EGU General Assembly 2022  

I will review the main magnetosphere-ionosphere (MI) coupling mechanisms thought to play a role at Jupiter and at Saturn. We are interested in the extent to which the magnetospheres are driven by internal processes (plasma sources, planetary rotation) versus external mechanisms (solar wind, interplanetary magnetic field). At both planets, momentum is mostly transferred via the rotating planetary magnetic field from the ionosphere to the magnetosphere. The solar wind can also play a role in driving dynamics, e.g. via the interaction of corotating interaction regions (CIRs). The NASA/ESA Cassini Huygens mission revealed that Saturn’s system also has a unique feature driven by the ionosphere known as “planetary period oscillations”. These phenomena interact with the effects of the solar wind to produce complex MI coupling signatures. The NASA Juno mission has provided the first in situ evidence of MI coupling in Jupiter's polar magnetosphere. I will compare the similarities and differences between observation and theory discovered thus far.

How to cite: Bunce, E.: Magnetosphere-Ionosphere Coupling and Aurora at Jupiter and Saturn, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11375, https://doi.org/10.5194/egusphere-egu22-11375, 2022.

A long-standing question within Saturn’s magnetosphere is the source of the ubiquitous oscillations, known as planetary period oscillations (PPOs). From radio and magnetometer data it is known there are two such oscillation systems, one in the northern hemisphere and the other in the southern. In this talk, we will review analyses of azimuthal magnetic field data from the Cassini mission right up to its end in 2017 which show the presence of field-aligned currents. Using these data, several field-aligned current systems are shown to be present in Saturn’s auroral regions and their relationship with the PPOs was revealed. The implications of these results on Saturn’s periodicities, aurora, and coupling between the ionosphere and magnetosphere will be discussed.  

How to cite: Hunt, G.: Saturn's field-aligned current systems as observed by the Cassini mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13335, https://doi.org/10.5194/egusphere-egu22-13335, 2022.

The recent developments in computer vision research in the field of Single Image Super Resolution (SISR)

can help improve the satellite imagery data quality and, thus, find application in planetary exploration.

The aim of this study is to enhance planetary surface imagery, in planetary bodies that there are

available data but in a low resolution. Here, we have applied the holistic attention network (HAN)

algorithm to a set of images of Saturn’s moon Titan from the Titan Radar Mapper instrument in its

Synthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can find

correlations among hierarchical layers, channels of each layer, and all positions of each channel, which

can be interpreted as an application and intersection of previously known models. The algorithm used

in our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery dataset

resampled over different resolutions. Our experimental setup was to generate High Resolution (HR)

imagery from eight times lower resolution (x8 scale). We followed the standard workflow for this

purpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, and

finally x8 scale to x4 scale, using subsequently the results of the previous training. The promising results

open a path for further applications of the trained model to improve the imagery data quality, and aid

in the detection and analysis of planetary surface features.

How to cite: Maxheimer, D., Markonis, I., Jan, M., Vojtech, C., Jan, P., and Anezina, S.: Enhancing planetary imagery with the holistic attention network algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-486, https://doi.org/10.5194/egusphere-egu22-486, 2022.

Lineaments are prominent features on the surface of Jupiter's moon Europa. Analysing these linear features thoroughly leads to insights on their formation mechanisms and the interactions between the subsurface ocean and the surface. The orientation and position of lineaments is also important for determining the stress field on Europa. The Europa Clipper mission is planned to launch in 2024 and will fly by Europa more than 40 times. In the light of this, an autonomous lineament detection and segmentation tool would prove useful for processing the vast amount of expected images efficiently and would help to identify processes affecting the ice sheet. 

We have trained a convolutional neural network to detect, classify and segment lineaments in images of Europa returned by the Galileo mission. The Galileo images that make up the training set are segmented manually, following a dedicated guideline. For better performance, we make use of synthetically generated data to pre-train the network. The current status of the work will be described.

How to cite: Haslebacher, C. and Thomas, N.: Autonomous lineament detection in Galileo images of Europa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-692, https://doi.org/10.5194/egusphere-egu22-692, 2022.

The Waves of HIgh frequency and Sounder for Probing Electron density by Relaxation
(WHISPER) instrument, is part of the Wave Experiment Consortium (WEC) of the CLUSTER II
mission. The instrument consists of a receiver, a transmitter, and a wave spectrum
analyzer. It delivers active (when in sounding mode) and natural electric field spectra. The
characteristic signature of waves indicates the nature of the ambient plasma regime and, combined
with the spacecraft position, reveals the different magnetosphere boundaries and regions. The
thermal electron density can be deduced from the characteristics of natural waves in natural mode
and from the resonances triggered in sounding mode, giving access to a key parameter of scientific
interest and major driver for the calibration of particles instrument.
Until recently, the electron density derivation required a manual time/frequency domain
initialization of the search algorithms, based upon visual inspection of WHISPER active and natural
spectrograms and other datasets from different instruments onboard CLUSTER.
To automate this process, knowledge of the region (plasma regime) is highly desirable. A Multi-
Layer Perceptron model has been implemented for this purpose. For each detected region, a GRU,
recurrent network model combined with an ad-hoc algorithm is then used to determine the electron
density from WHISPER active spectra. These models have been trained using the electron density
previously derived from various semi-automatic algorithms and manually validated, resulting in an
accuracy up to 98% in some plasma regions. A production pipeline based on these models has been
implemented to routinely derive electron density, reducing human intervention up to 10 times. Work
is currently ongoing to create some models to process natural measurements where the data volume
is much higher and the validation process more complex. These models of electron density
automated determination will be useful for future other space missions.

How to cite: De Leon, E., Gilet, N., Vallières, X., Bucciantini, L., Henri, P., and Rauch, J.-L.: Automatic detection of the electron density from the WHISPER instrument onboard CLUSTER II, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1014, https://doi.org/10.5194/egusphere-egu22-1014, 2022.

Spectroscopy provides important information on the surface composition of Mars. Spectral data can support studies such as the evaluation of potential (manned) landing sites as well as supporting determination of past surface processes. The CRISM instrument on NASA’s Mars Reconnaissance Orbiter is a high spectral resolution visible infrared mapping spectrometer currently in orbit around Mars. It records 2D spatially resolved spectra over a wavelength range of 362 nm to 3920 nm. At present data collected covers less than 2% of the planet. Lifetime issues with the cryo-coolers prevents limits further data acquisition in the infrared band. In order to extend areal coverage for spectroscopic analysis in regions of major importance to the history of liquid water on Mars (e.g. Valles Marineris, Noachis Terra), we investigate whether data from other instruments can be fused to extrapolate spectral features in CRISMto these non-spectral imaged areas. The present work will use data from the CaSSIS instrument which is a high spatial resolution colour and stereo imager onboard the European Space Agency’s ExoMars Trace Gas Orbiter (TGO). CaSSIS returns images at 4.5 m/px from the nominal 400 km altitude orbit in four colours. Its filters were selected to provide mineral diagnostics in the visible wavelength range (400 – 1100 nm). It has so far imaged around 2% of the planet with an estimated overlap of ≲0.01% of CRISM data. This study introduces a two-step pixel based reconstruction approach using CaSSIS four band images. In the first step advanced unsupervised techniques are applied on CRISM hyperspectral datacubes to reduce dimensionality and establish clusters of spectral features. Given that these clusters contain reasonable information about the surface composition, in a second step, it is feasible to map CaSSIS four band images to the spectral clusters by training a machine learning classifier (for the cluster labels) using only CaSSIS datasets. In this way the system can extrapolate spectral features to areas unmapped by CRISM. To assess the performance of this proposed methodology we analyzed actual and artificially generated CaSSIS images and benchmarked results against traditional correlation based methods. Qualitative and quantitative analyses indicate that by this novel procedure spectral features of in non-spectral imaged areas can be predicted to an extent that can be evaluated quantitatively, especially in highly feature-rich landscapes.

How to cite: Fernandes, M., Thomas, N., Elser, B., Rossi, A. P., Pletl, A., and Cremonese, G.: Extrapolation of CRISM based spectral feature maps using CaSSIS four-band images with machine learning techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2765, https://doi.org/10.5194/egusphere-egu22-2765, 2022.

Solar flares and coronal mass ejections (CMEs) are the main drivers for severe space weather disturbances on Earth and other planets. While the geo-effects of CMEs give us a lead time of about 1 to 4 days, the effects of flares and flare-accelerated solar energetic particles (SEPs) are very immediate, 8 minutes for the enhanced radiation and as short as about 20 minutes for the highest energy SEPs arriving at Earth. Thus, predictions of solar flare occurrence at least several hours ahead are of high importance for the mitigation of severe space weather effects.

