Current inference techniques for processing multi-needle Langmuir Probe (m-NLP) data are often based on adaptations of the Orbital Motion-Limited (OML) theory which relies on several simplifying assumptions. Some of these assumptions, however, are typically not well satisfied in actual experimental conditions, thus leading to uncontrolled uncertainties in inferred plasma parameters. In order to remedy this difficulty, three-dimensional kinetic particle in cell simulations are used to construct a synthetic data set, which is used to compare and assess different m-NLP inference techniques. Using a synthetic data set, regression-based models capable of inferring electron density and satellite potentials from 4-tuples of currents collected with fixed-bias needle probes similar to those on the NorSat-1 satellite, are trained and validated. The regression techniques presented show promising results for plasma density inferences with RMS relative errors less than 20 %, and satellite potential inferences with RMS errors less than 0.2 V for potentials ranging from -6 V to -1 V. The new inference approaches presented are applied to NorSat-1 data, and compared with existing state-of-the-art inference techniques.

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.

This study uses several years of 16 Hz density measurements, field-aligned current, 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. The data covers the polar cap, auroral oval and mid-latitudes with an emphasis on high latitudes. We present power spectral densities (PSD) of electron density irregularities and magnetic field for one-minute intervals. These PSD were characterized by the probability of a slope steepening, and by integrating the power deposited within different frequency intervals. For the electron density, we observe seasonal dependencies for both the integrated power and slope characteristics. While the 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 integrated power from the density PSD, 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 region. For the PSD of the magnetic field data, we obtain the strongest enhancements within the cusp for all seasons and all hemispheres. These enhancements are co-located with the highest ROTI values.

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

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.

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 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.

For over a decade, the Super Dual Auroral Radar Network (SuperDARN) and the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) have been measuring ionospheric convection and field-aligned currents in the high-latitude regions, respectively. Using both, whole hemisphere maps of the magnetosphere-ionosphere energy transfer rate (the Poynting flux) have been generated with a time resolution of two minutes between 2010 and 2017. These uniquely data driven Poynting flux patterns are used in this study to perform a superposed epoch analysis of the northern hemisphere ionospheric response to transitions of the IMF B_z component. We discuss the difference in the distribution of Poynting flux between the magnetosphere-ionosphere Dungey cycle “switching on” and “switching off” to solar wind driving, revealing that they are not symmetric temporally or spatially.

The Australian NWC (North West Cape) signal transmitter is known to strongly interfere with the topside ionosphere. We analyze 456 conjunctions between Swarm A, B and NWC, in addition to 58 conjunctions between NorSat-1 and NWC. The in-situ measurements provided by these satellites include the 16 Hz Swarm Advanced Plasma density dataset, and the novel 1000 Hz plasma density measurements from the m-NLP system aboard NorSat-1. We subject the data to a detailed PSD analysis and subsequent superposed epoch analysis. This allows us to present comprehensive statistics of the NWC-induced plasma fluctuations, both their scale-dependency, and their climatology. The result should be seen in the context of VLF signal transmitter-induced plasma density fluctuations, where we find counter-evidence for the existence of turbulent structuring induced by the NWC transmitter.

Large thermospheric neutral density enhancements in the cusp region have been examined for many years. The CHAMP satellite for example has enabled many observations of the perturbation, showing that it is mesoscale in size and exists on statistical timescales. Further studies examining the relationship with magnetospheric energy input have shown that fine-scale Poynting fluxes are associated with the density perturbations on a case-by-case basis, whilst others have found that mesoscale downward fluxes also exist in the cusp region statistically. In this study, we use nearly 8 years of the overlapping SuperDARN and AMPERE datasets to generate global-scale patterns of the high-latitude and height-integrated Poynting flux into the ionosphere, with a time resolution of two minutes. From these, average patterns are generated based on the IMF orientation. We show the cusp is indeed an important feature in the Poynting flux maps, but the magnitude does not correlate well with statistical neutral mass density perturbations observed by the CHAMP satellite on similar spatial scales. Mesoscale height-integrated Poynting fluxes thus cannot fully account for the cusp neutral mass density enhancement, meaning energy deposition in the F-region or on fine-scales, which is not captured by our analysis, could be the primary driver.

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.