionosphere (IRI)‐2016 as prior knowledge to develop a low‐latitude (30°N–30°S) three‐dimensional ionospheric electron density model using ANN, namely ANN‐IRI, based on COSMIC radio occultation ionospheric profiles during 2006–2020. The prior knowledge helps ANN‐IRI get a better prediction effect and obtain convergence more quickly. The COSMIC data sets above the altitude 150 km are divided into three sets, a training set (2006–2014 and 2018–2020), a validation set (2016), and a test set (2015 and 2017). For the test set, ANN‐IRI shows a good performance for predicting the electron density, better than ANN (without prior knowledge) and IRI‐2016. And ANN‐IRI behaves better during quiet than disturbed times as well as during low solar activity than high solar activity years. In addition, we corrected effectively the error of ANN‐IRI in the lower ionosphere source from COSMIC data based on IRI‐2016 and spline interpolation. Compared with completely independent data sets from incoherent scatter radars (ISRs) and ROCSAT‐1 in situ observations, the electron density predicted by corrected ANN‐IRI exhibits good similarity. Generally, ANN‐IRI behaves better than ANN and IRI‐2016 for predicting electron density. Our work demonstrates a new possibility of applying deep learning methods with prior knowledge to a broader field of geosciences, particularly for problems of prediction.

Relationships between ionospheric parameters derived from ionosonde observations and characteristics of post-sunset GHz scintillation during high solar activities (2012−2013) at Sanya (18.3°N, 109.6°E), China

In this study, we presented the characteristics of post-sunset GHz scintillation occurrence and ionospheric parameters derived from ionosonde observations, including the critical frequency of F2-layer (foF2), peak height of F2-layer (hmF2), minimum virtual height of F-layer (h’F), scale height around the F2-layer peak (Hm), virtual height (h’F5) and true height (hF5) measured at 5 MHz, in high solar activity years (2012-2013) of solar cycle 24 at Sanya (18.3°N, 109.6°E; dip lat.: 12.8°N), China. Furthermore, we investigated the relationships between the equinoctial asymmetry of scintillation and these ionospheric parameters. In addition, the growth rates of Rayleigh-Taylor instability were calculated on the basis of the ionosonde measurements and models, respectively. The results showed that the equinoctial asymmetry of scintillation onset time would be associated with the scale length of vertical electron density gradient (L), which affects the growth of Rayleigh-Taylor instability at the bottom of F-layer. The seasonal variations of foF2, Hm and scale length of vertical electron density gradient would be the reason to cause the seasonal variations of scintillation occurrence, and the equinoctial asymmetry of scintillation occurrence rate over low latitude can be related to the background electron density and the vertical drifts around sunset in F-layer. The correlations between the scintillation strength represented by the daily maximum S4 and the daily maximum values of foF2, hmF2, h’F, Hm, and also the drifts over low latitudes were weak, which need to be further studied.

Variation of ionosonde [formula omitted] and its comparison with IRI-2016 & a local model over Pakistan longitude sector during solar cycle 22

The study presents the variation of ionospheric critical frequency of E-layer (foE), its comparison with International Reference Ionosphere (IRI-2016) over Pakistan and suggests an artificial neural network (ANN) based algorithm to predict noontime daily hourly values. The ionosonde measurements of Karachi (Geog. Coord: 24.95°N, 67.13°E, dip latitude = 17.0°N), Multan (30.18°N, 71.48°E, 21.8°N) and Islamabad (33.75°N, 73.13°E, 25.2°N) have been used for the purpose. The observed foE values have been analysed during the high, moderate and low solar activity years 1989, 1992 and 1996, respectively. Results show a strong correlation between foE and solar activity which was expected because the E-layer is essentially solar controlled. It is found that foE peaks around noon with higher values during summer. The annual relative percentage deviation between data and IRI-2016 modelled values remains less than 6% for all locations and years under study. The ANN based algorithm predicts daily noontime foE values with a goodness of fit of 78%, compared to the IRI-2016 prediction, which has a goodness of fit of 71%. It is noted that the performance of IRI-2016 in predicting monthly hourly median values of foE improves with decreasing solar activity. The study confirms the data authenticity of foE over Pakistan and suggests an ANN based algorithm in predicting noontime values with no immediate updates to IRI-2016 for this Asian longitude sector.