Observations and simulations of solar flares suggest that the structure and evolution of the active region’s magnetic field is a key component for energetic eruptions. The recent advances in deep learning provide tools to directly learn complex relations from multi-dimensional data. Here, we present a novel deep learning method for short-term solar flare prediction. The algorithm is based on the HMI photospheric line-of-sight magnetic field and its temporal evolution together with the coronal evolution as observed by multi-wavelengths EUV filtergrams from the AIA instrument onboard the Solar Dynamics Observatory. We train a neural network to independently identify features in the imaging data based on the dynamic evolution of the coronal structure and the photospheric magnetic field evolution, which may hint at flare occurrence in the near future.

We show that our method  can reliably predict flares six hours ahead, with 73% correct flaring predictions (89% when considering only M- and X-class flares), and 83% correct quiet active region predictions.

In order to overcome the “black box problem” of machine-learning algorithms, and thus to allow for physical interpretation of the network findings, we employ a spatio-temporal attention mechanism. This allows us to extract the emphasized regions, which reveal the neural network interpretation of the flare onset conditions. Our comparison shows that predicted precursors are associated with the position of flare occurrence, respond to dynamic changes, and align with characteristics within the active region.

How to cite: Jarolim, R., Veronig, A., Podladchikova, T., Thalmann, J., Narnhofer, D., Hofinger, M., and Pock, T.: Interpretable Solar Flare Prediction with Deep Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2994, https://doi.org/10.5194/egusphere-egu22-2994, 2022.

The magnetopause (MP) and the bow shock (BS) are the two boundaries bounding the magnetosheath, the region between the magnetosphere and the solar wind. Their position and shape depend on the upstream solar wind and interplanetary magnetic field conditions.

Predicting their shape and position is the starting point of many subsequent studies of processes controlling the coupling between the Earth’s magnetosphere and its interplanetary environment. We now have at our disposal an important amount of data from a multitude of spacecraft missions allowing for good spatial coverage, as well as algorithms based on statistical learning to automatically detect the two boundaries. From the data of 9 satellites over 20 years, we identified around 19000 crossings of the BS and 36000 crossings of the MP. They were used, together with their associated upstream conditions, to train a regression model to predict the shape and position of the boundaries. 

Preliminary results indicate that the obtained models outperform analytical models without making simplifying assumptions on the geometry and the dependency over control parameters.

How to cite: Ghisalberti, A., Aunai, N., and Michotte de Welle, B.: Magnetopause and bow shock models with machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5721, https://doi.org/10.5194/egusphere-egu22-5721, 2022.

Mantle convection plays a fundamental role in the long-term thermal evolution of terrestrial planets like Earth, Mars, Mercury and Venus. The buoyancy-driven creeping flow of silicate rocks in the mantle is modeled as a highly viscous fluid over geological time scales and quantified using partial differential equations (PDEs) for conservation of mass, momentum and energy. Yet, key parameters and initial conditions to these PDEs are poorly constrained and often require a large sampling of the parameter space to find constraints from observational data. Since it is not computationally feasible to solve hundreds of thousands of forward models in 2D or 3D, some alternatives have been proposed. 

The traditional alternative to high-fidelity simulations has been to use 1D models based on scaling laws. While computationally efficient, these are limited in the amount of physics they can model (e.g., depth-dependent material properties) and predict only mean quantities such as the mean mantle temperature. Hence, there has been a growing interest in machine learning techniques to come up with more advanced surrogate models. For example, Agarwal et al. (2020) used feedforward neural networks (FNNs) to reliably predict the evolution of entire 1D laterally averaged temperature profile in time from five parameters: reference viscosity, enrichment factor for the crust in heat producing elements, initial mantle temperature, activation energy and activation volume of the diffusion creep. 

We extend that study to predict the full 2D temperature field, which contains more information in the form of convection structures such as hot plumes and cold downwellings. This is achieved by training deep learning algorithms on a data set of 10,525 2D simulations of the thermal evolution of the mantle of a Mars-like planet. First, we use convolutional autoencoders to compress the size of each temperature field by a factor of 142. Second,  we compare the use of two algorithms for predicting the compressed (latent) temperature fields: FNNs and long-short-term memory networks (LSTMs).  On the one hand, the FNN predictions are slightly more accurate with respect to unseen simulations (99.30%  vs. 99.22% for the LSTM). On the other hand, Proper orthogonal decomposition (POD) of the LSTM and FNN predictions shows that despite a lower mean relative accuracy, LSTMs capture the flow dynamics better than FNNs. The POD coefficients from FNN predictions sum up to 96.51% relative to the coefficients of the original simulations, while for LSTMs this metric increases to 97.66%. 

We conclude the talk by stating some strengths and weaknesses of this approach, as well as highlighting some ongoing research in the broader field of fluid dynamics that could help increase the accuracy and efficiency of such parameterized surrogate models.

How to cite: Agarwal, S., Tosi, N., Kessel, P., Breuer, D., and Montavon, G.: Deep learning for surrogate modeling of two-dimensional mantle convection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5739, https://doi.org/10.5194/egusphere-egu22-5739, 2022.

The Spectrometer Telescope for Imaging X-rays (STIX) is an instrument onboard Solar Orbiter. It measures X-rays emitted during solar flares in the energy range from 4 to 150 keV and takes X-ray images by using an indirect imaging technique, based on the Moiré effect. STIX instrument
consists of 32 pairs of tungsten grids and 32 pixelated CdTe detector units. Flare Images can be reconstructed on the ground using algorithms such as back-projection, forward-fit, and maximum-entropy after full pixel data are downloaded. Here we report a new image reconstruction and
classification model based on machine learning. Results will be discussed and compared with those from the traditional algorithms.

How to cite: Xiao, H., Krucker, S., Ryan, D., Battaglia, A., Lastufka, E., László, E., Dickson, E., and Wang, W.: STIX solar flare image reconstruction and classification using machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6371, https://doi.org/10.5194/egusphere-egu22-6371, 2022.

Since deployed on the Martian surface, the seismometer SEIS (Seismic Experiment for Interior Structure) and the APSS (Auxiliary Payload Sensors Suite) of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission have been recorded the daily Martian respectively ground acceleration and pressure. These data are essential to investigate the geophysical and atmospheric features of the red planet. So far, the InSight team were able to detect multiple Martian events. We distinguish two types: the artificial events like the lander modes or the micro-tilts known as glitches or the natural events like the pressure drops which are important to estimate the Martian subsurface and the seismic events used to study the interior structure of Mars. Despite the data complexity, the InSight team was able to catalog these events (Clinton et al 2020 for the seismic event catalog, Banfield et al., 2018, 2020 for the pressure drops catalog and Scholz et al. (2020) for the glitches catalog). However, despite all this effort, we are still in front of multiple challenges. In fact,  the seismic events' detection is limited  due to the SEIS sensitivity, which is the origin of a high noise level that may contaminate the seismic events. Thus, we can miss some of them, especially in the noisy period. Besides, their detection is very challenging and require multiple preprocessing task which is time-consuming. For the pressure drops, the detection method used in Banfield et al.  2020 is limited by a threshold equal to 0.3 Pa. Thus, the rest of pressure drops are not included. Plus, due to lack of energy, the pressure sensor was off for several days. As a result, many pressure drops were missed. As a result, being able to detect them directly on the SEIS data which are, in contrast,  provided continuously, is very important.

In this regard, the aim of this study is to overcome these challenges and thus improve the Martian events detection and provide an updated catalog automatically. For that, we were inspired of one of the main technics used today in data processing and analysis in a complete automatic way: it is the Machine Learning and particularly in our case is the Deep Learning. The architecture used for that is the “Hybrid Recurrent Scattering Neural Network” (Bueno et al 2021)  adapted for Mars

How to cite: Barkaoui, S., Bueno Rodriguez, A., Lognonné, P., De Hoop, M., Sainton, G., Plasman, M., and kawamura, T.: Mars events polyphonic detection, segmentation and classification with a hybrid recurrent scattering neural network using InSight mission data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8940, https://doi.org/10.5194/egusphere-egu22-8940, 2022.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past,
different machine learning approaches have been used to automatically detect events in existing time series resulting from
solar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facing
the large amount of data from different instruments. We propose a pipeline using a Network similar to the ResUNet++ (Jha et al. (2019)), for the automatic detection of ICMEs. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other datasets.
The method has been tested on in situ data from WIND. Additionally, it produced reasonable results on STEREO A and STEREO B datasets
with less input parameters. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.

