The Ionosphere Prediction Service

Albanese, C.; Rodriguez, Filippo; Ronchini, Roberto; Rollo, Stefano Di; Berrilli, F.; Cristaldi, Antonio; Moro, D. Del; Forte, Roberta; Franceschi, Giorgiana De; Cesaroni, Claudio; Spogli, Luca; Romano, Vincenzo; Aquino, Márcio; Veettil, Sreeja Vadakke; Kalden, Osman; Hutchinson, Michael; Guyader, Eric

doi:10.1017/s174392131800025x

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Making use of the forecasted Kp provided by NOAA, the model is currently implemented as a real-time service in the Ionosphere Prediction Service (IPS) prototype (Albanese et al, 2017;Rodriguez et al, 2018;Veettil et al, 2019), available at http://ips.gsc-europa.eu. In addition, products of the realtime operational model implemented at Istituto Nazionale di Geofisica e Vulcanologia are provided to the Pan-European Consortium for Aviation Space Weather User Services (PECASUS, http://pecasus.eu/), as one of the key products in the GNSS expert group.…”

Section: Discussionmentioning

confidence: 99%

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Neural network based model for global Total Electron Content forecasting

Cesaroni

Spogli

Aragón‐Àngel

et al. 2020

J. Space Weather Space Clim.

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We introduce a novel empirical model to forecast, 24 h in advance, the Total Electron Content (TEC) at global scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 h single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE, as it has been found to range between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-h forecasting technique even over the oceans. The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.

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Section: Discussionmentioning

confidence: 99%

“…The results are finally discussed in Section 4 together with the limitations of the model and concluding remarks on the operational performance of the proposed forecasting approach within the Ionosphere Prediction Service (IPS) prototype (Albanese et al, 2017;Rodriguez et al, 2018;Veettil et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neural network based model for global Total Electron Content forecasting

Cesaroni

Spogli

Aragón‐Àngel

et al. 2020

J. Space Weather Space Clim.

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“…Finally, we chose the prediction of the phase SI, σ Φ , as our predictive task due to the actionable information for GNSS users provided by such a prediction (e.g., Albanese et al, ; Prikryl et al, ; Sreeja et al, ; Strangeways, ), the availability of large volumes of SI data, and the body of literature surrounding this task (e.g., Spogli et al, ; Prikryl et al, ; Rezende et al, , and references therein). Other useful approaches to ionospheric scintillation prediction also exist.…”

Section: Discussionmentioning

confidence: 99%

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

et al. 2018

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As societal dependence on transionospheric radio signals grows, space weather impact on these signals becomes increasingly important yet our understanding of the effects remains inadequate. This challenge is particularly acute at high latitudes where the effects of space weather are most direct and no reliable predictive capability exists. We take advantage of a large volume of data from Global Navigation Satellite Systems (GNSS) signals, increasingly sophisticated tools for data-driven discovery, and a machine learning algorithm known as the support vector machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation. This work, to our knowledge, represents the first time an SVM model has been created to predict high-latitude phase scintillation. We use the true skill score to evaluate the SVM model and to establish a benchmark for high-latitude ionospheric phase scintillation prediction. The SVM model significantly outperforms persistence (i.e., current and future scintillation are identical), doubling the predictive skill according to the true skill score for a 1-hr lead time. For a 3-hr lead time, persistence is comparable to a random chance prediction, suggesting that the memory of the ionosphere in terms of high-latitude plasma irregularities is on the order of, or shorter than, a few hours. The SVM model predictive skill only slightly decreases between the 1-and 3-hr predictive tasks, pointing to the potential of this method. Our findings can serve as a foundation on which to evaluate future predictive models, a critical development toward the resolution of space weather impact on transionospheric radio signals.Plain Language Summary Society is increasingly dependent on radio signals, particularly those from the Global Navigation Satellite Systems (GNSS), and the technologies (e.g., navigation and financial transactions) that they enable. The integrity and reliability of these signals is threatened by their travel from the GNSS satellites to the ground, which includes passage through a charged region between 100 and 1,000 km known as the ionosphere. Disturbances to the ionosphere from solar energy, or space weather, cause variations in GNSS signals that adversely affect the dependent systems and technologies. Currently, the effect of the ionosphere on these signals cannot be reliably predicted, and the challenge is particularly important at latitudes above 45 ∘ where space weather impacts are most direct. We have compiled a large volume of data from the regions important to space weather (i.e., from the Sun to the Earth) to develop a novel machine learning model capable of skillfully predicting disruptions to GNSS signals at high latitudes.To our knowledge, this model is the first of its kind. We find that the new model is capable of more accurate predictions than current methods and position this model as a benchmark on which future predictive models can be measured.

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“…Similar O2R activities have been an important aspect also within Space Weather projects in which the Italian community has been involved, allowing science to respond to ideas coming, for example, from Space Weather risk assessments and mitigation plans. A pertinent example is the Ionosphere Prediction Service (IPS) project (2015-2017) of the European Commission in the frame of the Galileo programme (Albanese et al, 2018), where research activities were strongly linked to the provided Space Weather service (Rodriguez et al, 2018). Additionally, the PECASUS (Pan-European Consortium for Aviation Space Weather User Services) initiative aims for a global Space Weather information service center as specified by the International Civil Aviation Organisation (ICAO).…”

Section: Topic Area N 4: Particle Radiationmentioning

confidence: 99%

“…Note 5.8. Telespazio coordinated the Ionosphere Prediction Service (IPS) project (2015-2017) of the European Commission in the frame of the Galileo programme (Albanese et al, 2018). The aim of the IPS project was strongly related to Space Weather: in particular, the project scope was to design and develop a prototype platform to translate the prediction and forecast of the ionosphere effects into a service customized for specific Global Navigation Satellite System (GNSS) user communities.…”

Section: Appendix Amentioning

confidence: 99%

Current state and perspectives of Space Weather science in Italy

Plainaki

Antonucci²,

Бемпорад

et al. 2020

J. Space Weather Space Clim.

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Italian teams have been involved many times in Space Weather observational campaigns from space and from the ground, contributing in the advancing of our knowledge on the properties and evolution of the related phenomena. Numerous Space Weather forecasting and now-casting modeling efforts have resulted in a remarkable add-on to the overall progress in the field, at both national and international level. The Italian Space Agency has participated several times in space missions with science objectives related to Space Weather; indeed, an important field for the Italian scientific and industrial communities interested in Heliophysics and Space Weather, is the development of new instrumentation for future space missions. In this paper, we present a brief state-of-the-art in Space Weather science in Italy and we discuss some ideas on a long-term plan for the support of future scientific research in the related disciplines. In the context of the current roadmap, the Italian Space Agency aims to assess the possibility to develop a national scientific Space Weather data centre to encourage synergies between different science teams with interest in the field and to motivate innovation and new mission concept development. Alongside with the proposed recommendations, we also discuss how the Italian expertise could complement international efforts in a wider international Space Weather context.

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The Ionosphere Prediction Service

Cited by 4 publications

References 14 publications

Neural network based model for global Total Electron Content forecasting

Neural network based model for global Total Electron Content forecasting

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

Current state and perspectives of Space Weather science in Italy

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