Auroral Images Research Articles

Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness

Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods.

Crucial future observations and directions for unveiling magnetopause dynamics and their geospace impacts

The dynamics of Earth’s magnetopause, driven by several different external/internal physical processes, plays a major role in the geospace energy budget. Given magnetopause motion couples across many space plasma regions, numerous forms of observations may provide valuable information in understanding these dynamics and their impacts. In-situ multi-point spacecraft measurements measure the local plasma environment, dynamics and processes; with upcoming swarms providing the possibility of improved spatiotemporal reconstruction of dynamical phenomena, and multi-mission conjunctions advancing understanding of the “mesoscale” coupling across the geospace “system of systems.” Soft X-ray imaging of the magnetopause should enable boundary motion to be directly remote sensed for the first time. Indirect remote sensing capabilities might be enabled through the field-aligned currents associated with disturbances to the magnetopause; by harnessing data from satellite mega-constellations in low-Earth orbit, and taking advantage of upgraded auroral imaging and ionospheric radar technology. Finally, increased numbers of closely-spaced ground magnetometers in both hemispheres may help discriminate between high-latitude processes in what has previously been a “zone of confusion.” Bringing together these multiple modes of observations for studying magnetopause dynamics is crucial. These may also be aided by advanced data processing techniques, such as physics-based inversions and machine learning methods, along with comparisons to increasingly sophisticated geospace assimilative models and simulations.

Early auroral photography and observations at the Sodankylä Geophysical Observatory in Finland, 1927–1929

Abstract. In Finland, auroral photography started in 1927 at the Sodankylä Geophysical Observatory (SGO) with the initiative of famous Norwegian scientist Carl Störmer. In less than 2 years about 600 photographs of auroras were taken at Sodankylä. Some of the images were obtained simultaneously at auxiliary stations for parallactic determinations of the height of auroral arcs. Most of the pictures of auroras were lost in the destruction of the SGO during the war in 1944. About 200 images were rescued in the archive of the Finnish Meteorological Institute, where they were recently found. These pictures of auroras are the first ones taken in Finland. These photographs are now digitized and archived in the SGO. During the polar year period 1932–1933, auroral photography was mostly discontinued, but visual observations of auroras were made instead at several sites in Lapland. The main sources of information about the history of auroral images are handwritten notebooks of Eyvind Sucksdorff for 1927–1929. They contain relevant data for each photograph (date, exposure time, orientation of camera etc.). In Appendix A there are tables showing the dates of rescued auroral photographs as well as the lost ones. In Finland, Sucksdorff's contribution to studies of auroras was a pioneering effort with minimal resources. Regular photographing of auroras started in Finland during the International Geophysical Year (IGY) 1957–1958.

Classification of Ground‐Based Auroral Images by Learning Deep Tensor Feature Representation on Riemannian Manifold

AbstractAutomatically classifying a huge amount of ground‐based auroral images is essential to facilitate aurora morphology statistical research and aid in comprehending the magnetospheric dynamics. However, facing the challenge of insufficient labeled images, deep learning methods perform suboptimally on small auroral image data sets, and traditional machine learning methods based on handcrafted features heavily rely on expert knowledge. In this paper, we propose a novel method that leverages the merits of both traditional machine learning and deep learning methods by extracting deep second‐order tensor features to train a support vector machine (SVM). To improve compactness and discriminative ability of the features, we comply the intrinsic data geometry on Riemannian manifold to employ dimensionality reduction and map the dimensionality‐reduced features from Riemannian space to Euclidean space for the SVM classifier. Experimental results on small aurora data sets conclusively demonstrate the effectiveness of our method, exhibiting competitive performance with recent aurora classification methods.

Short Wave Infrared Imaging for Auroral Physics and Aeronomy Studies

Advances in solar-terrestrial physics are generally linked to the development of innovative new sensor technologies, affording us ever better sensitivity, higher resolution, and broader spectral response. Recent advances in low-noise InGaAs sensor technology have enabled the realization of low-light-level scientific imaging within the short-wave infrared (SWIR) region of the electromagnetic spectrum. This paper describes a new and highly sensitive ultra-wide angle imager that offers an expansion of auroral and airglow imaging capabilities into the SWIR spectral range of 900–1,700 nm. The imager has already proven successful in large-area remote sensing of mesospheric temperatures and in providing intensity maps showing the propagation and dissipation of atmospheric gravity waves and ripples. The addition of an automated filter wheel expands the range of applications of an already versatile SWIR detector. Several potential applications are proposed herein, with an emphasis on auroral science. The combined data from this type of instrument and other existing instrumentation holds a strong potential to further enhance our understanding of the geospace environment.