How to cite: Ruedisser, H., Windisch, A., Amerstorfer, U. V., Amerstorfer, T., Möstl, C., Reiss, M. A., and Bailey, R. L.: Automatic Detection of Interplanetary Coronal Mass Ejections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9077, https://doi.org/10.5194/egusphere-egu22-9077, 2022.

Machine learning (ML) techniques have been successfully introduced in the fields of Space Physics and Space Weather, yielding highly promising results in modeling and predicting many disparate aspects of the geospace. Magnetospheric ultra-low frequency (ULF) waves play a key role in the dynamics of the near-Earth electromagnetic environment and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence, we are now able to use more robust approaches for automated ULF wave identification and classification. Here, we present results employing various neural networks (NNs) methods (e.g. Fuzzy Artificial Neural Networks, Convolutional Neural Networks) in order to detect ULF waves in the time series of low-Earth orbit (LEO) satellites. The outputs of the methods are compared against other ML classifiers (e.g. k-Nearest Neighbors (kNN), Support Vector Machines (SVM)), showing a clear dominance of the NNs in successfully classifying wave events.

How to cite: Balasis, G., Antonopoulou, A., Papadimitriou, C., Boutsi, A. Z., Giannakis, O., and Daglis, I. A.: Machine Learning Techniques for Automated ULF Wave Recognition in Swarm Time Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9621, https://doi.org/10.5194/egusphere-egu22-9621, 2022.

The solar wind and its variability is well understood at Earth. However, at distances larger than 1AU the is less clear, mostly due to the lack of in-situ measurements. In this study we use transfer learning principles to infer solar wind conditions at Mars in periods where no measurements are available, with the aim of better illuminating the interaction between the partially magnetised Martian plasma environment and the upstream solar wind. Initially, a convolutional neural network (CNN) model for forecasting measurements of the interplanetary magnetic field, solar wind velocity, density and dynamic pressure is trained on terrestrial solar wind data. Afterwards, knowledge from this model is incorporated into a secondary CNN model which is used for predicting solar wind conditions upstream of Mars up to 5 hours in the future. We present the results of this study as well as the opportunities to expand this method for use at other planets.

How to cite: Durward, S.: Forecasting solar wind conditions at Mars using transfer learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10105, https://doi.org/10.5194/egusphere-egu22-10105, 2022.

We propose a new method for automatic detection of solar magnetic tornadoes based on computer vision methods. Magnetic tornadoes are magneto-plasma structures with a swirling magnetic field in the solar corona, and there is also evidence for the rotation of plasma in them. A theoretical description and numerical modeling of these objects are very difficult due to the three-dimensionality of the structures and peculiarities of their spatial and temporal dynamics [Wedemeyer-Böhm et al, 2012, Nature]. Typical sizes of magnetic tornadoes vary from 10² km up to 10⁶ km, and their lifetime is from several minutes to many hours. So far, quite a few works are devoted to their study, and there are no accepted algorithms for detecting solar magnetic tornadoes by machine methods. An insufficient number of identified structures is one of many problems that do not allow studying physics of magnetic tornadoes and the processes associated with them. In particular, the filamentous rotating structures are well delectable only at the limb, while one can only make suppositions about their presence at the solar disk.
Our method is based on analyzing SDO/AIA images at wavelengths 171 Å, 193 Å, 211 Å and 304 Å, to which several different algorithms are applied, namely, the convolution with filters, convolutional neural network, and gradient boosting. The new technique is a combination of several approaches (transfer learning & stacking) that are widely used in various fields of data analysis. Such an approach allows detecting the structures in a short time with sufficient accuracy. As test objects, we used magnetic tornadoes previously described in the literature [e.g., Wedemeyer et al 2013, ApJ; Mghebrishvili et al. 2015 ApJ]. Our method made it possible to detect those structures, as well as to reveal previously unknown magnetic tornadoes.

How to cite: Vorobev, D., Blumenau, M., Fridman, M., Khabarova, O., and Obridko, V.: Automatic detection of solar magnetic tornadoes based on computer vision methods., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11501, https://doi.org/10.5194/egusphere-egu22-11501, 2022.

The large amount of data produced by measurements and simulations of space plasmas has made it fertile ground for the application of classification methods, that can support the scientist in preliminary data analysis. Among the different classification methods available, Self Organizing Maps, SOMs [Kohonen, 1982] offer the distinct advantage of producing an ordered, lower-dimensional representation of the input data that preserves their topographical relations. The 2D map obtained after training can then be explored to gather knowledge on the data it represents. The distance between nodes reflects the distance between the input data: one can then further cluster the map nodes to identify large scale regions in the data where plasma properties are expected to be similar.

In this work, we train SOMs using data from different simulations of different aspects of the heliospheric environment: a global magnetospheric simulation done with the OpenGGCM-CTIM-RCM code, a Particle In Cell simulation of plasmoid instability done with the semi-implicit code ECSIM, a fully kinetic simulation of single X point reconnection done with the Vlasov code implemented in MuPhy2.

We examine the SOM feature maps, unified distance matrix and SOM node weights to unlock information on the input data. We then classify the nodes of the different SOMs into a lower and automatically selected number of clusters, and we obtain, in all three cases, clusters that map well to our a priori knowledge on the three systems. Results for the magnetospheric simulations are described in Innocenti et al, 2021. 

This classification strategy then emerges as a useful, relatively cheap and versatile technique for the analysis of simulation, and possibly observational, plasma physics data.

Innocenti, M. E., Amaya, J., Raeder, J., Dupuis, R., Ferdousi, B., & Lapenta, G. (2021). Unsupervised classification of simulated magnetospheric regions. Annales Geophysicae Discussions, 1-28. 

https://angeo.copernicus.org/articles/39/861/2021/angeo-39-861-2021.pdf

How to cite: Innocenti, M. E., Köhne, S., Hornisch, S., Grauer, R., Amaya, J., Raeder, J., Ferdousi, B., Edmond, J. "., and Lapenta, G.: A versatile exploration method for simulated data based on Self Organizing Maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12480, https://doi.org/10.5194/egusphere-egu22-12480, 2022.

Generating reliable databases of electron density measurements over a wide range of geomagnetic conditions is essential for improving empirical models of electron density. The Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm has been developed for automated extraction of electron density from Van Allen Probes electric field measurements, and has been shown to be in good agreement with existing semi-automated methods and empirical models. The extracted electron density data has since then been used to develop the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model, an empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices.
In this study we re-implement the NURD algorithm in both Python and Matlab, and compare the performance of these implementations to each other and previous NURD results. We take advantage of a labeled training data set now being available for the full duration of the Van Allen Probes mission to train the network and generate an electron density data set for a significantly longer time period. We perform detailed comparisons between this output, electron density produced from Van Allen Probes electric field measurements using the AURA semi-automated algorithm, and electron density obtained from existing empirical models. We also present preliminary results from the PINE plasmasphere model trained on this extended NURD electron density data set.

How to cite: Szabo-Roberts, M., Kume, K., Smirnov, A., Zhelavskaya, I., and Shprits, Y.: Re-implementing and Extending the NURD Algorithm to the Full Duration of the Van Allen Probes Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12830, https://doi.org/10.5194/egusphere-egu22-12830, 2022.

Mercury is the only telluric planet of the solar system, other than Earth, with an intrinsic magnetic field. Thus, the Hermean surface is shielded from the impinging solar wind via the presence of an “Earth-like” magnetosphere. However, this cavity is twenty times smaller than its alike at the Earth. The relatively small extension of the Hermean magnetosphere enables us to model it using global full-kinetic simulation with the aid of modern supercomputers. Such modeling is crucial to interpret, and prepare, the future observations of the ongoing joint ESA-JAXA mission BepiColombo.

The model used in this work is based on three-dimensional, implicit full-PIC simulations of the interaction between the solar wind and Mercury’s magnetosphere (i.e. at 0.3-0.47 AU). This model includes self-consistently the ion and electron physics down to kinetic electron scales. On top of that, we show comparisons between in-situ observations by Mariner-X and BepiColombo space missions. This comparison allows us (i) to validate our model and (ii) to gain insights into the electron dynamics in the Hermean environment, thought to be governed by kinetic-scale processes.

