Probabilistic hazard assessment: Application to geomagnetic activity

Probabilistic Hazard Assessment (PHA) provides an appropriate methodology for assessing space weather hazard and its impact on technology. PHA is widely used in the geosciences to determine the probability of exceedance of critical thresholds, caused by one or more hazard sources. PHA has proved useful where there are limited historical data to estimate the likelihood of specific impacts. PHA has also driven the development of empirical and physical models, or ensembles of models, to replace measured data. Here we aim to highlight the PHA method to the space weather community and provide an example of it could be used. In terms of space weather impact, the critical hazard thresholds might include the Geomagnetically Induced Current in a specific high voltage power transformer neutral, or the local pipe-to-soil potential in a particular metal pipe. We illustrate PHA in the space weather context by applying it to a twelve-year dataset of Earth-directed solar Coronal Mass Ejections (CME), which we relate to the probability that the global three-hourly geomagnetic activity index K p exceeds specific thresholds. We call this a ‘Probabilistic Geomagnetic Hazard Assessment’, or PGHA. This provides a simple but concrete example of the method. We find that the cumulative probability of K p > 6-, > 7-, > 8- and K p = 9o is 0.359, 0.227, 0.090, 0.011, respectively, following observation of an Earth-directed CME, summed over all CME launch speeds and solar source locations. This represents an order of magnitude increase in the a priori probability of exceeding these thresholds, according to the historical K p distribution. For the lower Kp thresholds, the results are distorted somewhat by our exclusion of coronal hole high speed stream effects. The PGHA also reveals useful (for operational forecasters) probabilistic associations between solar source location and subsequent maximum Kp .

The Great Aurora of 4 February 1872 observed by Angelo Secchi in Rome

Observation of auroras at low latitudes is an extremely rare event typically associated with major magnetic storms due to intense Earth-directed Coronal Mass Ejections. Since these energetic events represent one of the most important components of space weather their study is of paramount importance to understand the Sun-Earth connection. Due to the rarity of these events, being able to access all available information for the few cases studied is equally important. Especially if we refer to historical periods in which current accurate observations from ground-based instruments or from space were not available. Certainly, among these events we must include the great aurora of February 4, 1872. An event whose effects have been observed in different regions of the Earth. What we could consider today a global event, especially for its effects on the communication systems of the time, such as the transatlantic cable that allowed a connection between the United States and Europe since 1866. In this paper we describe the main results of the observations and studies carried out by Angelo Secchi at the Observatory of the Roman College and described in his Memoria sull’Aurora Elettrica del 4 Febbraio 1872 for the Notes of the Pontifical Academy of new Lincei. This note is extremely modern both in its multi-instrumental approach to the study of these phenomena and in its association between solar-terrestrial connection and technological infrastructures on the Earth. The Secchi's note definitely represents the first example of analysis and study of an event on a global scale, such as the Atlantic cable, affecting the Earth. What we nowadays call an extreme space weather event.

An operational approach to forecast the Earth’s radiation belts dynamics

The Ca index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a ground-based measurement with extensive historical records. In this work, we propose a data-driven model to forecast Ca up to 24 hours in advance from near-Earth solar wind parameters. Our model relies mainly on a recurrent neural network architecture called Long Short Term Memory that has shown good performances in forecasting other geomagnetic indices in previous papers. Most implementation choices in this study were arbitrated from the point of view of a space system operator, including the data selection and split, the definition of a binary classification threshold, and the evaluation methodology. We evaluate our model (against a linear baseline) using both classical and novel (in the space weather field) measures. In particular, we use the Temporal Distortion Mix (TDM) to assess the propensity of two time series to exhibit time lags. We also evaluate the ability of our model to detect storm onsets during quiet periods. It is shown that our model has high overall accuracy, with evaluation measures deteriorating in a smooth and slow trend over time. However, using the TDM and binary classification forecast evaluation metrics, we show that the forecasts lose some of their usefulness in an operational context even for time horizons shorter than 6 hours. This behaviour was not observable when evaluating the model only with metrics such as the root-mean-square error or the Pearson linear correlation. Considering the physics of the problem, this result is not surprising and suggests that the use of more spatially remote data (such as solar imaging) could improve space weather forecasts.

