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)

Total electron content PCA-NN model for middle latitudes

A regression-based model was previously developed to forecast the total electron content (TEC) at middle latitudes. We present a more sophisticated model using neural networks (NN) instead of linear regression. This regional model prototype simulates and forecasts TEC variations in relation to space weather conditions. The development of a prototype consisted of the selection of the best set of predictors, NN architecture and the length of the input series. Tests made using the data from December 2014 to June 2018 show that the PCA-NN model based on a simple feed-forward NN with a very limited number (up to 6) of space weather predictors performs better than the PCA-MRM model that uses up to 27 space weather predictors. The prototype is developed on a TEC series obtained from a GNSS receiver at Lisbon airport and tested on TEC series from three other locations at middle altitudes of the Eastern North Atlantic. Conclusions on the dependence of the forecast quality on longitude and latitude are made.Comment: arXiv admin note: text overlap with arXiv:2201.0347

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data

A NOVEL PATH LOSS FORECAST MODEL TO SUPPORT DIGITAL TWINS FOR HIGH FREQUENCY COMMUNICATIONS NETWORKS

The need for long-distance High Frequency (HF) communications in the 3-30 MHz frequency range seemed to diminish at the end of the 20th century with the advent of space-based communications and the spread of fiber optic-connected digital networks. Renewed interest in HF has emerged as an enabler for operations in austere locations and for its ability to serve as a redundant link when space-based and terrestrial communication channels fail. Communications system designers can create a “digital twin” system to explore the operational advantages and constraints of the new capability. Existing wireless channel models can adequately simulate communication channel conditions with enough fidelity to support digital twin simulations, but only when the transmitter and receiver have clear line of sight or a relatively simple multi-path reflection between them. With over-the-horizon communications, the received signal depends on refractions of the transmitted signal through ionospheric layers. The time-varying nature of the free electron density of the ionosphere affects the resulting path loss between the transmitter and receiver and is difficult to model over several days. This dissertation examined previous efforts to characterize the ionosphere and to develop HF propagation models, including the Voice of America Coverage Analysis Prediction (VOACAP) tool, to support path loss forecasts. Analysis of data from the Weak Signal Propagation Reporter Network (WSPRnet), showed an average Root Mean Squared Error (RMSE) of 12.9 dB between VOACAP predictions and actual propagation reports on the WSPRnet system. To address the significant error in VOACAP forecasts, alternative predictive models were developed, including the Forecasting Ionosphere-Induced Path Loss (FIIPL) model and evaluated against one month of WSPRnet data collected at eight geographically distributed sites. The FIIPL model leveraged a machine learning algorithm, Long Short Term Memory, to generate predictions that reduced the SNR errors to an average of 4.0 dB RMSE. These results could support more accurate 24-hour predictions and provides an accurate model of the channel conditions for digital twin simulations. Advisor: Hamid R. Sharif-Kashan

Pre-Earthquake Ionospheric Perturbation Identification Using CSES Data \u3cem\u3evia\u3c/em\u3e Transfer Learning

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data

Imaging ionospheric irregularities by earth observation radar satellite

The sensitivity of Synthetic Aperture Radar (SAR) satellite signal in the L-band to ionospheric plasma density is used to obtain two-dimensional imaging of ionospheric density irregularities. As an application for equatorial ionosphere, we have recently reported first simultaneous observation of equatorial plasma bubble by the ALOS-2/PALSAR-2 satellite and a ground 630-nm airglow imager in northern Brazil. In this case, SAR ionospheric scintillation are represented as stripe-like signature of radar image over the terrain along the local magnetic field lines near an airglow depletion region. This so-called SAR scintillation stripes are discussed to be the signature of existing small-scale plasma irregularities with the scale size of hundreds of meters associated with equatorial plasma bubbles. We present the observational setup and the interpretation of SAR signal parameters to characterize the two-dimensional ionospheric density structures, and discuss future studies

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

Application of convolution neural networks for critical frequency fₒF2 prediction

Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies on a regression approach to the prediction based on the known daily quasi-periodicity of ionospheric parameters related to solar illumination. Algorithmically, this approach is implemented in the form of convolutional neural networks with two-dimensional convolution. The input data for the analysis is presented as two-dimensional solar time — date matrices. The model was trained on data from the mid-latitude ionosonde in Irkutsk (RF) and tested using data from several mid-latitude ionosondes: Arti (RF), Warsaw (Poland), Mohe (China). It is shown that the main contribution to the prediction value of fₒF2 is made by the data on the nearest few days before the prediction; the contribution of the remaining days strongly decreases. This model has the following forecast quality metrics (Pearson correlation coefficient 0.928, root mean square error 0.598 MHz, mean absolute error in percent 10.45 %, coefficient of determination 0.861) and can be applied to fₒF2 forecast in middle latitudes