The Variation Characteristics and Prediction Performance of TEC in the Geomagnetic Latitude and Local Time Coordinate

AbstractTotal electron content (TEC), as one of the most valuable ionosphere parameters, has many variation characteristics. The TEC has specific characteristics at a given time, suggesting that the global TEC morphology distribution is maintained for a period. We consider the global ionosphere map (GIM) to be the actual ionospheric electron density integration, predict the GIM by a previous period in a geomagnetic latitude and local time (LT) coordinate system, and compare the differences between these two GIMs. The objective of this research is to find the morphological properties of global TEC in the geomagnetic latitude and LT coordinate system, reveal ionospheric behavior and predict TEC using an uncomplicated method. The GIM data for a solar cycle (2009–2020) are used to find the error characteristics and prediction performance of TEC in this coordinate. The equatorial ionization anomaly is the dominant error region. This error shows diurnal and seasonal variations and has a strong correlation with solar activity. The prediction error of this method is smaller than that of the International Reference Ionosphere (IRI) model, especially in high solar activity years. The root mean square error of the 3‐hr prediction is 6.53 in 2014 and 2.02 TECU in 2019, and it is 28.5% smaller than that of IRI on average for 2009–2020.

Quasi-4-dimension ionospheric modeling and its application in PPP

Ionospheric delay modeling is not only important for Global Navigation Satellite System (GNSS) based space weather study and monitoring, but also an efficient tool to speed up the convergence time of Precise Point Positioning (PPP). In this study, a novel model, denoted as Quasi-4-Dimension Ionospheric Modeling (Q4DIM) is proposed for wide-area high precision ionospheric delay correction. In Q4DIM, the Line Of Sight (LOS) ionospheric delays from a GNSS station network are divided into different clusters according to not only the location of latitude and longitude, but also satellite elevation and azimuth. Both Global Ionosphere Map (GIM) and Slant Ionospheric Delay (SID) models that are traditionally used for wide-area and regional ionospheric delay modeling, respectively, can be regarded as the special cases of Q4DIM by defining proper grids in latitude, longitude, elevation, and azimuth. Thus, Q4DIM presents a resilient model that is capable for both wide-area coverage and high precision. Four different sets of clusters are defined to illustrate the properties of Q4DIM based on 200 EUREF Permanent Network (EPN) stations. The results indicate that Q4DIM is compatible with the GIM products. Moreover, it is proved that by inducting the elevation and azimuth angle dependent residuals, the precision of the 2-dimensional GIM-like model, i.e., Q4DIM 2-Dimensional (Q4DIM-2D), is improved from around 1.5 Total Electron Content Units (TECU) to better than 0.5 TECU. In addition, treating Q4DIM as a 4-dimensional matrix in latitude, longitude, elevation, and azimuth, whose sparsity is less than 5%, can result in its feasibility in a bandwidth-sensitive applications, e.g., satellite-based Precising Point Positioning Real-Time Kinematic (PPP-RTK) service. Finally, the advantages of Q4DIM in PPP over the 2-dimensional models are demonstrated with the one month's data from 30 EPN stations in both high solar activity year 2014 and low solar activity year 2020.

Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years

Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012).

