G5.1

How to cite: Schreiter, L., Kervalishvili, G., Rauberg, J., Stolle, C., van den Ijssel, J., Arnold, D., Xiong, C., and Callegare, A. O.: Topside Ionosphere Radio Observations from multiple LEO-missions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5129, https://doi.org/10.5194/egusphere-egu22-5129, 2022.

This study introduces a new two-step neural-network based approach for modelling vertical total electron content (VTEC) on a global level. The inputs to the neural network are chosen and evaluated through different feature selection techniques, namely time-lagged Pearson cross-correlation, mutual information, random forests and permutation feature importance. The feature sets consist of geomagnetic and solar wind indices, their time histories and geomagnetic and geographic coordinates. The parameters of the neural networks are tuned with cross validation and the final model is tested in extended time intervals covering a wide range of solar activity conditions. The proposed approach increases computational efficiency and provides with a high generalization skill.

How to cite: Kume, K., Shprits, Y., Smirnov, A., Zhelavskaya, I., Vasile, R., and Bianco, S.: Modelling global vertical total electron content with machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8475, https://doi.org/10.5194/egusphere-egu22-8475, 2022.

The objective of the work is to compare geomagnetic storm impact on the ionosphere parameters measured from ground-based GNSS permanent stations and Swarm satellites. The analyses compare the changes of vertical total electron content (VTEC) measured along the entire ionosphere cross-section to the variations of electron density (ED) on the orbit, at ~500 km altitude. The objective is how sensitive are the measures of electric field variations available on the Earth, with respect to those obtained from the satellite orbit. The study applies Swarm in-situ ED measured by Langmuir Probes (LP), topside TEC from onboard Swarm GNSS receivers and vertical TEC determined from ground-based GNSS stations available in the area of the Northern polar cap. Ground and satellite data were processed in different ways. Ground-based VTEC is available at number of stations providing heterogeneous but useful horizontal coverage. Therefore ROTI values were calculated from VTEC as gradient values capable to indicate the disturbances. These ROTI values were interpolated spatially to obtain maps. Swarm passes over the polar cap starting from 45° lasts for several minutes each, and repeat in this region approximately every 1.5 h. Such along-track collected small data samples are useless in horizontal correlation analysis. Therefore Swarm data disturbances, in this case, are extracted with the use of Fourier transform-based filtering and are also analyzed in the spectrograms based on short-term Fourier transform (STFT). The case study has used three geomagnetic storms, namely: the St. Patrick storm of March 17, 2015, the storm on June 22, 2015, and the storm on August 25-26, 2018. The results reveal differences in storm impact on VTEC measured by GNSS on the Earth, with respect to the storm influence on topside TEC and in-situ ED disturbances measured onboard the Swarm. The overall summary statistics provide some preliminary conclusions on different times of the reaction to the storm. Additionally, some interesting differences between FT filtering and a very popular moving average are shown, with respect to Swarm data. The research was done in the frame of the FORSWAR project (Forecasting Space Weather in the Arctic Region) funded by ESA.

How to cite: Jarmolowski, W., Monte-Moreno, E., Wielgosz, P., Paziewski, J., Hernandez-Pajares, M., Miloch, W., Jin, Y., Berdermann, J., Hoque, M., Høeg, P., Garcıa Rigo, A., Milanowska, B., Clausen, L., Yang, H., Lyu, H., and Orús-Pérez, R.: Swarm in-situ electron density disturbances over Greenland compared to VTEC disturbances from ground-based GNSS, during three geomagnetic storms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8785, https://doi.org/10.5194/egusphere-egu22-8785, 2022.

In the last decades, advances in satellite technologies, data analysis techniques, and models, and a growing number of analysis centers allow modeling the ionospheric electron content with unprecedented accuracy. International GNSS Service (IGS) Ionosphere Associated Analysis Centers (IAAC) continuously provide global ionospheric maps (GIMs) based on processing GNSS data from the ground IGS network. It is a great advantage that these GIMs are often based on very different modeling techniques and thus, are also characterized by different accuracy levels. Due to the dynamic nature of the ionosphere, there is a permanent need to improve the modeling techniques of ionospheric key parameters such as the vertical total electron content (vTEC).

In this presentation, we evaluate a new ionosphere model from DGFI-TUM denoted OTHG, which is a candidate for a new IGS IAAC product. The OTHG model is based on tensor products of trigonometric B-spline functions in longitude and polynomial B-spline functions in latitude for a global representation (Goss et al. 2019). Here, we complement our earlier investigations of the seven analysis center models (Wielgosz et al. 2021) with new results for the OTHG GIMs. For these investigations, we use our own validation methodology presented in Krypiak-Gregorczyk et al. (2017), which is based on GIM-derived slant TEC (sTEC) comparison with carrier phase geometry-free combination of GNSS signals. In the presented study, we use one year of GNSS data collected by 25 globally distributed stations. The overall yearly RMS value is calculated for each product based on all 365 days of continuous observations from all stations. The results show that the overall RMS of the tested GIMs ranges from 0.93 TECU to 1.29 TECU. The OTHG GIMs performed as one of the best. In addition, GIM vTEC comparisons to Jason-2 and Jason - 3 altimetry data are studied. In these analyses, the OTHG GIMs also showed a good performance. Therefore, it can be concluded that  DGFI-TUM is a valuable ionospheric product for the research community.