A Quantitative Analysis of the Uncertainties on Reconnection Electric Field Estimates Using Ionospheric Measurements

AbstractCalculating the magnetic flux transfer across the open‐closed boundary (OCB) per unit time and distance—the reconnection electric field—is an important means of remotely monitoring magnetospheric dynamics. Ground‐based measurements of plasma convection velocities together with velocities of the OCB are commonly used to infer reconnection rates. However, this approach is limited by spatial coverage and often lacks robust uncertainty quantification. In this paper, we assimilate Super Dual Auroral Radar Network convection measurements, ground magnetometer data, and estimates of the conductance derived from the Imager for Magnetopause‐to‐Aurora Global Exploration satellite imagers, using the Local mapping of polar ionospheric electrodynamics (Lompe) framework over a region in North America. We present a new method to assess various contributions to uncertainties in the derived reconnection electric fields, including a novel approach to estimate uncertainties in conductance from global auroral imaging. Our method is demonstrated on a substorm event with an associated pseudobreakup during a period of favorable observational coverage. In this case study, the uncertainties in the reconnection electric field are ∼5–10 mV/m at the peak of substorm expansion, roughly 15% of the peak reconnection electric field. We find that the main contributor to the reconnection electric field estimates after substorm onset is the OCB motion, whereas during the pseudobreakup the main contributor is ionospheric plasma convection.

Application of generalized aurora computed tomography to the EISCAT_3D project

Abstract. EISCAT_3D is a project to build a multi-site phased-array incoherent scatter radar system in northern Fenno-Scandinavia. We demonstrate via numerical simulation how useful monochromatic images taken by a multi-point imager network are for auroral research in the EISCAT_3D project. We apply the generalized aurora computed tomography (G-ACT) method to modelled observational data from real instruments, such as the Auroral Large Imaging System (ALIS) and the EISCAT_3D radar. G-ACT is a method for reconstructing the three-dimensional (3D) distribution of auroral emissions and ionospheric electron density (corresponding to the horizontal two-dimensional (2D) distribution of energy spectra of precipitating electrons) from multi-instrument data. It is assumed that the EISCAT_3D radar scans an area of 0.8° in geographic latitude and 3° in longitude at an altitude of 130 km with 10 × 10 beams from the radar core site at Skibotn (69.35° N, 20.37° E). Two neighboring discrete arcs are assumed to appear in the observation region of the EISCAT_3D radar. The reconstruction results from G-ACT are compared with those from the normal ACT as well as the ionospheric electron density from the radar. It is found that G-ACT can interpolate the ionospheric electron density at a much higher spatial resolution than that observed by the EISCAT_3D radar. Furthermore, the multiple arcs reconstructed by G-ACT are more precise than those by ACT. In particular, underestimation of the ionospheric electron density and precipitating electrons' energy fluxes inside the arcs is significantly improved by G-ACT including the EISCAT_3D data. Even when the ACT reconstruction is difficult due to the unsuitable locations of the imager sites relative to the discrete arcs and/or a small number of available images, G-ACT allows us to obtain better reconstruction results.

Latitudinal Profiles of Auroral Forms/Motions and Plasma Properties Based on Simultaneous Image‐Particle Measurements by Reimei in the Midnight Auroral Oval

