This study uses over two years of 16 Hz density measurements, 50 Hz magnetic field data and ROTI data from the Swarm mission to perform long term statistics of plasma structuring in the polar ionosphere. The timeframe covers more than two years near the 24th solar cycle peak. We additionally use three years of data obtained from a timeframe close to solar minimum for discussion. We present power spectral densities (PSD) of electron density irregularities and magnetic field for one-minute intervals. These PSD have been characterized by the probability of a slope steepening, and by integrating the power deposited within frequency intervals corresponding to kilometer scales. For the electron density, we observe seasonal dependencies for both the integrated power and slope characteristics. While the dual slope probability, especially within the polar cap, varies with solar EUV-radiation, the integrated power is strongest around the equinoxes. Additionally, while we found similar results for the slope probability for both hemispheres, the integrated power exhibits strong hemispheric asymmetries with stronger enhancements within local summer in the southern hemisphere. The ROTI data shows a similar seasonal variability as the density PSD integrated power, in both seasonal dependency and interhemispheric variability. However, for the ROTI data the strongest fluctuations were found within the nightside auroral oval and the cusp. For the PSD of the magnetic field data, we obtain the strongest enhancements within the cusp for all seasons and all hemispheres. The fluctuations may indicate an increase in Alfvénic energy associated with a downward Poynting flux.

We investigate the role of auroral particle precipitation in small-scale (below hundreds of meters) plasma structuring in the auroral ionosphere over the Arctic. To the scope, we together analyse data recorded by an Ionospheric Scintillation Monitor Receiver (ISMR) of Global Navigation Satellite System (GNSS) signals and by an All-Sky Camera located in Longyearbyen, Svalbard (Norway). We leverage on the raw GNSS samples provided at 50 Hz by the ISMR to evaluate amplitude and phase scintillation indices at 1 s time resolution and the Ionosphere-Free Linear Combination at 20 ms time resolution. The simultaneous use of the 1 s GNSS-based scintillation indices allows identifying the scale size of the irregularities involved in plasma structuring in the range of small (up to few hundreds of meters) and medium-scale size ranges (up to few kilometers) for GNSS frequencies and observational geometry. Additionally, they allow identifying the diffractive and refractive nature of the found fluctuations on the recorded GNSS signals. Six strong auroral events and their effects on plasma structuring are studied. Plasma structuring down to scales of hundreds of meters are seen when strong gradients in auroral emissions at 557.7 nm cross the line of sight between the GNSS satellite and receiver. Local magnetic field measurements confirm small-scale structuring processes coinciding with intensification of ionospheric currents. Since 557.7 nm emissions primarily originate from the ionospheric E-region, plasma instabilities from particle precipitation at E-region altitudes are considered to be responsible for the signatures of small-scale plasma structuring highlighted in the GNSS scintillation data.

Abrupt changes in the solar wind dynamic pressure can greatly affect the Earth’s magnetosphere‐ionosphere system. We present an ionospheric flow vortex in the morning side during the sudden decrease in the solar wind dynamic pressure. The flow vortex was clearly observed by both the Hankasalmi radar and the azimuthal scan mode of the European Incoherent Scatter (EISCAT) Svalbard Radar (ESR). The flow vortex was first seen in the eastern field of view (FOV) of the Hankasalmi radar, and then propagated poleward and westward into the FOV of the ESR. During the passage of the flow vortex, a gradual decrease of electron density was observed by the field-aligned ESR 42 m antenna. When the equatorward directed ionospheric flow reached the ESR site, weak and visible increases in the electron density and electron temperature were observed. This impact was likely caused by soft electron precipitation associated with the clockwise flow vortex and upward field-aligned current. The azimuthal scan mode of the ESR 32 m radar at low elevation angle (30°) allowed us to measure key ionospheric parameters over a larger area (6° in latitude and 120° in azimuthal angle). The latitudinal scan of the electron temperature was used to derive the equatorward auroral boundary, which shows that the flow vortex was located in the subauroral region. We further demonstrated that it is possible to study the weak increase of electron density by using GPS total electron content (TEC) data. A minor TEC increase was observed near the center of the flow vortex.

