The auroral oval is the region receiving most magnetospheric particle precipitations and its boundaries can be highly dynamic and influenced by the solar wind and geomagnetic conditions. Polar orbiting Low Earth Orbit (LEO) satellites carrying high-precision magnetometers are suitable to derive Field-Aligned Currents (FACs) with tens of km-scale lengths flowing between the magnetosphere and ionosphere. Our previous work has used nearly 10 years FAC data from the CHAMP mission to construct an empirical model (called CH-Aurora-204) for estimating the location of the auroral oval boundaries. The model has been recently updated by taking nearly 5 years FAC data from the three Swarm satellites. Furthermore, magnetic data from other non-dedicated magnetic LEO missions, like Cryosat or e-PoP, are also used for improving the model predicts of the auroral oval boundaries.

Irregular fluctuations of the plasma density in the F region were first
detected as spread F, where the fluctuations cause coherent Bragg-type
scattering of HF and VHF radio signals. The irregularities and the spread F
phenomenon occur near the magnetic equator predominantly after sunset until
midnight as a result of plasma instabilities. Density irregularities affect
propagating radio signals also by producing scintillations, often in signals for
satellite communication and navigation, which has led to continued interest in
the topic. In situ measurements of the density with satellites have allowed us
to study in detail how the irregularities produce both spread F and
scintillations. Here we use measurements from the Langmuir Probes (LPs) on board the
three ESA Swarm satellites and data at 50 MHz from the Jicamarca Unattended
Long-term Investigation of the Ionosphere and Atmosphere (JULIA) radar at the
Jicamarca Radio Observatory (JRO) near Lima in Peru.

We present concurrent observations of JULIA and the LPs for the years 2014 to
2017. Results from this study show that in situ density fluctuations observed by
Swarm on magnetic flux tubes that pass over (or near ) Jicamarca Radio
Observatory (JRO) correlate well with the occurrence of spread F type radar
plumes at the JRO in the topside ionosphere. We apply the phase screen model to
estimate the effect of the irregularities on the JULIA radar signal. Significant
amplitude scintillations are so predicted, suggesting that the spread F
phenomenon is not only shaped by coherent Bragg-type scatterings from
irregularities, but also scintillations of the signal to and from the
reflections play a role.

The seasonal dependence of occurrence of ionospheric irregularities as obtained
from Swarm and JULIA were compared. Both data sets showed similar patterns
with the highest percentage of occurrence near December Solstice as well as
Equinoxes and low occurrence near June Solstice. Also the vertical drift
measurements from the JRO incoherent scatter radar show a similar seasonal
dependence, consistent with the explanation that topside ionospheric
irregularities are triggered by a pre-reversal enhancement of the vertical
drift.

The Earth’s ionosphere is often subject to instabilities and turbulence, which can create irregularities in plasma density at various scales and at all latitudes. Plasma density irregularities are one of the space weather effects, as they depend on the geomagnetic activity and can influence propagation of trans-ionospheric radio signals. As such they can increase the uncertainty of positioning with the Global Navigation Satellite Systems (GNSS), such as GPS, Galileo or GLONASS. Examples of measurable effects are radio wave scintillations in the phase and amplitude, which are significant issues at low geomagnetic latitudes and in the polar regions. Thus, a comprehensive characterization of ionospheric irregularities over all geomagnetic latitudes is important for both research and operations that rely on trans-ionospheric radio signals.

To study and characterize plasma irregularities at all latitudes, one can employ in-situ measurements by satellites in polar orbits, such as the European Space Agency’s Swarm mission. For the Swarm mission, we have developed the Ionospheric Plasma IRregularities (IPIR) product for a global characterisation of ionospheric irregularities along the whole satellite track. This new Level-2 data product combines complementary datasets from the Swarm satellites, i.e. the electron density from the electric field instrument, the GPS data from the onboard GPS receiver, and the magnetic data. The data product is a new tool for the global studies of ionospheric irregularities and turbulence.

We present the rationale behind the IPIR product and the first datasets. We analyse the results together with measurements from our ground-based scintillation receivers located in the Scandinavian Arctic and in Antarctica as well as optical instruments. Satellite in-situ data combined with the ground-based observations can provide better understanding of phenomena associated with the largest disturbances of the GNSS signals. Finally, we present case studies at different latitudes for various geomagnetic activity. The development has been carried out within the framework Swarm-DISC, Data, Innovation and Science Cluster consortium.

Recently a new method for monitoring the plasmapause location in the equatorial plane was introduced based on magnetic field observations made by LEO satellites in the topside ionosphere. Related signals are small-scale field-aligned currents (SSFACs, some 10km scale size). The equatorial boundary was SSFACs was found to be a good proxy for the location of the midnight plasmapause. The method has been applied to the CHAMP satellite, as well as the Swarm constellation of three identical satellites orbiting the Earth on polar orbits. The plasmapause is crossed around 60 times daily by each satellite. This makes this constellation very efficient in monitoring plasmapause dynamics. The
boundary can be clearly found on the night side, especially during disturbed conditions, while on the day side the signals related to the plasmapause are often masked by other phenomena (e.g. ULF waves). These observations are validated and calibrated using the in-situ plasma density observations of the NASA IMAGE satellite and Van Allen Probes, and based on the calibrated values, a plasmapause model is being constructed. The model, combined with recent Swarm observations, yield an estimate of the plasmapause location at any MLT.