Climatology of TEC Longitudinal Difference in Middle Latitudes of East Asia

In this paper, a statistical analysis of the diurnal, seasonal and solar cycle variation in the TEC longitudinal difference in midlatitudes of East Asia is presented using CODE GIMs data in 2015–2019. Moreover, the empirical neutral wind model HWM-14 and geomagnetic field model IGRF-2020 were employed to analyze the influence of geomagnetic configuration-neutral wind mechanism on the TEC longitudinal difference, and the F2 layer peak electron density (NmF2) data from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) were also used to study the role of local electron density in the TEC longitudinal difference. For the high solar activity year, the results show that east-west TEC longitudinal difference index Re/w is negative in the noon and positive at evening-night. Moreover, the longitudinal difference of daytime TEC is most evident in summer, less in autumn and least in spring and winter, while the nighttime difference is most obvious in equinox, followed by summer and winter during nighttime. The model simulation shows that the TEC longitude difference around noon is mainly caused by the zonal wind-declination mechanism, and a 4-h time delay seems to be an optimal result for the vertical drift velocity to cause the longitudinal TEC difference during pre-noon hours. At night, the uplifting electron flux, which is a product of local electron density and vertical drift velocity, shows a good correlation with Re/w, indicating that the local electron density is also an important factor affecting the TEC longitudinal difference during the nighttime. Moreover, there was about a 3-h time delay between the TEC longitudinal variations and the uplifting electron flux at night. For the low solar activity years, the western TEC is greater than eastern TEC during most of the year except in the summer nighttime. The TEC diurnal variation in the east and west suggested that the nighttime Re/w should be related to other physical process, such as the midlatitude summer nighttime anomaly (MSNA) in the east and the ionospheric nighttime enhancement (INE). The current study provides evidence for the longitudinal difference of NmF2 in East Asian midlatitudes and geomagnetic configuration-neutral wind mechanism proposed in previous studies and finds some new features which need further studying to improve our current understanding of ionospheric longitudinal difference in the low solar activity years. The results provide new insight into TEC longitudinal variations at midlatitudes, and they can contribute to understanding the ionosphere-thermosphere coupling system.

On the Origin of the Solar Cycle Modulation of the Winter North Atlantic Oscillation

AbstractUsing data from recent 64 years, we found that the zonal wind signal associated with the winter‐mean North Atlantic Oscillation (NAO) tends to extend into the upper stratosphere when solar activity is high, but the vertical extension is limited when solar activity is low. To examine the physical source of this phenomenon, the momentum and wave‐energy budget are analyzed using the 63‐winter record of the Japanese Reanalysis data set from 1958 to 2021. These analyses show that (a) the zonal‐mean momentum acceleration is driven by eddies throughout the entire depth of the atmosphere around 60°N, (b) the barotropic energy conversion from eddies to zonal‐mean flow is centered in the middle stratosphere around 55°N, and (c) the baroclinic energy conversion from zonal‐mean flow to eddies from the surface to the middle stratosphere around 60°N tends to be enhanced from December to February associated with the positive winter‐mean NAO index in High Solar (HS) activity years. These energy transfers are closely related to enhanced zonal wind. These analyses suggest that the structural modulation of the NAO due to the solar cycle comes from the enhanced sensitivity of the wave‐mean flow interaction in the stratosphere in HS years, which is broadly shared with the solar cycle modulation of the late‐winter Southern Annular Mode (SAM).

CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast

In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences.

Equatorial Plasma Bubble Detection Using Vertical TEC From Altimetry Satellite

AbstractIn this study, we detect the equatorial plasma bubble (EPB) using vertical total electron content (vTEC) from altimetry satellite for the first time. The Jason‐2 vTEC data during the high solar activity year of 2014 are employed to investigate the characteristics of EPB. The latitudinal and temporal variations of EPB are analyzed first and the results are consistent to those of previous reported studies, suggesting the observed EPB events are reliable. Furthermore, due to the features of vTEC, the zonal scale and vertical depth of EPB are also obtained: the zonal scale varies mainly with small scope of 0.5 ∼ 1.5° and seems to be independent of latitude, whereas the vertical depth is related to latitude with large value located in the crest region of the equatorial anomaly. The results show the altimetry satellite vTEC is feasible for EPB detection and can serve as a new tool for EPB monitoring.

Two Hours Ahead Prediction of the TEC over China Using a Deep Learning Method

In this paper, we forecast the regional total electron content (TEC) over China (0–60° N, 70–140° E) two hours in advance using a deep learning method called pix2pixhd that is based on Generative Adversarial Networks (GAN). We use the International GNSS Service (IGS) TEC maps over China during the 2003–2018 period for training and divide the data into three parts: a training set (2003–2013), a test set (2014–2017), and a validation set (2018). We evaluate the prediction effect of our model using Root Mean Square Error and correlation coefficient and compare our model with IRI-2016. The result demonstrates that our model shows a good performance for TEC prediction in China. Under different geomagnetic and solar activity conditions, the performance of our model is always better than IRI-2016. Analyzing the average difference map between the output of our model and the target IGS TEC map (+2 h), our model behaves well in China including the low-latitude region. In addition, our model behaves better during quiet time and high solar activity years. The successful application of pix2pixhd in forecasting the regional TEC maps over China demonstrates that deep learning methods can solve many geoscience problems, especially for ionospheric parameter forecasting.

