Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning prediction approach. The results show that the low-latitude sunspot number has a larger effect on the solar wind speed. The time delay between the annual average solar wind speed and sunspot number decreases as the latitude range decreases. A machine-learning model is developed for the prediction of solar wind speed considering latitude and time effects. It is found that the model performs differently with latitude-dependent sunspot data. It is revealed that the timescale of the solar wind speed is more strongly influenced by low-latitude sunspots and that sunspot data have a greater impact on the 30 day average solar wind speed than on a daily basis. With the addition of sunspot data below 7.°2 latitude, the prediction of the daily and 30 day averages is improved by 0.23% and 12%, respectively. The best correlation coefficient is 0.787 for the daily solar wind prediction model. 

How to cite: Jiao, Q., liu, W., zhang, D., and cao, J.: Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5106, https://doi.org/10.5194/egusphere-egu24-5106, 2024.

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring large scale ionospheric irregularities and anomalies is possible. We recently developed a fully connected neural network model trained with Global Ionospheric Maps (GIMs) data from the last two solar cycles. The model can successfully reconstruct ionospheric features that are not always visible such as Nighttime Winter Anomaly (NWA) which is only visible in the Northern Hemisphere at the American sector and in the Southern Hemisphere at the Asian longitude sector during low solar activity, winter and local night-time conditions. The NN based TEC model inherits also other features such as the distribution of Mid-latitude Ionospheric Trough (MIT) and the longitudinal variation of the Equatorial Ionization Anomaly (EIA) features. Being motivated from the performance of the NN based TEC model in ionosphere reconstruction we applied the model for differential code bias (DCB) estimation for a network of ground GNSS receivers. The investigation shows that the receiver DCBs can be accurately computed by the NN-based TEC model. The obtained accuracies are comparable to those obtained by the conventional method of DCB estimation by fitting GNSS TEC data to the ionospheric basis function represented by spherical harmonics or other approaches. It is expected that the application of NN based TEC model for GNSS receiver bias estimation will simplify the operational procedures for near real-time ionosphere monitoring without losing its accuracy.

How to cite: Hoque, M., Adolfs, M., and Salamanca, L. R.: Improved GNSS receiver bias estimation using a neural-network based total electron content model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12493, https://doi.org/10.5194/egusphere-egu24-12493, 2024.

In this study, we present a novel approach to improve ionosphere prediction by combining the Ionospheric Data Assimilation Four-Dimensional (IDA4D) algorithm with Convolutional Neural Networks (CNNs). The IDA4D algorithm constructs a three-dimensional global electron density by assimilating ground-based GPS slant total electron content (STEC), radio occultation STEC, and radio occultation NmF2 data into the IRI model. The IDA4D outputs are fed into CNNs to learn spatiotemporal patterns. Results are validated with ionosonde and CODE TEC data, demonstrating significant improvements and reducing the root-mean-square error (RMSE) of Nmf2 and vertical TEC by 34% and 51%, respectively, compared to the IRI model. Furthermore, the IDA4D technique successfully reconstructed storm time enhancement of the northern crest equatorial ionization anomaly during late evening hours, resulting from upward and northward plasma transport. The combination of IDA4D and CNNs predicts a three-dimensional electron density more accurately than the IRI model for up to two days.

How to cite: Mengist, C. K. and Seo, K.-H.: Predicting Global Ionosphere in Three Dimensions: Integrating Data Assimilation with Convolutional Neural Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15325, https://doi.org/10.5194/egusphere-egu24-15325, 2024.

Using the electron density (Ne) observations from the Defense Meteorological Satellite Program (DMSP), and Constellation Observing Systems for Meteorology, Ionosphere, and Climate mission (COSMIC) and simulations from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM), we investigate the dynamic evolution of the polar tongue of ionization (TOI) from double to single structures at different altitudes during a geomagnetic storm. The modeled Ne depicted that double and single TOIs occurred at altitudes above 300 km, respectively. During the northward turning of IMF Bz, the afternoon TOI disappeared and the morning TOI was reduced. The plasma transport due to neutral winds and ambipolar diffusion facilitated (prevented) the depletion of plasma density of the morning TOI at 300 (500) km, with a relative contribution of 42.8% and 28.6% (-15.4% and -76.9%), respectively. Downward E × B drifts led to an enhancement/reduction of plasma density in the SED region in the lower/upper ionosphere. During the duskward turning of IMF By, the morning TOI could be mostly attributed to the anti-sunward plasma drifts (75.8% at 300 km, 100% at 500 km), with a relatively stronger role of the zonal component than that of meridional E × B drifts. The upward E × B drifts were important/ignorable in the upper/lower ionosphere. Both the neutral winds and ambipolar diffusion resulted in an accumulation of plasma density of the morning TOI at 300 km indirectly (24.2%), however, their roles were minor at 500 km.

How to cite: song, H.: Dynamics of the tongue of ionizations at different altitudes during the geomagnetic storm on September 7, 2015, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14780, https://doi.org/10.5194/egusphere-egu24-14780, 2024.