A document by Pascal Sado. Click on the document to view its contents.

In the upper atmosphere (ionosphere), plasma with two electron populations is very common. The cold electrons (\(T_{e}\sim 1\) eV) usually originate in the ionosphere, while the hot electrons (\(T_{e}\sim 100\) eV) come from the magnetosphere. In addition to these two electron populations, there may be a beam of electrons streaming along the magnetic field lines. These electrons are responsible for exciting various electrostatic wave modes. In this article, we provide a systematic insight in the evolution of the system based on the beam energy.

Data assimilation (DA) techniques have recently gained traction in the ionospheric community, particularly at regional operational centers where more precise data are becoming prevalent. At centre stage is the argument over which technique or scheme merits realization. At 4DSpace, we have in-house developed and assessed the performance of two regional flavors of short-term forecast strong constraint four-dimensional (4D, space and time) variational (SC4DVar) DA schemes; the orthodox incremental (SC4DVar-Inc) and ensemble-based (SC4DEnVar) approach. SC4DVar-Inc is bottled-necked by expensive Tangent Linear Models (TLMs) and model Ad-joints (MAs), while SC4DEnVar design mitigates these limitations. Both schemes initialize from the same background (IRI-2016), and electron densities forward propagated (30-min) by a Gauss Markov filter- the densities take on a log-normal distribution to assert the mandatory ionosphere density positive definiteness. Preliminary assimilation is performed only with ubiquitous Global Navigation Satellite System observables from ground-based receivers, with a focus on moderately stable mid-latitudes, specifically the Japanese archipelago and neighboring areas. Using a simulation analysis, we find that under model space localization, 30 member Ensembles are sufficient for regional SC4DEnVar. Verification of reconstructions is with independent observations from ground-based ionosonde and satellite radio occultations: the performance of both schemes is fairly adequate during the quiet period when the background has a better estimation of the hmF2. SC4DVar-Inc is slightly better over areas densely populated with measurements, but SC4DEnVar estimates the overall 3D ionosphere picture better, particularly in remote areas and during severe conditions. These results warrant SC4DEnVar as a better candidate for precise short-time regional forecasts.

We develop a climatological model for the Northern Hemisphere based on a long-term dataset (2010-2021) of the rate of change of the total electron content (TEC) index (ROTI) maps from the International GNSS Service (IGS). The IGS ROTI maps are daily averaged in magnetic latitude and local time coordinates. To develop a climatological model, the ROTI maps are decomposed into a few base functions and coefficients using the empirical orthogonal function (EOF) method. The EOF method converges very quickly, and the first four EOFs reflect the majority (96%) of the total data variability. Furthermore, different EOF components can reflect different drivers of ionospheric irregularities. The first EOF reflects the averaged ROTI activity and the impact of the solar radiation and geomagnetic activity; the 2nd EOF reflects the impact of the interplanetary magnetic field (IMF) Bz and electric field; the 3rd and 4th EOFs reflect the dawn-dusk asymmetry around the auroral oval and polar cap, and they can be related to the IMF By. To build an empirical model, we fit the EOF coefficients using helio-geophysical indices from four different categories (solar activity; geomagnetic indices; IMF; the solar wind coupling function). The final EOF model is dependent on seven selected indices (F10.7P, Kp, Dst, Bt, By, Bz and Ekl). The statistical data-model comparisons show satisfactory results with a good correlation coefficient. However, the model cannot capture the significant expansion of the dayside ROTI activity during strong geomagnetic storms. Future effort is needed to provide corrections to the model for severe storms.