First, we validate our model through a qualitative comparison between three-dimensional outcomes of our global simulations and the ones of reduced fluid/hybrid simulations (in the context of the SHOTS collaboration). Moreover, comparison with in-situ Mariner-X observations during its first Mercury flyby complete the validation of our model. Second, we study the global dynamics of electrons showing regions where strongest particle acceleration/energization occurs, giving quantitative estimate of electron temperature anisotropy in the Hermean environment. Such results are used to interpret past, and plan future, BepiColombo in-situ observations.

How to cite: Lavorenti, F., Henri, P., Califano, F., Deca, J., Aizawa, S., and Andre, N.: Full-kinetic global simulations of the plasma environment at Mercury: a model from planetary to electrons scales to support BepiColombo, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-62, https://doi.org/10.5194/egusphere-egu22-62, 2022.

We analyzed MAVEN observations of fields and plasma signatures associated with an encounter of fully-developed Kelvin–Helmholtz (K–H) vortices at the northern polar terminator along Mars’ induced magnetosphere boundary. The signatures of the K–H vortices event are: (i) quasi-periodic, “bipolar-like” sawtooth magnetic field perturbations, (ii) corresponding density decrease, (iii) tailward enhancement of plasma velocity for both protons and heavy ions, (iv) co-existence of magnetosheath and planetary plasma in the region prior to the sawtooth magnetic field signature (i.e. mixing region of the vortex structure), and (v) pressure enhancement (minimum) at the edge (center) of the sawtooth magnetic field signature. Our results strongly support the scenario for the non-linear growth of K–H instability along Mars’ induced magnetosphere boundary, where a plasma flow difference between the magnetosheath and induced-magnetospheric plasma is expected. Our findings are also in good agreement with 3-dimensional local magnetohydrodynamics (MHD) simulation results. MAVEN observations of protons with energies greater than 10 keV and results from the Walén analyses suggests the possibility of particle energization within the mixing region of the K–H vortex structure via magnetic reconnection, secondary instabilities or other turbulent processes. We estimated the lower limit on the K–H instability linear growth rate to be ~5.84 x 10^-3 s^-1. For these vortices, we estimate the lower limit of the instantaneous atmospheric ion escape flux due to the detachment of plasma clouds during the late non-linear stage of K–H instability to be ~5.90 x 10²⁶ particles/s, which is agrees with earlier studies for the Venusian plasma clouds but ~two orders of magnitude larger than that calculated for Mars. 

How to cite: Poh, G., Espley, J., Nykyri, K., Fowler, C., Ma, X., Xu, S., Hanley, G., Romanelli, N., Bowers, C., Gruesbeck, J., and DiBraccio, G.: On the Growth and Development of Non-linear Kelvin-Helmholtz Instability at Mars: MAVEN Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-169, https://doi.org/10.5194/egusphere-egu22-169, 2022.

Venus’ lack of an intrinsic magnetic field allows the solar wind to closely interact with its atmosphere [1], making it a
prime target for investigating how unmagnetized atmospheric bodies in our Solar System [2] or elsewhere [3] interact
with magnetized plasma flows. This close interaction means that solar-activity correlations exhibited by the solar wind and
other heliospheric parameters [4, 5] cause solar-cycle variations in Venus’ plasma environment and plasma phenomena. We
investigate these variations by characterizing the proton population around Venus during periods of solar minimum (2006–2009)
and maximum (2010–2014). We use data from the Ion Mass Analyser (IMA) instrument, a particle mass-energy spectrometer
which was onboard the Venus Express (VEX) mission. We apply a previously developed methodology which fits Maxwellian
models to measurements of the protons’ velocity distribution functions [6] to produce statistical distributions of bulk speeds and
temperatures in various regions of Venus’ plasma environment. We also present spatial maps and probability-density histograms
comparing the proton parameters between the two time periods.
We find that the temperatures perpendicular (T_⊥) and parallel (T) to the background magnetic field are 20–35% lower
in the magnetosheath during solar maximum. This suggests that the heating of particles as they cross the bow shock varies
between the two time periods. We also find that the regions in the magnetosheath with highest temperature ratio T_⊥/T are
farther downstream from the bow shock during solar maximum than minimum. This is consistent with previous observations of
how mirror-mode structures presumably generated at the bow shock strictly decay as they are convected into the magnetosheath
during solar minimum, whereas during solar maximum they first grow and then decay [7]. We also present ongoing work to
further characterize the plasma environment as a function of upstream solar-wind parameters (such as Mach number or cone
angle) and bow shock geometry. We discuss preliminary results concerning energy conversion processes at Venus’ bow shock.

REFERENCES
[1] Y. Futaana, G. Stenberg Wieser et al., “Solar Wind Interaction and Impact on the Venus Atmosphere,” Space Science Reviews, vol. 212, no. 3-4, 2017.
[2] C. Bertucci, F. Duru et al., The induced magnetospheres of mars, venus, and titan, 2011, vol. 162, no. 1-4.
[3] C. Dong, M. Jin et al., “Atmospheric escape from the TRAPPIST-1 planets and implications for habitability,” Proceedings of the National Academy of
Sciences of the United States of America, vol. 115, no. 2, 2017.
[4] C. T. Russell, E. Chou et al., “Solar and interplanetary control of the location of the Venus bow shock,” Journal of Geophysical Research, vol. 93, no. A6, 1988.
[5] P. R. Gazis, “Solar cycle variation in the heliosphere,” Reviews of Geophysics, vol. 34, no. 3,  1996.
[6] A. Bader, G. Stenberg Wieser et al., “Proton Temperature Anisotropies in the Plasma Environment of Venus,” Journal of Geophysical Research: Space
Physics, vol. 124, no. 5, 2019.
[7] M. Volwerk, D. Schmid et al., “Mirror mode waves in Venus’s magnetosheath: Solar minimum vs. solar maximum,” Annales Geophysicae, vol. 34, no. 11, 2016.

How to cite: Rojas Mata, S., Stenberg Wieser, G., Futaana, Y., Bader, A., Persson, M., Fedorov, A., and Zhang, T.: Proton Temperature Anisotropies in the Venus Plasma Environment during Solar Minimum and Maximum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-653, https://doi.org/10.5194/egusphere-egu22-653, 2022.

In 2020 and 2021 both BepiColombo and Solar Orbiter used Venus for a gravity assist in order to reach Mercury and to finally get into the correct orbit around the Sun, respectively. These flybys were the first since Mariner 10 to sample a long stretch, more than 30 Venus radii of the induced magnetotail of Venus. This brought the opportunity to study the structure and dynamics of the tail during different solar wind conditions. On this poster we will discuss the differences and also the similarities (even though the four flybys took different trajectories through the induced magnetotail) using the magnetometers on both spacecraft. Field line draping, magnetic reconnection, and plasma waves will all pass by on stage.

How to cite: Volwerk, M. and the The VenusMagTeam: Two Spacecraft, Four Flythroughs: Magnetometer Measurements by BepiColombo and Solar Orbiter in the Induced Magnetotail of Venus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-822, https://doi.org/10.5194/egusphere-egu22-822, 2022.

The Martian interaction with the solar wind leads to the formation of a bow shock upstream of the planet. The shock dynamics appears complex, due to the combined influence of external (solar photons, solar wind plasma and fields) and internal (crustal magnetic fields, ionized atmosphere) drivers. The extreme ultraviolet fluxes and magnetosonic mach number are known major drivers of the shock location, while the influence of other possible drivers is less constrained or unknown such as crustal magnetic fields or the solar wind dynamic pressure and the Interplanetary Magnetic Field (IMF) intensity and orientation.

We analyze and rank the influence of the main drivers of the Martian shock location, based on published datasets from Mars Express and Mars Atmosphere Volatile EvolutioN missions and on several methods such as the Akaike Information Criterion, Least Absolute Shrinkage Selection Operator regression, and partial correlations. We include here the influence of the crustal fields, extreme ultraviolet fluxes, magnetosonic mach number, solar wind dynamic pressure and various Interplanetary Magnetic Field parameters (intensity and orientation angles).

We conclude that the major drivers of the shock location are extreme ultraviolet fluxes and magnetosonic mach number, while crustal fields and solar wind dynamic pressure are secondary drivers at a similar level. The IMF orientation also plays a significant role, with larger distances for perpendicular shocks rather than parallel shocks.

How to cite: Garnier, P., Jacquey, C., Gendre, X., Génot, V., Mazelle, C., Fang, X., Gruesbeck, J., Sanchez-Cano, B., and Halekas, J.: Ranking the drivers of the Martian bow shock location: a statistical analysis of Mars Atmosphere and Volatile EvolutioN and Mars Express observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1609, https://doi.org/10.5194/egusphere-egu22-1609, 2022.