AbstractWe present the simultaneous and conjugated auroral emission and particle data obtained by a low‐altitude polar‐orbiting micro‐satellite, Reimei, for elucidating their latitudinal distributions and variations in the nightside auroral oval. Here are reported a few notable examples of the Reimei observations with high time and spatial resolutions, namely ∼120 msec. and ∼1.2 km × 1.2 km for multispectral auroral images and 40 msec. for energy‐pitch angle distributions of electrons and ions with energies of 10 eV–12 keV, respectively. The auroral images show various fine‐scale auroral activities characterized by the following types of auroral forms and variations: faint bands, streaming multiple arcs, shearing arcs, and vortices/curls, which are typical of the latitudinal properties of auroras. The particle analyzer simultaneously observed various properties of electron energy‐pitch angle and latitudinal distributions, and their temporal variations, each of which corresponds to a type of the auroral activities. Their features are summarized below. Reimei repetitively observed inverted‐V signatures of low‐energy (<1 keV) field‐aligned electrons in addition to the higher‐energy (several keV) diffuse electrons in low‐latitude auroral oval. In more active regions at higher latitudes, the dominant energy flux responsible for the multiple‐arc emissions was carried by the well‐known inverted‐V electron precipitation. The rapidly rotating vortices or so‐called curls of fine‐scale discrete auroras near the poleward boundary of the auroral oval were closely associated with the significant energy fluxes of spiky field‐aligned electron bursts with energy‐time dispersions produced by dispersive Alfvén waves.

AuroraMag: Twin explorer of asymmetry in aurora and solar wind-magnetosphere coupling

In the present-day context, small satellites and their constellations consisting of varying sizes (nano, micro, pico satellites) are being favored for remote sensing and in situ probing of the heliosphere and terrestrial magnetosphere-ionosphere system. We introduce a mission concept aimed at concurrently observing Earth’s northern and southern auroral ovals while conducting in situ measurements of particles, fields, and electron temperature. The mission concept consists of two small satellites, each having an identical auroral X-ray imager, an in situ particle detector, a magnetometer pair, and an electron temperature analyzer onboard in an elliptical polar orbit (400 km × 10000 km). This mission would assist the space weather community in primarily answering important questions about the formation, morphology, and hemispherical asymmetries that we observe in the X-ray aurora, the fluxes of precipitating particles, Solar Energetic Particles, currents, and cusp dynamics. Once realized, this would be the first dedicated twin spacecraft mission of such kind to simultaneously study hemispheric asymmetries of solar-wind magnetosphere coupling. This study reveals the intricacies of the mission concept, encompassing orbital details, potential payloads, and its underlying scientific objectives. By leveraging the capabilities of small satellites, this mission concept is poised to make significant contributions to space weather monitoring and research.

Auroral breakup detection in all-sky images by unsupervised learning

Abstract. Due to a large number of automatic auroral camera systems on the ground, image data analysis requires more efficiency than what human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by an unsupervised learning method on an image set that contains both nightside and dayside aurora. We used 6 months of full-colour auroral all-sky images captured at a high-Arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input into a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological clusters. In the clustering of auroral image data with two different time resolutions, we found that the occurrence of 8 clusters strongly increased when the image cadence was high (24 s), while the occurrence of 14 clusters experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of a group of eight “active aurora” clusters. Time periods for which this active aurora persisted for longer than two consecutive images with a maximum cadence of 6 min coincided with ground-magnetic deflections, and their occurrence was found to maximize around magnetic midnight. The active aurora onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image clustering method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.

Automatically Sketching Auroral Skeleton Structure in All‐Sky Image for Measuring Aurora Arcs

AbstractThe auroral arc is the typical track of the interaction between the solar wind and the Earth's magnetosphere. A sketch of skeletons for arc‐like aurora is usually used to describe auroral structures, such as vortex, fold and curl structures, etc. With artificial intelligence technologies, sketching auroral skeleton structure (AuroSS) in all‐sky images enables automatic detection and measurement of aurora arcs in very large amounts of ground‐based auroral observation data. The skeleton is a highly characterizing topological structure that has been extensively studied in the field of computer vision. However, AuroSS is not the medial axis of auroral shapes and a large number of accurate AuroSS annotations are not available. It is difficult to detect AuroSS by using an unsupervised or fully‐supervised method. In this paper, we formulate the automatic AuroSS extraction to learn a mapping from an all‐sky auroral image to a ridge style AuroSS. Without accurate AuroSS annotations, emission ridge and coarse localization of aurora are incorporated to generate pseudo‐labels of AuroSS. A series of functional weakly supervised models are trained and cascaded to achieve AuroSS detection. Experimental results on auroral images obtained from all‐sky imagers at Yellow River Station (YRS) show that the detected AuroSS is consistent with that of human visual perception. Based on the obtained AuroSS, the orientations and lengths of auroral arcs can be estimated automatically. By browsing the temporal variation in arc orientation from dusk to dawn, we can acquire synoptic observations of auroral activities at YRS.