Analysis of local geomagnetic index under the influence of equatorial electrojet (EEJ) at the equatorial Phuket geomagnetic station in Thailand

The local K-index is an important proxy to monitor geomagnetic disturbances due to the solar wind in space weather study. The diurnal variation of geomagnetic fields observed in the magnetic equatorial region is dominated by the equatorial electrojet (EEJ), and the variation of EEJ is directly related to the local ionospheric dynamics; therefore, in this work, the local K-index is generated by based on the geomagnetic field measurement at an equatorial geomagnetic station in Phuket, Thailand and the effects of EEJ on the computed local K-indices are analyzed. At each station, an L9 (the lower limit for K = 9) value is set to develop a conversion table between the magnetic range scales and K-indices, and that L9 value must be assigned based on the characterization of the geomagnetic variations at that station. In this work, suitable L9 values are determined by analyzing the distributions of the local K-index and the planetary geomagnetic index, Kp-index. According to the results in the present study, the L9 value of 500 nT can provide local K-indices that can classify the geomagnetic disturbances more correctly. The results show that 40% of the local K-index is consistent with the Kp-index, and about 45% of the local K indices are ±1 deviated from Kp-indices. It is found that using the suitable L9 value can partially control the EEJ’s dominance on K-index. Moreover, we investigated the seasonal and day-to-day variability of the diurnal variation of the geomagnetic fields from the Phuket station. Upon reviewing the data, the equatorial geomagnetic field variations were consistent with the planetary geomagnetic activity levels, and the day-to-day changes of the daytime field amplitudes were relatively high in the high solar activity year and moderate in the low solar activity year.

Nighttime morphology of vertical plasma drifts over Vietnam during different seasons and phases of sunspot cycles

This study is the first to provide the nighttime morphology of vertical plasma drifts over Vietnam at the equatorial trough and the northern tropical crest of ionization anomaly in the Asian sector. We use the h’F data in Vietnam from Bac Lieu observatory (9.28°N, 105.73°E, dip: 1.73°N) during the 2006–2019 period and Phu Thuy observatory (21.03°N, 105.96°E, dip: 14.49°N) for the 1964–2011 period, to calculate the vertical plasma drift velocity (Vd) and analyze the annual, seasonal, sunspot cycle, and sunspot cycle (SC) phase evolutions. For Bac Lieu, the PRE and minimum reversal peaks evolve according to the sunspot cycles. The seasonal average of the drift pattern shows that the PRE peak appears earlier one hour in summer (1800 LT) than in equinoxes and winter (1900 LT) and the minimum reversal peak occurred about 2100–2200 LT, except in summer, in which this peak identifies around 0100 LT. This drift pattern exhibits a semiannual asymmetry during PRE/minimum reversal periods with peaks in March and September/October. The evening-time pre-reversal enhancement (PRE)at Bac Lieu is dependent on the phase of the sunspot cycle. For Phu Thuy, the mean annual peak magnitudes of Vd during the PRE and minimum reversal periods exhibit a poor correlation with the sunspot number (Rz). For the SC seasonal variation, the lowest PRE peak magnitude in autumn appeared one hour later compared with winter, one hour earlier than summer, and the same time as spring, while the minimum reversal peak is larger in summer for SCs 20–23 and in autumn for SCs 20, 23, and 24 at around 0200–0300 LT than in another season at about 2000, 2200, and 2300 LT. The morphology of the seasonal average variations of Vd is rather similar for all SCs except SC 24 at Phu Thuy. The PRE peak during the high solar activity years (2000 LT) appears earlier about 4 h than that during the low solar years (at 2300 LT/0000 LT). The monthly average of plasma drift also exhibits semiannual asymmetry during PRE events for cycles 22, 24, and minimum reversal period for cycle 24 with peaks in March and September. In addition, it shows an annual variation for the remaining cycles during PRE/minimum reversal periods with peaks in the summer months. The pre-sunrise enhancement characteristic (0500 LT) was observed during all seasons and phases (ascending, descending, minimum) at Bac Lieu, summer for all SCs, equinoxes for SC 24, the ascending and minimum phases for the SC 24, and the descending phase for the SC 23 at Phu Thuy.