For more than two years, ESA’s Rosetta mission measured the complex and ever-evolving plasma environment surrounding comet 67P/Churyumov-Gerasimenko. In this work, we explore the structure and dynamics of the near-comet plasma environment at steady state, comparing directly the results of a spherically symmetric Haser model and an asymmetric outgassing profile based on the measurements from the ROSINA instrument onboard Rosetta during 67P’s weakly outgassing stages. Using a fully kinetic semi-implicit particle-in-cell code, we are able to characterise (1) the various ion and electron populations and their interactions, and (2) the implications to the mass-loading process caused by taking into account asymmetric outgassing. Our model complements observations by providing a full 3D picture that is directly relevant to help interpret the measurements made by the Rosetta Plasma Consortium instruments. In addition, understanding such details better is key to help disentangle the physical drivers active in the plasma environment of comets visited by future exploration missions.

How to cite: Deca, J., Stephenson, P., Divin, A., Henri, P., and Galand, M.: A Fully Kinetic Perspective on Weakly Active Comets: Symmetric versus Asymmetric Outgassing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1814, https://doi.org/10.5194/egusphere-egu22-1814, 2022.

On August 10, 2021, the Mercury-bound BepiColombo spacecraft flew for the second time by Venus for a Gravity-Assist Maneuver. During this second flyby of Venus, a limited number of instruments were turned on, allowing unique observations of the planet and its environment. Among these instruments, the Mass Spectrum Analyzer (MSA) that is part of the particle analyzer consortium onboard the magnetospheric orbiter (Mio) was able to acquire its first plasma composition measurements in space. As a matter of fact, during a limited time interval upon approach of the planet, substantial ion populations were recorded by MSA, with characteristic energies ranging from about 20 eV up to a few hundreds of eVs. Comparison of the measured Time-Of-Flight spectra with calibration data reveals that these populations are of planetary origin, containing both Oxygen and Carbon ions. The Oxygen observations are to some extent consistent with previous in situ measurements from mass spectrometers onboard Venus Express and Pioneer Venus Orbiter. On the other hand, the MSA data provide the first ever in situ evidences of Carbon ions in the near-Venus environment at about 6 planetary radii. We show that the abundance of C+ amounts to about ~30% of that of O+. Furthermore, the fact that photoelectrons are simultaneously observed with the low energy planetary ions indicate a magnetic connection to the dayside ionosphere from which ions are ejected under the effect of the ambipolar electrostatic field.

How to cite: Hadid, L. and the MSA, MIA and MEA teams: First evidence of carbon escape through Venus magnetosheath along draped magnetic field lines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1821, https://doi.org/10.5194/egusphere-egu22-1821, 2022.

Solar energetic particles (SEPs) are high-energetic particles that consist mainly of electrons and protons with energies from a few tens of keV to GeV ejected  associated with solar flares and coronal mass ejections. SEPs can precipitate into planetary atmospheres cause ionization, excitation and dissociation of atmospheric molecules, leading to changes in atmospheric chemical composition via chemical network [e.g. Solomon et al., 1981; Adams et al., 2021].

The effect of SEPs on ozone concentration in the Earth’s polar region has been intensively studied for the past decades. For instance, during the enormous solar flare that occurred in late October 2003, NO_x and HO_x concentrations were enhanced and ozone concentration was depleted by 40% at the polar lower mesosphere [e.g. Jackman et al., 2005]. Increased ionization and dissociation of atmospheric N₂ and O₂molecules led to the production of NOx and HOx, which catalytically destroyed ozone at the polar mesosphere.

Recently, the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft has discovered global diffuse aurora on the nightside of Mars down to few tens km in altitude during SEP events, indicating that a significant amount of energy could be deposited in the atmosphere deeper than previously thought  [Schneider et al., 2015; Nakamura et al., 2022]. However, the effects of SEPs on the atmospheric chemistry of present-day Mars have not yet been investigated by observations and/or models.

By coupling a Monte Carlo model PTRIP (Nakamura et al., 2022) and a newly developed photochemical model to investigate the effects of SEPs on the atmospheric compositions at Mars, we performed a simulation to track the effects of a large SEP event on the Martian atmospheric composition. We found that HO_x increased by a factor of 10 and ozone decreased by a factor of 10 in the altitude range from 20 km to 60 km. This is the very first estimation of the effects of SEPs on the atmospheric neutral compositions at Mars, indicating that similar effects on HO_x and ozone could be expected on Mars than on Earth.

How to cite: Nakamura, Y., Terada, N., Leblanc, F., Nakagawa, H., Sakai, S., Hiruba, S., Kataoka, R., and Murase, K.: Numerical prediction of the effects of solar energetic particle precipitation on the Martian atmospheric chemical composition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2181, https://doi.org/10.5194/egusphere-egu22-2181, 2022.

The Martian crustal magnetic anomalies create a varied, asymmetric obstacle for the draped interplanetary magnetic field (IMF) to interact with. One possible result of this interaction is magnetic reconnection, a process by which anti-parallel magnetic field lines connect and reconfigure, transferring energy into the surrounding environment and mixing previously separated plasma populations. Here, we present an analysis to determine the draped IMF conditions that favor reconnection with the underlying crustal anomalies at Mars. First, we plot a map of the crustal anomalies’ strength and orientation compiled from magnetic field data taken throughout the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. Second, we create “shear maps” which calculate and plot the angle of shear between the transverse component of the anomalies and a chosen overlaid draping direction. Third, we define a “shear index” which quantifies the susceptibility of a particular region to undergo reconnection based on a given draped IMF orientation and the resulting shear map for that region. We then compare the shear index for a variety of draped field orientations within different regions of the Martian magnetosphere. Our results suggest eastward/westward (horizontal) draped fields present regions that are more likely for anti-parallel magnetic reconnection to occur with the crustal anomalies than northward/southward (vertical) draped fields, with one notable exception being the strongest crustal anomalies located in the southern hemisphere ~180° longitude. An east/west draped field roughly corresponds to a +/- B_y IMF direction on the dayside, implying the rate of magnetic reconnection on the dayside of Mars may be enhanced for IMF field lines pointing in the +/- Y_MSO direction compared to that of IMF field lines pointing in the +/- Z_MSO direction, with MSO referring to the Mars Solar Orbital coordinate system. Understanding the interplay between Mars’s crustal magnetic fields and the IMF is crucial to answer outstanding science questions regarding nightside magnetospheric activity at Mars, namely how IMF orientation affects the twisting of the magnetotail, open magnetic topology observations on the nightside, and discrete aurora observations in the southern hemisphere.

How to cite: Bowers, C. F., DiBraccio, G. A., Slavin, J. A., Gruesbeck, J. R., Weber, T., Romanelli, N., Azari, A. R., and Xu, S.: Exploring the solar wind-planetary interaction at Mars: Implication for Magnetic Reconnection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2853, https://doi.org/10.5194/egusphere-egu22-2853, 2022.

We investigate the Venusian magnetotail and its boundaries utilising magnetic field and density measurements that cover a wide range of radial distances, from the two geometrically similar Solar Orbiter Venus flybys on 27 December 2020 and 9 August 2021. We look at the magnetic field components along the spacecraft trajectory in an attempt to identify boundary crossings, as well as the extent and intensity of the bowshock deep in the magnetotail. We compare these observations with results of a simulation of the induced magnetosphere and magnetotail of Venus, where the initial upstream conditions are provided by Solar Orbiter measurements, to examine in what degree the simulation representation agrees with the observations. The model encloses a massive volume of 80R_V x 60R_V x 60R_V  in which we look at magnetic field and proton density variations. Additionally, we vary the rotation of the clock angle in order to find for which rotation angle we get the best match with the observations during the different steps of the spacecraft's trajectory. 

How to cite: Stergiopoulou, K., Jarvinen, R., Andrews, D. J., Edberg, N. J. T., Dimmock, A. P., Kallio, E., and Khotyaintsev, Y.: Solar Orbiter Data-Model Comparison in Venus' Induced Magnetotail, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3658, https://doi.org/10.5194/egusphere-egu22-3658, 2022.

As Venus does not have an intrinsic magnetic field, the solar wind interacts directly with the Venusian atmosphere, altering its structure and composition, for example through atmospheric ion escape to space. In particular, the interaction will result in the formation of plasma boundaries, which separate regions of different plasma populations around Venus. Knowing how space weather influences the shape of these boundaries is one of the key pieces to understanding the current state of the Venusian atmosphere.