Cassini UVIS Observations of the Enceladus Auroral Footprint on Saturn in 2017

Ultraviolet Imaging Spectrograph (UVIS) observations show the Enceladus auroral footprint on Saturn on 2017 September 14, near the end of the Cassini mission. A series of Saturn north polar auroral images were obtained by slowly slewing the Cassini spacecraft at right angles to the UVIS long slit. The images were limb-fit to improve the spacecraft geometry. Enhanced extreme-ultraviolet 88–118 nm channel emissions due to electron impact on atomic and molecular hydrogen were seen in the expected location for the Enceladus auroral footprint on five successive images spanning almost 4 hr. Enhanced emissions were also seen in simultaneously obtained far-ultraviolet 111–165 nm images in at least two of these images, with the spectral signature expected for auroral emissions. While most Cassini UVIS auroral images do not show the Enceladus auroral footprint, these 2017 images support the earlier detection of an Enceladus-linked spot on Saturn in 2008 Cassini UVIS data.

Multiview Learning for Automatic Classification of Multiwavelength Auroral Images

Multiview Learning for Automatic Classification of Multiwavelength Auroral Images

Background removal from global auroral images: Data-driven dayglow modeling

Background removal from global auroral images: Data-driven dayglow modeling

On mechanisms for high-frequency pump-enhanced optical emissions at 557.7 and 630.0 nm from atomic oxygen in the high-latitude F-region ionosphere

Abstract. The EISCAT (European Incoherent Scatter Scientific Association) Heating facility was used to transmit powerful high-frequency (HF) electromagnetic waves into the F-region ionosphere to enhance optical emissions at 557.7 and 630.0 nm from atomic oxygen. The emissions were imaged by several stations of ALIS (Auroral Large Imaging System) in northern Sweden, and the EISCAT UHF incoherent scatter radar was used to obtain plasma parameter values. The ratio of the 557.7 to 630.0 nm column emission rates changed from I5577/I6300≈0.2 for the HF pump frequency f0=6.200 MHz ≈4.6fe to I5577/I6300≈0.5 when f0=5.423 MHz ≲4fe, where fe is the ionospheric electron gyro frequency. The observations are interpreted in terms of decreased electron heating efficiency and thereby weaker enhancement at 630.0 nm for f0=5.423 MHz ≲ 4 fe. The emissions at 557.7 nm are attributed to electron acceleration by upper hybrid waves of metre-scale wavelengths that can be excited with f0=5.423 MHz ≲ 4 fe.

Cavsiopy: a Python package to calculate and visualize spacecraft instrument orientation

Spacecraft attitude plays an important role in the observations of various atmospheric, planetary, and terrestrial parameters and phenomena that are of interest to the scientific community. Precise measurements from imagers, particle sensors, and antennas require accurate knowledge of instrument orientation. cavsiopy is an easy-to-install and use, light-weight open-source Python package for researchers who need to consider instrument pointing direction and observation geometry. cavsiopy contains the coordinate transformation routines and the corresponding rotation matrices from the spacecraft orbital reference frame (ORF) to any of the geocentric equatorial inertial for epoch J2000 (GEI J2K)/International Celestial Reference Frame (ICRF), Earth-centered, Earth-fixed (ECEF), International Terrestrial Reference Frame (ITRF), geodetic north-east-down, and geocentric north-east-center coordinate systems. Additionally, cavsiopy includes routines for importing Swarm-E ephemeris and generic two-line-element (TLE) data files; for the calculation of spacecraft azimuth, elevation, and orbital parameters; as well as for the 2D/3D visualization of the geometry between the instrument and the target. Functionality and utilization of cavsiopy for research problems are demonstrated with examples and visualizations for the Radio Receiver Instrument (RRI) and the Fast Auroral Imager (FAI) of e-POP/Swarm-E.

Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite

An auroral substorm is an important physical process of energy accumulation and explosive release in the Earth’s magnetosphere, and is an important research object of space environment monitoring and space weather warnings. A westward traveling surge (WTS) is a typical auroral physical process of an auroral substorm. Its static characteristic is auroral folding at the polar boundary of an auroral oval and its dynamic characteristic is the westward motion of auroral folding. According to the static characteristic of a WTS, we defined a set of feature parameters based on the morphology and designed a set of automatic detection and discrimination methods; that is, the WTS was identified by using the extracted features and pattern recognition approaches. This approach was tested by using the aurora data of the ultraviolet auroral spectral imager of the Defense Meteorological Satellite Program (DMSP) satellite. The results showed that the accuracy rate of automatic recognition was 61.39%~63.61% and the precision rate was 55.52%~57.92%. The experimental results showed that the approach was effective at detecting the typical characteristics of an auroral substorm (WTS).