Atmospheric whistlers are very low frequency (VLF) waves originating as electromagnetic lightning discharges and propagating through the magnetosphere. Whistlers detected on the ground have long been proven to be practical tools to measure electron densities in the plasmasphere around the Earth [1], an essential input in space weather modelling. Their fully automated analysis, however, was demonstrated only recently [2]. This automated inversion procedure has the additional advantage of inverting not just single whistlers, but also multiple path whistlers, which essentially provide several data points of an equatorial plasmaspheric electron density profile. The results of the procedure were demonstrated to agree with alternative plasmaspheric density measurements by satellites [3] in an ESA PECS funded study. The Automatic Whistler Detector and Analyser Network (AWDANet) was set up as a global ground-based network of VLF stations that automatically detect and analyse atmospheric whistlers based on the aforementioned procedure. The autonomous stations carry out whistler inversion in real time, providing plasmaspheric equatorial electron densities. The network has been in continuous operation since 2014, with a number of stations added since then. Continuous raw measurements at some of these sites started over ten years earlier. In order to extend the dataset, we have recently processed all data using GPU clusters, resulting in a long-term database of plasmaspheric electron densities.

In this talk we give a brief description of the network, the signal analysis and our calibrations using CLUSTER and Van Allen Probes satellite observations. We present the available data products in the obtained dataset, which spans fourteen years and involves fifteen ground station locations. We demonstrate how these data can be combined with modelling and/or other data sources on plasmaspheric densities to study the evolution of the plasmasphere over longer time scales or during individual space weather events [4][5].

To further demonstrate its rich possibilities, we show an example of our most recent scientific result based on this dataset. Our long-term correlation study with terrestrial lightning sources (using the WWLLN global lightning database) precisely identified the geographical source region of whistlers for the first time, conforming to earlier expectations from theory. This in turn allows the separation of the source effect from ionospheric and magnetospheric effects on VLF wave transmissions.

[1] Helliwell, R. A. (1965). Whistlers and related ionospheric phenomena. Stanford : Stanford University Press.

[2] Lichtenberger, J. (2009). A new whistler inversion method. Journal of Geophysical Research (Space Physics), 114 , A07222. 10.1029/2008JA013799

[3] Koronczay, D. et al. (2018). VLF transmitters as tools for monitoring the plasmasphere. Journal of Geophysical Research (Space Physics). 10.1029/2018JA025802

[4] Jorgensen, A. et al. (2015). Specification of the Plasmasphere by Combining Models with Observations in a Data Assimilation Framework. Inner Magnetosphere Coupling III, Los Angeles, USA

[5] Koronczay, D. et al. (2015). Data assimilation with plasmaspheric density measurements from VLF whistlers: preliminary results. 12th International School/Symposium for Space Simulations, Prague, Czech Republic. 10.13140/RG.2.2.11113.34408

Solar proton events (SPEs, defined as those that meet or exceed a proton flux of 10 pfu at >10 MeV energies) are one of the major challenging problems for modern (technological) society due to their unpredictability. Indeed, they have impacts on space systems (spacecraft, instruments, electronic components, ...), avionics and living organisms (e.g., Malandraki & Crosby, 2017).

A prime focus of the ESPERTA (Empirical model for Solar Proton Event Real Time Alert) forecast tool is to provide timely warnings of SPE events and the severity of their terrestrial impacts, within 10 minutes of the associated flare soft X-ray (SXR) maximum (Laurenza et al., 2009). Within this time constraint, ESPERTA is based on input flare data (flare location, flare 1-8 Å SXR fluence, and flare 1 MHz radio fluence) that are, or could be made, available in real time. These three parameters provide information on proton propagation, solar event energy, and particle escape, respectively.

ESPERTA provided good verification measures (Probability of Detection, POD=63%, and False Alarme Rate, FAR=38%) for the 1995 – 2014 interval (Laurenza et al., 2009, Alberti et al., 2017) with median (average) warning time for the 66 SPE events with peak fluxes ≥10 pfu that were correctly predicted from 1995 to 2014, was 4.8 hr (~9 hr), in the range 0.4 – 52.8 hr, exceeding those obtained by other methods (e.g., Núñez, 2011 average warning time of ~5 hr).

ESPERTA has been adapted in predicting solar proton events from moderate to extreme intensities (i.e., ≥100 pfu) which produce the most significant biological and space operation impacts, with increased effects on HF propagation in the polar regions (Laurenza et al., 2018). We obtained a POD of 75% (41/55) and a FAR of 24% (13/54) for the 1995–2014 interval with a median (minimum) WT of ~1.7 (0.2) hr based on predictions made at the time of the S1 threshold crossing. The improved performance of ESPERTA for ≥S2 events is a reflection of the big flare syndrome, which postulates that the measures of the various manifestations of eruptive solar flares increase as one considers increasingly larger events. Here, we also present results from both versions of the ESPERTA model on the more recent events from 2015 to 2017 confirming their good performance.

References

O. Malandraki & N. B. Crosby 2018, Astrophysics and Space Science Library, 444, pp. 203.

M. Laurenza et al. 2009, SpWea ,7, S04008.

T. Alberti et al 2017, ApJ, 838, 59.

M. Laurenza et al 2018, ApJ, 857, 107.