During its eight years mission, including more than 3000 orbits around Venus, Venus Express made measurements of the plasma environment, covering a wide range of upstream conditions. Using conjoint plasma and magnetic field measurements from the ASPERA-4 (Analyser of Space Plasma and Energetic Atoms) and the magnetometer instruments, we identified the locations where the spacecraft crossed the bow shock and the ion composition boundary for each orbit. Using the derived dataset, we then determined the boundary shapes with a two-parameter fit. The boundary shape fittings were done with respect to one or multiple upstream conditions.

Here we report that both boundaries are highly dependent on solar wind extreme ultraviolet (EUV) flux, expanding further from the planet at solar maximum. A likely explanation is that at solar maximum, combined heating of the exosphere ions and a higher photoionization rate lead to a higher planetary ion production. These additional ions increase the internal thermal pressure, pushing the boundaries outward.

Additionally, at solar minimum, solar wind parameters like dynamic pressure and energy flux were found to not affect the shape of the bow shock, which is consistent with previous studies. The influence of the strength and orientation of the interplanetary magnetic field, the Mach number, and potential correlations between multiple upstream parameters, are also discussed in this talk.

How to cite: Signoles, C., Persson, M., Wolff, A., Martinez, N., Lindwall, V., Futaana, Y., Rojas-Mata, S., Zhang, T., André, N., Aizawa, S., and Fedorov, A.: Influence of planetary space weather on the shapes of Venus plasma boundaries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4000, https://doi.org/10.5194/egusphere-egu22-4000, 2022.

The 2nd Venus flyby of BepiColombo has been examined and compared by the newly developed global hybrid simulation LatHyS for the Venusian environment. The LatHyS has been first validated by comparison with Venus Express observations, then using the observation from Solar Orbiter, which was located in the upstream region and both observed the same solar wind, it is applied for the Venus flyby. The simulation successfully reproduced the observed signatures and it shows that BepiColombo passed through the stagnation region of Venus, which supports the results obtained by data-analysis. In addition, we have sampled the plasma information along the trajectory and constructed the energy spectrum for three species (solar wind proton, planetary proton, and planetary oxygen ion) and possible effect due to the limited field of view is discussed. Moreover, ion escape from Venus for planetary species have been discussed and the escape rate is estimated. 

How to cite: Aizawa, S., Persson, M., Menez, T., Andre, N., Modolo, R., Barthe, A., Penou, E., Fedorov, A., Sauvaud, J.-A., Leblanc, F., Chaufray, J.-Y., Saito, Y., Yokota, S., Murakami, G., Genot, V., Sanchez-Cano, B., Heyner, D., Horbury, T., Louarn, P., and Owen, C.: LatHyS hybrid simulation of the August, 10 2021 BepiColombo Venus flyby, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4175, https://doi.org/10.5194/egusphere-egu22-4175, 2022.

While space weather has been a growing field of research and applications over the last 15-20 years, “planetary space weather” is an emerging discipline. In fact, as long as we expand our robotic exploration within the solar system, monitoring planetary space weather is becoming more necessary than ever. Despite this, not every spacecraft is designed for plasma science and only a few of them have the necessary plasma instrumentation for space weather purposes. However, all of them have thousands of housekeeping detectors distributed along the spacecraft. In particular, energetic particles impact detectors and subsystems on a spacecraft and their effects can be identified in selected housekeeping data sets, such as the Error detection and correction (EDAC) counters. In this study, we investigate these engineering datasets for scientific purposes by performing the first feasibility study of solar energetic particle detection using EDAC counters from several available ESA Solar System missions, such as Mars Express, Rosetta, BepiColombo and Solar Orbiter. In order to validate the results, these detections are compared to other observations from scientific instruments on board these missions. Moreover, the potential implications of space weather event detections based on EDAC sensors at Mars and Comet 67P/Churyumov-Gerasimenko is analysed. This study has the potential to provide a good network of solar particle observations at locations where no scientific observations of this kind are available.

How to cite: Sanchez-Cano, B., Witasse, O., Knutsen, E. W., Meggi, D., Lester, M., and Wimmer-Schweingruber, R. F. and the ESA mission teams: Space Weather detections with housekeeping sensors onboard Mars Express, Rosetta, BepiColombo and Solar Orbiter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4289, https://doi.org/10.5194/egusphere-egu22-4289, 2022.

We apply a new method, coupling a hybrid plasma model (ions as particles, electrons as a fluid) and measurements from the Mars Atmosphere and Volatile Evolution (MAVEN) mission, to calculate heavy ion escape rates from Mars. With this method, we acquire estimates of the escape rate orbit by orbit in different upstream conditions. We have investigated how the estimated ion escape depends on the assumed composition of heavy ions, the solar wind velocity aberration and the amount of alpha particles in the solar wind. We also estimate the amount of tail escape and radial escape and compare the model results with  recent Mars Express and MAVEN studies.

How to cite: Zhang, Q., Holmström, M., Wang, X., and Fatemi, S.: Estimating heavy ions escape rate from Mars using hybrid model and observations from MAVEN, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4455, https://doi.org/10.5194/egusphere-egu22-4455, 2022.

We study the solar wind interactions of Mercury, Venus and Mars in a global hybrid model, where ions are treated as particles and electrons form a charge-neutralizing fluid. We concentrate on the formation of large-scale, ultra-low frequency (ULF) waves in planetary ion foreshocks and their dependence on solar wind and interplanetary magnetic field conditions in the inner solar system. The ion foreshock forms in the upstream region ahead of the quasi-parallel bow shock, where the angle between the shock normal and the magnetic field is small enough. The magnetic connection to the bow shock allows the backstreaming of solar wind ions leading to the formation of the ion foreshock. This kind of beam-plasma configuration is a source of free energy for the excitation of plasma waves. The foreshock ULF waves convect downstream with the solar wind flow and encounter bow shock and transmit in the downstream region. The analyzed simulation runs use more than two hundred simulation particles per cell on average to allow fine enough velocity space resolution for resolving the foreshocks and waves self-consistently. We find significant differences in wave and foreshock properties between these three planets and discuss their causes.

How to cite: Jarvinen, R., Kallio, E., and Pulkkinen, T.: Global hybrid modeling of ultra-low frequency solar wind foreshock waves at Mercury, Venus and Mars, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5255, https://doi.org/10.5194/egusphere-egu22-5255, 2022.

Mirror mode structures arise whenever a temperature anisotropy is present in the plasma, classically in the wake of the bow shock in a quasi-perpendicular configuration with respect to the interplanetary magnetic field, or from pickup ion distribution effects. Born from space plasma instabilities and in competition with other wave modes, these ultra-low frequency waves contribute to energy exchanges between the different plasma populations present in the magnetosheath. At Mars and Venus, such structures have very similar scales: they last typically a few tens of seconds and appear as peaks or dips in the magnetic field data in antiphase with the local plasma density variations. As magnetometers are present on many space missions, magnetic field-only criteria are an ideal tool to study these structures across different magnetosheath environments. We present here for the first time a comparison of the statistical occurrence of magnetosheath mirror mode-like structures at Mars with MAVEN and at Venus with Venus Express. Based on magnetic field-only measurements, we use identical detection criteria at both planets to select quasi-linear structures in B-field measurements. We then present two-dimensional maps of mirror mode-like occurrence rates with respect to solar cycle variations and EUV flux levels, atmospheric seasons (for Mars) and the nature of the shock crossing (quasi-parallel or quasi-perpendicular configurations), and compare them between planets. Finally, we discuss ambiguities in the nature of the detected structures and their global effects on the magnetosheath.

How to cite: Simon Wedlund, C., Volwerk, M., Mazelle, C., Rojas Mata, S., Stenberg Wieser, G., Mautner, D., Halekas, J., Espley, J., Rojas-Castillo, D., Möstl, C., and Bertucci, C.: Mirror mode-like structures around unmagnetised planets: a comparison between the magnetosheaths of Mars and Venus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5413, https://doi.org/10.5194/egusphere-egu22-5413, 2022.

The Rosetta spacecraft orbited the comet 67P/Churuymov-Gerasimenko for approximately two years, primarily remaining close to the nucleus, unlike previous cometary flyby missions. The combination of Rosetta's close orbit and comet 67P's relatively low cometary activity make detections of the bow shock difficult. However, magnetosheath-like proton distributions have been observed, indicating Rosetta indeed was downstream of a bow shock, during periods of higher cometary activity. Here, we search the Ion Composition Analyzer (ICA) data for additional evidence of the cometosheath, the region downstream of the bow shock analogous to a magnetosheath. We examine the proton velocity distributions for high time and spatial variability that is not correlated with changes in the electric or magnetic fields. We present an overview of cometosheath detections and a discussion of the relation between the cometosheath and bow shock properties. Other work shows that the electric potential of the solar wind can be retrieved from the differential slowing of the solar wind species, so we compare time periods with a high electric potential to cometosheath detections, as a high potential can also indicate shock formation.