A new climatological electron density model for supporting space weather services

The ionosphere is the ionized part of the Earth atmosphere, ranging from about 60 km up to several Earth radii whereas the upper part above about 1000 km height up to the plasmapause is usually called the plasmasphere. We present a new three-dimensional electron density model aiming for supporting space weather services and mitigation of propagation errors for trans-ionospheric signals. The model is developed by superposing the Neustrelitz Plasmasphere Model (NPSM) to an ionosphere model composed of separate F and E-layer distributions. It uses the Neustrelitz TEC model (NTCM), Neustrelitz Peak Density Model (NPDM) and the Neustrelitz Peak Height Model (NPHM) for the total electron content (TEC), peak ionization and peak height information. These models describe the spatial and temporal variability of the key parameters as function of local time, geographic/geomagnetic location, solar irradiation and activity. The model is particularly developed to calculate the electron concentration at any given location and time in the ionosphere for trans-ionospheric applications and named as the Neustrelitz Electron Density Model (NEDM2020). A comprehensive validation study is conducted against electron density in-situ data from DMSP and Swarm, Van Allen Probes and ICON missions, and topside TEC data from COSMIC/FORMOSAT-3 mission, bottom side TEC data from TOPEX/Poseidon mission and ground-based TEC data from International GNSS Service (IGS) covering both high and low solar activity conditions. Additionally, the model performance is compared with the 3D electron density model NeQuick2. Our investigation shows that the NEDM2020 performs better than the NeQuick2 when compared with the in-situ data from Van Allen Probes and ICON satellites and TEC data from COSMIC and TOPEX/Poseidon missions. When compared with DMSP and IGS TEC data both NEDM2020 and NeQuick2 perform very similarly.

Solar Energetic Particle Event occurrence prediction using Solar Flare Soft X-ray measurements and Machine Learning

The prediction of the occurrence of Solar Energetic Particle (SEP) events has been investigated over many years and multiple works have presented significant advances in this problem. The accurate and timely prediction of SEPs is of interest to the scientific community as well as mission designers, operators, and industrial partners due to the threat SEPs pose to satellites, spacecrafts and crewed missions. In this work we present a methodology for the prediction of SEPs from the soft X-rays of solar flares associated with SEPs that were measured in 1 AU. We use an expansive dataset covering 25 years of solar activity, 1988-2013, which includes thousands of flares and more than two hundred identified and catalogued SEPs. Neural networks are employed as the predictors in the model providing probabilities for the occurrence or not of an SEP which are converted to yes/no predictions. The neural networks are designed using current and state-of the-art tools integrating recent advances in the machine learning field. The results of the methodology are extensively evaluated and validated using all the available data and it is shown that we achieve very good levels of accuracy with correct SEP occurrence prediction higher than 85% and correct no-SEP predictions higher than 92%. Finally we discuss further work towards potential improvements and the applicability of our model in real life conditions.

Combining Swarm Langmuir probe observations, LEO-POD-based and ground-based GNSS receivers and ionosondes for prompt detection of ionospheric earthquake and tsunami signatures: case study of 2015 Chile-Illapel event.

The study investigates ionospheric electric field responses to the earthquake (EQ) of magnitude 8.3, and to the related seismic activity and tsunami triggered by the mainshock in Chile-Illapel region, at 22:54 UTC, in the evening of 16.09.2015. The work is a wider review of available ground and satellite data and techniques available in detection of seismically induced traveling ionospheric disturbances (TID) and irregularities of smaller scale. The data used in the experiment includes several types of ground and satellite observations from low-Earth orbit (LEO) satellites. The number of techniques applied here is also extended and includes spectral analysis of LEO along-track data and composed analysis of ground GNSS data. The timeframe of the analyses is focused on 16.09 and 17.09.2015, but also extended to several adjacent days, where an enhanced seismic activity has been recorded. Several examples of seismically triggered TIDs are shown, as detected by combined observations from more than one source and with the application of different methods, including spectral analysis. These disturbances occur before the mainshock, just after, or in time following this large EQ, and can be found in close neighborhood of Chile-Illapel or far away from the epicenter. The objective of the work was to demonstrate increasing number of available data and techniques, which can be limited when applied alone, but their combination can provide many advantages in the analysis of seismically disturbed ionosphere. The combination of LEO satellite data reaching all regions of the globe with local, but dense ground-based GNSS data and ionospheric HF sounders looks promising, especially in view of nearby availability of CubeSat constellations equipped with instruments for ionosphere sounding. An important conclusion coming from the study is a need for spectral analysis techniques in the processing of LEO along-track data and requirement of the validation of LEO observations with separate LEO data or ground-based data. A general, but key finding refers to the complementarities of different observations of ionospheric electric field, which is critically important in case of analyzing ionospheric irregularities in the extended and composed ionosphere, especially if not every sounding direction can successfully find it.