Goss A., Schmidt M., Erdogan E., Görres B., Seitz F. (2019) High-resolution vertical total electron content maps based on multi-scale B-spline representations. Annales Geophysicae, 37(4), 10.5194/angeo-37-699-2019

Krypiak-Gregorczyk A., Wielgosz P., Borkowski A. (2017) Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation, Remote Sensing, 9(12), 1221,  DOI:10.3390/rs9121221

Wielgosz P., Milanowska B., Krypiak-Gregorczyk A., Jarmołowski W. (2021) Validation of GNSS‑derived global ionosphere maps for different solar activity levels: case studies for years 2014 and 2018. GPS Solutions 25, 103. https://doi.org/10.1007/s10291-021-01142-x

How to cite: Krypiak-Gregorczyk, A., Milanowska, B., Schmidt, M., Goss, A., Erdogan, E., Jarmołowski, W., and Wielgosz, P.: Validation of DGFI-TUM’s new ionosphere model: case studies for year 2018, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8931, https://doi.org/10.5194/egusphere-egu22-8931, 2022.

Benefiting from the global multi-frequency and multi-constellation GNSS measurements provided by the International GNSS Real-Time Service (IGS-RTS), real-time global ionospheric maps (RT-GIMs) have been generated by the Chinese Academy of Science (CAS), Centre National d’Etudes Spatiales (CNES) and Universitat Politècnica de Catalunya (UPC) since 2017, and late by Wuhan University (WHU) since late 2020. To provide a more stable real-time ionospheric correction stream for precise GNSS application and ionospheric space weather monitoring, an experimental combined RT-GIM has been generated at CAS since late-2021 using real-time streams from CNES, UPC, WHU, and CAS itself. CAS combined RT-GIM streams are transmitted in both RTCM-SSR (IONO01IGS0) and IGS-SSR (IONO01IGS1) standards, which are freely accessible from IGS (products.igs-ip.net:2101) and CAS (cas-ip.gipp.org.cn:2101) casters. Following the motivation for the RT-GIM combination, the method used to generate the CAS combined RT-GIM is described in detail. The performance of CAS combined RT-GIM is validated in both RT-GPS dSTEC and single-frequency precise point positioning (SF-PPP) domains by comparison with the existing combined RT-GIM provided by UPC. CAS combined RT-GIM is presently validated and analyzed for a short period (2-3 months), and the analysis covering different time spans (e.g., different seasons) is being done together with the RT-GIM combination.

How to cite: Wang, N., Zhang, Y., Li, Z., Li, Y., and Krankowski, A.: Combination of IGS Real-Time Global Ionospheric Maps at CAS: Method and Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11458, https://doi.org/10.5194/egusphere-egu22-11458, 2022.

GNSS single-frequency applications are affected by the interaction of the radio signals with the free electrons of the ionosphere, introducing range errors of up to 100 m in the L-band. Besides the GPS Klobuchar and the Galileo NeQuick ionospheric models, also the Neustrelitz Total Electron Content Model (NTCM) has been proposed as a practicable solution to mitigate such propagation errors.

In this investigation we present a global statistical validation of the NTCM version driven by Galileo ionization coefficients (NTCM-GlAzpar) by comparing its performance with the performance achieved by the NeQuickG and Klobuchar models in the position domain. For this aim, we used the ESA analysis tool “gLAB” in the Standard Point Positioning (SPP) mode and GNSS data over two different time periods. The first period covers one month of perturbed solar activity (December 2014) and the second period corresponds to a month of quiet conditions (December 2019). We achieved a worldwide coverage with data from up to 73 receivers of the International GNSS Service (IGS).

Our statistical analysis allows us to conclude that the NTCM-GlAzpar model clearly outperforms the results achieved with the Klobuchar model and is slightly better, or at least comparable, to the performance shown by NeQuickG. Indeed, the root mean squared (RMS) values of the hourly mean 3D position errors obtained for the global dataset are 4.36, 4.61 and 6.71 meters for perturbed conditions and 2.32, 2.35 and 2.75 meters for the quiet period, respectively for the NTCM-GlAzpar, NeQuickG and Klobuchar models. Nevertheless, through a geographic- and diurnal-specific analysis, we identify also that the performance of NTCM-GlAzpar slightly decreases for conditions of reduced solar activity – at night time, higher latitudes and low perturbations.