How to cite: Williamson, H., Nilsson, H., Stenberg Wieser, G., and Moeslinger, A.: Cometosheath observations around comet 67P/Churyumov-Gerasimenko, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5627, https://doi.org/10.5194/egusphere-egu22-5627, 2022.

During Rosetta’s 2-year observation period of comet 67P/Churyumov-Gerasimenko, the Ion Composition Analyser (ICA) continuously measured the plasma environment around the comet. The interaction of the solar wind with the cometary plasma and the evolution of the observed solar wind over the course of the mission has been subject of previous studies. It usually shows a single proton population with a large anti-sunward component that gets more and more deflected when the comet approaches perihelion. 

In this study we focus on ICA data obtained during the 19th of April 2016, where we detected two clear peaks in the energy spectra of the proton population. For the level of cometary activity during this time period, a few months after perihelion, a deflected single population is characteristic for the solar wind protons. We attempt to separate these two observed proton populations in the mass-separated ICA data. We then analyse selected plasma properties of the two populations, such as flow velocity (magnitude and direction) and temperatures. This dual proton population is sporadically observed throughout the day, but is otherwise uncommon during the mission. We want to study how these occurrences are related to changes in the cometary environment and the interaction with the solar wind.

A previous study has shown that the difference in proton and alpha particle velocity downstream of a shock can be used to estimate the electrostatic potential of the observation point relative to the solar wind. We take a look on how to interpret the electrostatic potential estimate using the newly estimated proton velocities of both populations.

How to cite: Moeslinger, A., Nilsson, H., Stenberg Wieser, G., and Williamson, H.: Observation of dual proton populations by the Rosetta Ion Composition Analyser, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5648, https://doi.org/10.5194/egusphere-egu22-5648, 2022.

 Rosetta followed comet 67P at heliocentric distances from 1.25 to 3.6 au. The solar wind was observed for much of this time, but significantly deflected and to some extent slowed down by the interaction with the coma. A method is derived to reconstruct the upstream solar wind from H⁺ and He²⁺ observations. The method is based on the assumption that the comet - solar wind interaction can be described by an electric potential that is the same for both H⁺ and He^2+. The reonstructed speed is compared to estimates from the Tao model, as well as OMNI and Mars Express data propagated to the observation point. The reconstruction agrees well with the Tao model for most of the observations, in particular the statistical distribution of solar wind speed. The electrostatic potential relative to the upstream solar wind is derived and shows values from a few tens of V at large heliocentric distances to about 1 kV during solar events and close to perihelion. Reconstructed values of the solar wind for periods of high electrostatic potential are also in good agreement with propagated observations and model results. The Tao model captures some slowing down of high speed streams as compared to observations at Earth or Mars. At low solar wind speeds, below 400 km/s, agreement is better between our reconstruction and Mars observations than with the Tao model. The magnitude of the reconstructed electrostatic potential is a good measure of the slowing down of the solar wind at the observation point.

How to cite: Nilsson, H., Möslinger, A., Williamson, H., Bergman, S., and Stenberg Wieser, G.: Reconstruction of the upstream solar wind at comet 67P, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5973, https://doi.org/10.5194/egusphere-egu22-5973, 2022.

Recent spacecraft observations have revealed the averaged global morphology of the magnetospheric current system of Mars. This current system is generated by the induction of the interplanetary magnetic field and the motional electric field of the solar wind. It couples the ionosphere below and the solar wind above and determines the distribution of the energy inputs from the fast-moving solar wind to the planetary atmospheric ions.

We use Amitis, a GPU-based hybrid (particle ions and fluid electrons) numerical model to study the current system. Under the typical space environment condition, we successfully reproduce the morphology of the observed current system, including the bow shock current, the induced magnetospheric boundary current, and the ionospheric current.

With the full information provided by the model, we can calculate the inner product of the electric field intensity and the current density for any location in the simulation domain. Furthermore, we can separate the currents due to solar wind and planetary ions, and separate the electric field terms caused by different mechanisms, thereby clarifying the contribution of different mechanisms to the ion escape in the solar wind interaction with Mars. 

How to cite: Wang, X. and Fatemi, S.: Global Current System of Martian Induced Magnetosphere: a Hybrid View, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6298, https://doi.org/10.5194/egusphere-egu22-6298, 2022.

The current work investigates the Venusian solar-wind-induced magnetosphere at a high spatial resolution using all Venus Express (VEX) magnetic observations through an unbiased statistical method. We first evaluate the predictability of the interplanetary magnetic field (IMF) during VEX's Venusian magnetospheric transits and then map the induced field in a cylindrical coordinate system under different IMF conditions. Our mapping resolves structures on various scales, ranging from the ionopause to the classical IMF draping. We also resolve two recently reported structures, a low-ionosphere magnetization over the terminator, and a global "looping" structure in the near magnetotail. In contrast to the reported IMF-independent cylindrical magnetic field of both structures, our results illustrate their IMF dependence. In both structures, the cylindrical magnetic component is more intense in the hemisphere with an upward solar wind electric field (E^SW) than in the opposite hemisphere. Under downward E^SW, the looping structure even breaks, which is attributable to an additional draped magnetic field structure wrapping toward −E^SW. In addition, our results suggest that these two structures are spatially separate. The low-ionosphere magnetization occurs in a very narrow region, at about 88°–95° solar zenith angle and 185–210 km altitude. A least-squares fit reveals that this structure is attributable to an antisunward line current with 191.1 A intensity at 179 ± 10 km altitude, developed potentially in a Cowling channel.

How to cite: He, M., Vogt, J., Dubinin, E., Zhang, T., and Rong, Z.: Spatially Highly Resolved Solar-wind-induced Magnetic Field on Venus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6696, https://doi.org/10.5194/egusphere-egu22-6696, 2022.

During the last decade, MAVEN space mission have emphasized a widespread spatial distribution of escaping O+ ions (Brain et al., 2015; Dong et al., 2015; Curry et al., 2015). Statistical studies have demonstrated that such structure is constant and present an asymmetry with respect to the solar wind convective electric field direction. In the Mars Solar Ecliptic coordinate system, continuous large O+ ion fluxes have been observed from the Martian wake to the Northward hemisphere. Global hybrid models have been developed since more than fiffteen years (Modolo et al., 2005, 2016; Brecht and Ledvina, 2006; Kallio et al., 2006) predicting and reproducing successfully the main characteristics of these escaping ion signatures. To further characterize this heavy-ion escape and its variability due to the solar wind forcing, global hybrid simulations have been performed with different set of upstream solar wind parameters. The impact of the solar wind drivers on the dynamics of O+ ion fluxes are reported and compared to the statistical ion fluxes maps derived from MAVEN/STATIC observations (Dong et al., 2015).

Brain, D. A., McFadden, J. P., Halekas, J. S., Connerney, J. E. P., Bougher, S. W., Curry, S., et al. (2015). The spatial distribution of planetary ion fluxes near Mars observed by MAVEN. Geophys. Res. Lett. 42, 9142–9148. doi:10.1002/2015GL065293

Dong, Y., Fang, X., Brain, D. A., McFadden, J. P., Halekas, J. S., Connerney, J. E., et al. (2015). Strong plume fluxes at Mars observed by MAVEN: An important planetary ion escape channel. Geophys. Res. Lett. 42, 8942–8950. doi:10.1002/2015GL065346

Curry, S. M., Luhmann, J. G., Ma, Y. J., Dong, C. F., Brain, D., Leblanc, F., et al. (2015). Response of Mars O+ pickup ions to the 8 March 2015 ICME: Inferences from MAVEN data-based models. Geophys. Res. Lett. 42, 9095–9102. doi:10.1002/2015GL065304

Modolo, R., Chanteur, G. M., Dubinin, E., and Matthews, A. P. (2005). Influence of the solar EUV flux on the Martian plasma environment. Annales Geophysicae 23, 433–444. doi:10.5194/angeo-23-433-2005

Brecht, S. H. and Ledvina, S. A. (2006). The Solar Wind Interaction With the Martian Ionosphere/Atmosphere 126, 15–38. doi:10.1007/s11214-006-9084-z

Kallio, E., Fedorov, A., Budnik, E., Sa¨les, T., Janhunen, P., Schmidt, W., et al. (2006). Ion escape at Mars: Comparison of a 3-D hybrid simulation with Mars Express IMA/ASPERA-3 measurements 182, 350–359. doi:10.1016/j.icarus.2005.09.018

How to cite: Modolo, R., Leblanc, F., Chaufray, J.-Y., Romanelli, N., Dubinin, E., Génot, V., Baskevitch, C., Brain, D., Curry, S., and Lillis, R.: Modeling the variability of Martian O+ ion escape due to Solar Wind forcing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7665, https://doi.org/10.5194/egusphere-egu22-7665, 2022.

