An investigation of new ionospheric models using multi-source measurements and neural networks

Ionosphere is one of the atmospheric layers that has a major impact on human beings since it significantly affects the radio propagation on Earth, and between satellites and Earth (e.g., Global Navigation Satellite Systems (GNSS) signal transmission). The variation of the electrons in the ionosphere is strongly influenced by the space weather due to solar and cosmic radiation. Hence, the short/long-term trend of the free electrons in the ionosphere has been regarded as very important information for both space weather and GNSS positioning. On the other hand, precisely quantifying the distribution and variation of free electrons at a high spatio-temporal resolution is often a challenge if the number of the electrons (electron density) is detected only from the traditional ionospheric sensors (e.g., ionosonde and topside sounder and Incoherent Scatter Radar (ISR)) due to their low spatio-temporal coverage. This disadvantage is also inherited from the empirical ionospheric model developed based on these data sources. Nowadays, the availability of advanced observation techniques, such as GNSS Radio Occultation (RO) and satellite altimetry, for the measurement of Electron Density (Ne) and related parameters (e.g., hmF2, NmF2, Vertical Scale Height (VSH), Electron Density Profile (EDP) and Vertical Total Electron Content (VTEC)) in the ionosphere has heralded a new era for space weather research in the upper atmosphere. The new sources of data for ionospheric modelling can improve not only the accuracy but also the reliability of the model (such as[96] for hmF2 and [28] for VTEC). In this study, Helmert Variance Component Estimation (VCE) aided Weight Total Least Squares (WTLS) is selected for modelling global VTEC using International GNSS Service stations, satellite altimetry and GNSS-RO measurements. The results show that the new VTEC model outperforms the traditional global ionospheric VTEC Model by at least 1.5 Total Electron Content Unit (TECU) over the ocean. This improvement is expected to be significant in the refinement of global ionospheric VTEC Model development. As is well known, the most traditional models developed are prone to the effects of inherent assumptions (e.g. for the construction of the base functions in the models) which may lead to large biases in the prediction. In this study, an innovative machine learning technique (i.e. Neural Network (NN)) is investigated as the modelling method to address this issue. Different from the traditional modelling method, neither the observation equations (or the so called `design matrix'), nor apriori knowledge of the relationship (both of them can be considered as the source of the aforementioned assumptions) is required in the modelling process of a NN. This network system can automatically construct an optimal regression function based on a large amount of sample data and the designed network [43]. In this study, Deep Neural Network (DNN), which is an advanced Artificial Neural Network (ANN) (with more than one hidden layer), is investigated for their usability of VSH and topside EDP modelling, as well as the relationship between Ne and electron temperature. The results reveal that the new VSH model agrees better than the traditional model with regards to either out-of-sample measurements or the external reference (i.e. ISR data). In addition, the new model can represent the characteristic of VSH in the equatorial region better than that of traditional approaches during geomagnetic storms. The relationship between Ne and Electron Temperature (Te) investigated from ISR data can be used to improve the performance of the current Te model. The local time-altitude variation of the model outputs agrees well with that from a physical model (i.e., Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM)). The new topside EDP model takes hmF2 and NmF2 into consideration as part of the variable set. Comparing with the reference data (i.e., out- of-sample COSMIC data, GRACE and ISR data), the new model agrees much better than the International Reference Ionosphere (IRI)-2016 model. In addition, an advanced NN technique, Bidirectional Long Short-Term Memory (Bi-LSTM), is utilised to forecast hmF2 by using the hmF2 measured by Australian ionosondes in the five hours prior. The forecast results are better than the results from real-time models in the next five hours. The new model performs also better than the current hmF2 model (i.e., AMTB [2] and shubin [96] models, which is used inside IRI-2016 model) by at least 10km in most ionosonde stations. Overall, the neural network technique has a great potential in being utilised in the ionospheric modelling. In addition to the accuracy improvement, the physical mechanism can be observed from the model outputs as well. In future work, the neural network is expected to be further applied in some other space weather studies (e.g., Dst, solar flare, etc)

Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde

The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-v

Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region

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), Kp, F10.7 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

Using Bidirectional Long Short-Term Memory Method for the Height of F2 Peak Forecasting from Ionosonde Measurements in the Australian Region

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

Precise thermospheric mass density modelling for orbit prediction of low earth orbiters

The steady increase in the number of space objects near the Earth has raised critical security concerns for the low Earth orbit (LEO) space environment where most of the near-Earth satellites missions operate. Orbit prediction (OP) is the foundation of many space missions and applications in LEO, e.g., space situational awareness, re-entry prediction and debris removal. However, the precision of OP is limited due to the accuracy of thermospheric mass density (TMD) prediction. In the past few decades, more atmospheric data sets have been inferred from different techniques such as the Global Navigation Satellite System, satellite laser ranging and two-line-element catalogue. However, accurately predicting TMD is still a challenging task due to the limited knowledge of thermospheric dynamics and the lack of measurements with sufficient temporal and spatial resolution. In this research, a precise OP platform for the analysis and prediction of the orbital motion of satellite and and space debris is developed. It consists of various precise perturbation models of gravitational and non-gravitational forces. This includes the high-order Earth gravitational acceleration with the effect of solid and ocean tides, third-body perturbations from other celestial bodies in the solar system, the general relativity effects, aerodynamic acceleration, direct solar radiation pressure, and Earth's albedo and infrared radiation pressure. Coordinate transformation is established on the precise time systems and the measured Earth orientation parameters. The developed OP platform is validated against the historical precise orbits of LEO satellites. In order to evaluate the most representative classes of empirical TMD models, a comprehensive comparison of 12 models is performed. The vertical variability, horizontal scale and the capability to capture the physics-based features of the selected models are investigated. Various validations against the TMD estimated from on-board accelerometer measurements of the GRACE satellites have been conducted. The performance of these models in the OP of the GRACE-A satellite is assessed under different solar and geomagnetic conditions. Also discussed is the coupling effect between the TMD and ballistic coefficient that measures the impact of atmospheric friction on the space object. The impact of TMD variations on orbit dynamics of LEO objects is an important focus in this thesis, which has not been well-quantified in previous studies. Intra-annual, intra-diurnal and horizontal TMD variations are reproduced using the empirical model DTM-2013. Also evaluated are physics-based variations including the equatorial mass density anomaly (EMA) and midnight mass density maximum (MDM), which exhibit both temporal and spatial variations and are simulated by the Thermosphere Ionosphere Electrodynamics General Circulation Model. The analysis is based on the one-day OP simulation at 400 km. The result show that TMD variations have a dominant impact on the predicted orbits in the along-track direction. Semiannual and semidiurnal TMD variations exert the most significant impact on OP among the intra-annual and intra-diurnal variations, respectively. In addition, both EMA and MDM create weaker but still discernible impacts than other TMD variations. Some recommendations for TMD modelling are also presented. Moreover, precise modelling of TMD during geomagnetic quiet time is performed. This is undertaken using the TMD data inferred from GRACE (500 km), CHAMP (400 km) and GOCE (250 km) satellites during the year of 2002-2013. Three different methods including the Fourier analysis, spherical harmonic (SH) analysis and the artificial neural network (ANN) technique are adopted and compared in order to determine the most suitable methodology for the TMD modelling. Additionally, different combinations of time and coordinate representations are also examined in the TMD modelling. The results reveal that the precision of the low-order Fourier-based model can be improved by up to 10% using the geocentric solar magnetic coordinate. Both the Fourier- and SH-based models have drawbacks in approximating the vertical gradient of TMD. The ANN-based model, however, has the capability in capturing the vertical TMD variability and is not sensitive to the input of time and coordinate