We show the first achievement of inferring the electron temperature in ionospheric conditions from synthetic data using fixed-bias Langmuir probes operating in the electron saturation region. This was done by using machine learning and altering the probe geometry. The electron temperature is inferred at the same rate as the currents are sampled by the probes. For inferring the electron temperature along with the electron density and the floating potential, a minimum number of three probes is required. Furthermore does one probe geometry need to be distinct from the other two, since otherwise the probe setup may be insensitive to temperature. This can be achieved by having either one shorter probe or a probe of a different geometry, e.g. two longer and a shorter cylindrical probe or two cylindrical probes and a spherical probe. We use synthetic plasma parameter data and calculate the synthetic collected probe currents to train a neural network and verify the results with a test set. We additionally verify the validity of the inferred temperature in altitudes ranging from about 100 km-500 km, using data from the International Reference Ionosphere model. Even minor changes in the probe sizing enable the temperature inference and result in root mean square relative errors between inferred and ground truth data of under 3%. When limiting the temperature inference to 120-450 km altitude an RMSRE of under 0.7% is achieved for all probe setups. In future, the multi-needle Langmuir Probe instrument dimensions can be adapted for higher temperature inference accuracy.

We develop an open source algorithm to apply Transfer learning to Aurora image classification and Magnetic disturbance Evaluation (TAME). For this purpose, we evaluate the performance of 80 pretrained neural networks using the Oslo Auroral THEMIS (OATH) data set of all-sky images, both in terms of runtime and their features’ predictive capability. From the features extracted by the best network, we retrain the last neural network layer using the Support Vector Machine (SVM) algorithm to distinguish between the labels “arc”, “diffuse”, “discrete”, “cloud”, “moon” and “clear sky / no aurora”. This transfer learning approach yields 73% accuracy in the six classes; if we aggregate the 3 auroral and 3 non-aurora classes, we achieve up to 91% accuracy. We apply our classifier to a new dataset of 550,000 images and evaluate the classifier based on these previously unseen images. To show the potential usefulness of our feature extractor and classifier, we investigate two test cases: First, we compare our predictions for the “cloudy” images to meteorological data and second we train a linear ridge model to predict perturbations in Earth’s locally measured magnetic field. We demonstrate that the classifier can be used as a filter to remove cloudy images from datasets and that the extracted features allow to predict magnetometer measurements. All procedures and algorithms used in this study are publicly available, and the code and classifier are provided, which opens possibility for large scale studies of all-sky images.

We present an investigation of polar cap plasma structure lifetimes. We analyze both simulated data from ionospheric models (International Reference Ionosphere model and Mass Spectrometer Incoherent Scatter model) and in-situ data from the Swarm satellite mission (the 16 Hz Advanced Plasma Density data set). We find that the theoretical prediction that E-region conductance is a predictor of F-region polar cap plasma structure lifetimes is indeed supported by both in-situ-based observations and by ionospheric models. In-situ plasma structure lifetimes correlate well with the ratio of F- to E-region conductance. We present explicit predictions of small scale (~1 km) structure lifetimes, which range from less than 1 hour during local summer to around 3 hours during local winter. We highlight a large discrepancy between the observational and theoretical scale-dependency of decay due to diffusion.

Ionospheric plasma irregularities can be successfully studied with the Swarm satellites. Parameters derived from the in situ plasma measurements and from the topside ionosphere total electron content provide a comprehensive dataset for characterising plasma structuring along the orbits of the Swarm satellites. The Ionospheric Plasma IRregularities (IPIR) data product summarizes these parameters and has already been used in studies related to structuring and variability of ionospheric plasma. We provide a detailed description of the algorithms behind the IPIR data product and demonstrate its use for ionospheric studies.

We classify all sky images from 4 seasons, transform the classification results into time-series data to include information about the evolution of images and combine these with information on the onset of geomagnetic substorms. We train a lightweight classifier on this dataset to predict the onset of substorms within a 15 minute interval after being shown information of 30 minutes of aurora. The best classifier achieves a balanced accuracy of 59% with a recall rate of 39% and false positive rate of 20%. We show that the classifier is limited by the strong imbalance in the dataset of approximately 50:1 between negative and positive events. All software and results are open source and freely available.