Aurora Classification in All-Sky Images via CNN–Transformer

An aurora is a unique geophysical phenomenon with polar characteristics that can be directly observed with the naked eye. It is the most concentrated manifestation of solar–terrestrial physical processes (especially magnetospheric–ionospheric interactions) in polar regions and is also the best window for studying solar storms. Due to the rich morphological information in aurora images, people are paying more and more attention to studying aurora phenomena from the perspective of images. Recently, some machine learning and deep learning methods have been applied to this field and have achieved preliminary results. However, due to the limitations of these learning models, they still need to meet the requirements for the classification and prediction of auroral images regarding recognition accuracy. In order to solve this problem, this study introduces a convolutional neural network transformer solution based on vision transformers. Comparative experiments show that the proposed method can effectively improve the accuracy of aurora image classification, and its performance has exceeded that of state-of-the-art deep learning methods. The experimental results show that the algorithm presented in this study is an effective instrument for classifying auroral images and can provide practical assistance for related research.

Auroral Imaging With Combined Suomi 100 Nanosatellite and Ground‐Based Observations: A Case Study

AbstractAuroras can be regarded as the most fascinating manifestation of space weather and they are continuously observed by ground‐based and, nowadays more and more, also by space‐based measurements. Investigations of auroras and geospace comprise the main research goals of the Suomi 100 nanosatellite, the first Finnish space research satellite, which has been measuring the Earth's ionosphere since its launch on 3 December 2018. In this work, we present a case study where the satellite's camera observations of an aurora over Northern Europe are combined with ground‐based observations of the same event. The analyzed image is, to the authors' best knowledge, the first auroral image ever taken by a CubeSat. Our data analysis shows that a satellite vantage point provides complementary, novel information of such phenomena. The 3D auroral location reconstruction of the analyzed auroral event demonstrates how information from a 2D image can be used to provide location information of auroras under study. The location modeling also suggests that the Earth's limb direction, which was the case in the analyzed image, is an ideal direction to observe faint auroras. Although imaging on a small satellite has some large disadvantages compared with ground‐based imaging (the camera cannot be repaired, a fast moving spinning satellite), the data analysis and modeling demonstrate how even a small 1‐Unit (size: 10 × 10 × 10 cm) CubeSat and its camera, build using cheap commercial off‐the‐shelf components, can open new possibilities for auroral research, especially, when its measurements are combined with ground‐based observations.

Contribution of Kinetic Alfvén Waves to Energetic Electron Precipitation From the Plasma Sheet During a Substorm

AbstractEnergetic (≳50 keV) electron precipitation from the magnetosphere to the ionosphere during substorms can be important for magnetosphere‐ionosphere coupling. Using conjugate observations between the THEMIS, ELFIN, and DMSP spacecraft during a substorm, we have analyzed the energetic electron precipitation, the magnetospheric injection, and the associated plasma waves to examine the role of waves in pitch‐angle scattering plasma sheet electrons into the loss cone. During the substorm expansion phase, ELFIN‐A observed 50–300 keV electron precipitation from the plasma sheet that was likely driven by wave‐particle interactions. The identification of the low‐altitude extent of the plasma sheet from ELFIN is aided by DMSP global auroral images. Combining quasi‐linear theory, numerical test particle simulations, and equatorial THEMIS measurements of particles and fields, we have evaluated the relative importance of kinetic Alfvén waves (KAWs) and whistler‐mode waves in driving the observed precipitation. We find that the KAW‐driven bounce‐averaged pitch‐angle diffusion coefficients near the edge of the loss cone are ∼10−6–10−5 s−1 for these energetic electrons. The due to parallel whistler‐mode waves, observed at THEMIS ∼10‐min after the ELFIN observations, are ∼10−8–10−6 s−1. Thus, at least in this case, the observed KAWs dominate over the observed whistler‐mode waves in the scattering and precipitation of energetic plasma sheet electrons during the substorm injection.