Space weather studies of IONOLAB group

IONOLAB is an interdisciplinary research group dedicated for handling the challenges of near earth environment on communication, positioning and remote sensing systems. IONOLAB group contributes to the space weather studies by developing state-of-the-art analysis and imaging techniques. On the website of IONOLAB group, www.ionolab.org, four unique space weather services, namely, IONOLAB-TEC, IRI-PLAS-2015, IRI-PLAS-MAP and IRI-PLAS-STEC, are provided in a user friendly graphical interface unit. Newly developed algorithm for ionospheric tomography, IONOLAB-CIT, provides not only 3-D electron density but also tracking of ionospheric state with high reliability and fidelity. The algorithm for ray tracing through ionosphere, IONOLAB-RAY, provides a simulation environment in all communication bands. The background ionosphere is generated in voxels where IRI-Plas electron density is used to obtain refractive index. One unique feature is the possible update of ionospheric state by insertion of Total Electron Content (TEC) values into IRI-Plas. Both ordinary and extraordinary paths can be traced with high ray and low ray scenarios for any desired date, time and transmitter location. 2-D regional interpolation and mapping algorithm, IONOLAB-MAP, is another tool of IONOLAB group where automatic TEC maps with Kriging algorithm are generated from GPS network with high spatio-temporal resolution. IONOLAB group continues its studies in all aspects of ionospheric and plasmaspheric signal propagation, imaging and mapping. © 2016 IEEE

Estimation of 3D electron density in the Ionosphere by using fusion of GPS satellite-receiver network measurements and IRI-Plas model

GPS systems can give a good approximation of the Slant Total Electron Content in a cylindrical path between the GPS satellite and the receiver. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model can also give an estimation of the vertical electron density profile in the ionosphere for any given location and time, in the altitude range from about 50 km to 20000 km. This information can be utilized to obtain total electron content between any given receiver and satellite locations based on the IRI-Plas model. This paper explains how the fusion of measurements obtained from a GPS satellite-receiver network can be utilized together with the IRI-Plas model in order to obtain a robust 3D electron density model of the ionosphere. © 2013 ISIF ( Intl Society of Information Fusi

Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

Space-time interpolation and automatic mapping of TEC using TNPGN-active

Turkish National Permanent GPS Network (TNPGN) is the Reference Station Network of 146 continuously-operating GNSS stations o which are distributed uniformly across Turkey and North Cyprus Turkish Republic since May 2009. IONOLAB group, formed by researchers and students in Hacettepe University, Bilkent University and General Command of Mapping is currently investigating new techniques for space-time interpolation, and automatic mapping of TEC through a TUBITAK research grant. This study presents the developments in monitoring of space weather, and correction of geodetic positioning errors due to ionosphere using TNPGN. © 2011 IEEE

The ESPAS e-infrastructure

ESPAS provides an e-Infrastructure to support access to a wide range of archived observations and model derived data for the near-Earth space environment, extending from the Earth's middle atmosphere up to the outer radiation belts. To this end, ESPAS will serve as a central access hub for researchers who wish to exploit multi-instrument multipoint data for scientific discovery, model development and validation, and data assimilation, among others. Observation based and model enhanced scientific understanding of the physical state of the Earth's space environment and its evolution is critical to advancing space weather and space climate studies, two very active branches of current scientific research. ESPAS offers an interoperable data infrastructure that enables users to find, access, and exploit near-Earth space environment observations from ground-based and spaceborne instruments and data from relevant models, obtained from distributed repositories. In order to facilitate efficient user queries ESPAS allows a highly flexible workflow scheme to select and request the desired data sets. ESPAS has the strategic goal of making Europe a leading player in the efficient use and dissemination of near-Earth space environment information offered by institutions, laboratories and research teams in Europe and worldwide, that are active in collecting, processing and distributing scientific data. Therefore, ESPAS is committed to support and foster new data providers who wish to promote the easy use of their data and models by the research community via a central access framework. ESPAS is open to all potential users interested in near-Earth space environment data, including those who are active in basic scientific research, technical or operational development and commercial applications

Solar-Terrestrial Science Strategy Workshop

The conclusions and recommendations reached at the Solar Terrestrial Science Strategy Workshop are summarized. The charter given to this diverse group was: (1) to establish the level of scientific understanding to be accomplished with the completion of the current and near term worldwide programs; (2) identify the significant scientific questions to be answered by future solar terrestrial programs, and the programs required to answer these questions; and (3) map out a program strategy, taking into consideration currently perceived space capabilities and constraints, to accomplish the identified program

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)

Analysis and Quality Assessment of LEO GPS Data for Geophysical and Ionospheric Applications

During the last few years, an ever-increasing fleet of Low Earth Orbiting (LEO) satellites for scientific purposes became operative. Most of these satellites carry dual-frequency Global Positioning System (GPS) receivers. The highly accurate dual-frequency observations allow mitigating the ionospheric signal contribution to estimate precise orbits and eventually the earth's gravity field. However, when comparing the obtained GPS only gravity fields derived from Swarm to gravity field solutions obtained by the dedicated gravity field mission GRACE, systematic band-shaped differences are visible in the vicinity of the geomagnetic equator. In this work, an empirical approach for the appropriate weighting of GPS observations is derived to mitigate these ionospheric artifacts. The cause of the artifacts is further analyzed by investigating the loop filter implementation. A tracking loop-specific transfer function is derived and used to invert the loop filter response to derive corrections for the GPS phase observations. Both methods are evaluated to achieve the best possible Swarm GPS only gravity field. Vice versa, the collected GPS observations from the LEO precise orbit determination antenna can also be used to gain insight into the topside ionosphere and plasmasphere. A three-dimensional model approach is developed using a fleet of LEO satellites to estimate a model of the electron density distribution between LEO and GPS satellites. Both aspects represent possibilities of using GPS/GNSS on-board of LEO satellites for geophysical applications

FY 1986 scientific and technical reports, articles, papers and presentations

Formal NASA technical reports, papers published in technical journals, and presentations by Marshall Space Flight Center (MSFC) personnel in FY-86 are presented. Also included are papers of MSFC contractors