               Galilean moons are embedded in Jupiter’s giant magnetosphere. The Jovian plasma particles interact with the atmosphere of the moons, exchanging momentum and energy, and generate different phenomena such as aurora, electric current, etc.

The exploration of the Galilean moons, and in particular Ganymede and Europa, considered as potential habitats, are listed among the main objectives of the ESA JUpiter ICy moon Explorer (JUICE) mission. In preparation for future observations, a modelling effort is conducted to describe the Europa moon-magnetosphere system.

               We have used the LATMOS Hybrid Simulation (LatHyS) model to characterize the Jovian plasma and magnetic field interaction with the moon and its atmosphere. The model is a hybrid 3D, multi-species and parallel simulation model which is based on a kinetic description of ions and a fluid description of electrons. The model is based on the CAM-CL algorithm and various physical processes has been implemented to describe the solar wind (or a magnetospheric plasma) interaction with Mars, Mercury, Titan, Ganymede, Earth-like body etc… (Matthews, 1994, Modolo et al, 2016, Richer et al, 2012, Modolo et al, 2008, Leclercq et al, 2015, Turc et al, 2015).  This simulation model depicts the dynamic and the structure of the ionized environment in the neighborhood of these bodies. Recently, the model has been adapted to Europa-Jupiter interaction. Global simulation results are compared to Galileo observations and will be used to illustrate the conditions that JUICE might encounter during its flybys.

         
References :

Alan P. Matthews, Current Advance Method and Cyclic Leapfrog for 2D Multispecies Hybrid Plasma Simulations, Journal of Computational Physics, Volume 112, Issue 1, 1994, Pages 102-116, ISSN 0021-9991, https://doi.org/10.1006/jcph.1994.1084.

Turc L., Fontaine D., Savoini P., Modolo R., 3D hybrid simulations of the interaction of a magnetic cloud with a bow shock, JGR, 2015

Richer E, Modolo R, Chanteur GM, Hess S and Leblanc F, A Global Hybrid Model for Mercury's Interaction With the Solar Wind: Case Study of the Dipole Representation, Journ. Geophys. Res., doi:10.1029/2012JA017898, 2012

Leclercq L., Modolo R., Leblanc F., Hess S., Mancini M. ,3D Magnetospheric parallel hybrid multi-grid method applied to planet-plasma interactions, Journal of Computational Physics, 309, pp.295-313, 10.1016/j.jcp.2016.01.005, 2016

Modolo R., Hess S., Mancini M., Leblanc F., Chaufray J.-Y., Brain D., Leclercq L., Esteban Hernandez R., Chanteur G., Weill P., Gonzalez-Galindo F. et al., Mars-solar wind interaction: LatHyS, an improved parallel 3-D multispecies hybrid model, Journal of Geophysical Research : Space Physics, American Geophysical Union/Wiley, 2016, 121 (7), pp.6378-6399.10.1002/2015JA022324, 2016

How to cite: Baskevitch, C.-A., Modolo, R., and Cecconi, B.: Europa’s interaction with the Jovian plasma from hybrid simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7692, https://doi.org/10.5194/egusphere-egu22-7692, 2022.

Induced magnetospheres of non-magnetized atmospheric bodies like Mars and Venus are formed by magnetic fields of ionospheric currents induced by the convective electric field E = - V x B/c of the solar wind. When the interplanetary magnetic field is mostly radial (the cone angle θ is close to 0°, quasi-parallel conditions) and the convective field E ≈ 0, an induced magnetosphere becomes degenerate. The degenerate induced magnetospheres can be considered as a specific type of the interaction with ambient plasma. This type of interaction were observed at Venus and Mars, for example, 12 observed cases for Venus and 17 observed cases for Mars for θ < 10° as recorded by Venus Express (2006-2014) and Mars express (2014-2019). However, the quasi-parallel conditions are nominal for the majority of discovered exoplanets (hot Jupiters) orbiting the parent stars on distances 0.01 – 0.1 au when θ < 4° (assuming the solar conditions). The conditions at some moons of icy giants, Neptune (Triton) and Uranus, are also quasi-parallel due to large angle between magnetic dipole and the rotation axis though the plasma flow is subsonic.

In this report we introduce degenerate induced magnetospheres as a new type of interaction and review the current works on the subject. We also show examples of observations at Mars and Venus and numerical simulations, and describe the main properties and basic physics of such configurations.

How to cite: Barabash, S., Holmström, M., Yoshifumi, F., Zhang, Q., and Ramstad, R.: Degenerate induced magnetospheres, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7952, https://doi.org/10.5194/egusphere-egu22-7952, 2022.

The magnetosheath is defined as the plasma region between the bow shock, where the super-magnetosonic solar wind plasma is decelerated and heated, and the outer boundary of the intrinsic planetary magnetic field, the so-called magnetopause. Recently we  presented an analytical magnetosheath plasma flow model around Mercury, which can be used to estimate the plasma flow magnitude and direction at any given point in the magnetosheath exclusively on the basis of the plasma parameters of the upstream solar wind. However, this model assumes a constant plasma density and velocity along the flowlines. Here we present a more sophisticated model were we take hydrodynamic effects into account, to also obtain the density and velocity change along the flowline. The model serves as a useful tool to trace the magnetosheath plasma along the streamline both in a forward sense (away from the shock) and a backward sense (toward the shock), offering the opportunity of studying the growth or damping rate of a particular wave mode or evolution of turbulence energy spectra along the streamline in view of upcoming arrival of BepiColombo at Mercury.

How to cite: Schmid, D., Narita, Y., Plaschke, F., Volwerk, M., Nakamura, R., and Baumjohann, W.: A magnetosheath hydrodynamic plasma flow model around Mercury, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8557, https://doi.org/10.5194/egusphere-egu22-8557, 2022.

Mars Express and MAVEN observations have demonstrated the influence of Mars’s spatially variable crustal magnetic fields upon the configuration of the plasma in the ionosphere. This influence furthermore leads to variations in ionospheric escape, conceivably in part through the modification of the plasma density and electron temperature in the upper ionosphere. However, quantifying this control remains challenging given the generally dynamic and spatially varied nature of the Mars solar wind interaction, and the therefore naturally varying densities and temperatures of the upper ionosphere in particular. In this study we examine MAVEN Langmuir Probe and Waves data, finding a very clear correspondence between the structure of the crustal fields and both the measured electron temperatures and densities, by first constructing a robust “average” profile from which departures can be quantified. Electron temperatures are shown to be systematically lower in regions of strong crustal fields over a wide altitude range, as has been previously reported. Here, we additionally use measurements made by MAVEN in the solar wind, to explore the dependence of this crustal field control on the coupling to the solar wind and IMF.  We also attempt to quantitatively determine the altitude range over which this coupling between plasma density and temperature and crustal fields is effective.

How to cite: Andrews, D., Andersson, L., Ergun, R., Eriksson, A., Pilinski, M., and Stergiopoulou, K.: Martian crustal magnetic fields and their control of ionospheric plasma densities and temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8569, https://doi.org/10.5194/egusphere-egu22-8569, 2022.

Analysis of nightside nadir-viewing observations taken by MAVEN's Imaging Ultraviolet Spectrograph instrument has identified nearly 200 discrete aurora emissions.  Discrete aurora are sporadic localized ultraviolet emissions originating in the upper Martian atmosphere that occur brightest and most frequently near regions of strong crustal magnetic field strength.  The emission detections were verified and characterized by visual appearance across the disk and spectral analysis of Cameron band and ultraviolet doublet emissions.  No geographic or magnetic field information was used to determine whether a suspected emission was real or an artifact in the data.   Unlike limb observations, nadir observations have no line-of-sight ambiguity, allowing us to locate the emissions with high geographic accuracy.  Nadir viewing also provides global coverage of the nightside disk, giving broad geographic and local time coverage.  We find the same dependence on local time, crustal field strength and interplanetary magnetic field orientation seen in limb observations (Schneider et. al. 2021).