We further discuss the applicability of the NTCM-GlAzpar ionospheric model in GNSS single-frequency applications motivated by its simple software adaptations and low computational cost.

How to cite: Cahuasquí, J. A., Hoque, M., and Jakowski, N.: Positioning performance of the Neustrelitz Total Electron Content Model (NTCM) driven by Galileo ionization coefficients, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10430, https://doi.org/10.5194/egusphere-egu22-10430, 2022.

During geomagnetic storms, communication and navigation instruments can be dramatically affected by the rapid changes that occur in the upper atmosphere. The assimilation of data in physics-based models such as the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model through and ensemble Kalman filter, can improve the representation of the thermosphere-ionosphere (TI) system. Due to the coupled nature of the TI system, the ionosphere is affected by, among others, changes in the neutral atmosphere. In this study, we investigate the capability of the CTIPe model to provide better estimates of the ionosphere by improving its specification of the thermosphere via data assimilation. Here, we assimilate thermospheric mass density (TMD) observations from the Swarm mission normalized to 400 km altitude during the 2015 St. Patrick’s Day storm. The changes that occur in the ionosphere due to assimilation of TMD data are measured by means of the difference between the model results with and without assimilation. To measure the improvement gained with data assimilation, we compare with independent measurements of electron density along the orbit of GRACE (Gravity Recovery and Climate Experiment) satellite, that shows a reduction in the root mean square error (RMSE) by a 22% with respect to the non-assimilation run. The impact on the global scale is measured by comparing the CTIPe model results with the corresponding output of the 3D B-Spline electron density model. The results illustrate that the electron density equatorial region is the most affected region by assimilation of TMD, with an average RMSE reduction of 25% at the assimilation altitude of 400 km.

How to cite: Fernandez-Gomez, I., Kodikara, T., Borries, C., Forootan, E., Schmidt, M., and Codrescu, M.: Improving the ionospheric state estimate during geomagnetic storm time through assimilation of neutral density data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9517, https://doi.org/10.5194/egusphere-egu22-9517, 2022.

The neutral mass density of the upper atmosphere (thermosphere) can be determined by orbit and accelerometer data from Low Earth Orbit (LEO) satellites. Especially the accelerometers of geodetic satellites, measuring the non-gravitational accelerations acting on the satellites, are a very useful observation for precise density determination also on very short time scales.

Nevertheless, the accelerometers of geodetic satellites are affected by bias and drift. Therefore a calibration of the data is indispensable. A time dependent bias and scale factor are to be determined. Usually calibration parameters are estimated by dynamic Precise Orbit Determination (POD) or Gravity Gield Recovery (GFR), together with all other parameters of interest. In both cases, the estimated accelerometer calibration parameters are not the major interest. With the used parametrizations and weighting of the observations, good gravitational field and orbit solutions, do not necessarily give good or physical accelerometer calibration solutions. This is unsatisfying, especially for the anticipated use in density determination and the direct comparison to modeled non-gravitational accelerations.

In this contribution we use dynamic POD and investigate different parametrization strategies tailored for an accurate and physical accelerometer calibration for thermospheric density determination. For example we investigate the effect of constraining the accelerometer calibration parameters in that way, that a continuous calibration over all arcs is achieved, where normally each arc is treated locally separated from all other arcs, leading to jumps in the calibration. The scale factor, which is highly correlated to the estimated bias, is concurrently estimated but over a longer batch of arcs. We compare different bias parametrizations, arc lengths, as well as different observation data and weighting strategies.

Finally we show some preliminary density estimation results with our approach and the influence of the accelerometer calibration.

How to cite: Wöske, F. and Rievers, B.: Accelerometer calibration for thermospheric neutral density estimation with GRACE data by dynamic Precise Orbit Determination (POD) with tailored parametrization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7702, https://doi.org/10.5194/egusphere-egu22-7702, 2022.

The TIE-GCM (Thermosphere Ionosphere Electrodynamics General Circulation Model) is a numerical model of the upper atmosphere, providing extensive information about the neutral and charged particles therein. It enables simulations of the neutral density that is required to compute the drag acceleration acting on satellites. We have developed an assimilative version of the TIE-GCM using PDAF (Parallel Data Assimilation Framework), to improve the neutral density modeling and the derived drag acceleration. Here, we present an experiment in which we assimilate neutral densities from a calibrated empirical model into the TIE-GCM: In a first step, we have scaled the NRLMSIS2.0 with densities derived from the CHAMP accelerometer, then evaluated it on a regular grid and finally assimilated the neutral densities into the TIE-GCM. We assess the performance of the assimilation framework using densities derived from the CHAMP and GRACE acceleromerters. The investigated time period includes quiet and stormy conditions.

How to cite: Corbin, A., Vielberg, K., and Kusche, J.: An Assimilative Version of TIE-GCM using PDAF, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2911, https://doi.org/10.5194/egusphere-egu22-2911, 2022.