Sensing the ionosphere with the Spire radio occultation constellation

Radio occultation (RO) provides a cost-effective component of the overall sensor mix required to characterise the ionosphere over wide areas and in areas where it is not possible to deploy ground sensors. The paper provides a description of the RO constellation that has been developed and deployed by Spire Global. This constellation and its associated ground infrastructure is now producing data that can be used to characterise the bulk ionosphere, lower ionosphere perturbations and ionospheric scintillation.

ORFEES - a radio spectrograph for the study of solar radio bursts and space weather applications

Radio bursts are sensitive tracers of non-thermal electron populations in the solar corona. They are produced by electron beams and shock waves propagating through the corona and the Heliosphere, and by trapped electron populations in coronal mass ejections (CMEs) and in quiescent active regions. Combining space borne and ground-based radio spectrographs allows one to track disturbances all the way between the low corona, near or at the sites of particle acceleration, and the spacecraft. Radio observations are therefore a significant tool in probing the solar origin of heliospheric disturbances, which is a central research topic as witnessed by the Parker Solar Probe and Solar Orbiter missions. The full scientific return of these projects needs vigorous ground-based support, which at radio wavelengths covers altitudes up to about a solar radius above the photosphere. Besides research in solar and heliospheric physics, monitoring solar radio bursts also supports space weather services. On occasion radio bursts can themselves be a space weather hazard. The Nan\c{c}ay radio astronomy station in central France has a long tradition of monitoring radio emission at decimetre-to-metre wavelengths. This article describes the radio spectrograph ORFEES ({\it Observations Radiospectrographiques pour FEDOME et l'Etude des Eruptions Solaires}). It observes the whole-Sun flux density between 144 and 1004 MHz, which pertains to regions between the low corona and about half a solar radius above the photosphere. ORFEES is the result of a partnership between Observatoire de Paris and the French Air Force, which operates the experimental space weather service FEDOME. The primary use of the instrument at Paris Observatory is the astrophysical observation. Low-resolution data with rapid availability are presently produced for the French Air Force. Similar information can be made available to a broader range of space-weather service providers. This article gives an overview of the instrument design and the access to the data, and shows a few illustrative observations.

OMEP-EOR: A MeV proton flux specification model for Electric Orbit Raising missions

Electric Orbit Raising (EOR) for telecommunication satellites has allowed significant reduction in on-board fuel mass, at the price of extended transfer durations. These relatively long transfers, which usually span a few months, cross large spans of the radiation belts, resulting in significant exposure of the spacecraft to space radiations. Since they are not very populated, the radiation environment of intermediate regions of the radiation belts is less constrained than on popular orbits such as LEO or GEO on standard environment models. In particular, there is a need for more specific models for the MeV energy range proton fluxes, responsible for solar arrays degradations, and hence critical for EOR missions. As part of the ESA ARTES program, ONERA has developed a specification model of proton fluxes dedicated for EOR missions. This model is able to estimate the average proton fluxes between 60 keV and 20MeV on arbitrary trajectories on the typical durations of EOR transfers. A global statistical model of the radiation belts was extracted from the Van Allen Probes (RBSP) RBSPICE data. For regions with no or low sampling, simulation results from the Salammbô radiation belt model were used. A special care was taken to model the temporal dynamics of the belts on the considered mission durations. A Gaussian Process (GP) model was developed, allowing to compute analytically the distribution of the average fluxes on arbitrary mission durations. Satellites trajectories can be flown in the resulting global distribution, yielding the proton flux spectrum distribution as seen by the spacecraft. We show results of the model on a typical EOR trajectory. The obtained fluxes are compared to the standard AP8 model, the AP9 model, and validated using the THEMIS satellites data.We illustrate the expected e ect on solar cell degradation, where our model is showing an increase of up to 20% degradation prediction compared to AP8.

Space Weather research in the Digital Age and across the full data lifecycle: Introduction to the Topical Issue

The onset and rapid advance of the Digital Age have brought challenges and opportunities for scientific research characterized by a continuously evolving data landscape reflected in the four V’s of big data: volume, variety, veracity, and velocity. The big data landscape supersedes traditional means of storage, processing, management, and exploration, and requires adaptation and innovation across the full data lifecycle (i.e., collection, storage and processing, analytics, and representation). The Topical Issue ``Space Weather research in the Digital Age and across the full data lifecycle'' collects research from across the full data lifecycle (collection, management, analysis, and communication; collectively `Data Science') and offers a tractable compendium that illustrates the latest computational and data science trends, tools, and advances for Space Weather research. We introduce the paradigm shift in Space Weather and the articles in the Topical Issue. We create a network view of the research that highlights the contribution to the change of paradigm and reveals the trends that will guide it hereafter.