The CAScade Smallsat and Ionospheric Polar Explorer (CASSIOPE) spacecraft containing the enhanced Polar Outflow Probe (e-POP) instrument suite was launched in 2013 by the Canadian Space Agency. In 2018, the spacecraft joined the European Space Agency (ESA) Swarm virtual constellation through ESA’s Third Party Mission program as Swarm-Echo. Here we present the new Swarm-Echo magnetic field data product based on the Magnetic Field instrument (MGF). The Swarm-Echo dataset follows the format and usage conventions of the Swarm Level 1B product definitions as much as possible, though, owing to differences in the platforms and sensors, some adjustments are necessary. Swarm-Echo carries two fluxgate magnetometers but lacks an absolute scalar field sensor. The Swarm-Echo data product is calibrated in-situ based on a vector-vector comparison to the CHAOS magnetic field model that has been enabled by a significantly improved attitude solution. We present algorithm used to create this dataset, the criteria used to select data suitable for calibration, and implementation of the linear inverse algorithm used to solve for the twelve standard magnetometer calibration parameters. Prior to calibrating the sensors, the data are corrected for transients that occur when update the magnetic feedback in the instrument. Then, we reduce the data to one sample per second, and bin it into seven-day intervals. The data are then culled using information from the attitude, bus telemetry, and location files, as well as conditions given by the Kp and Disturbance storm time (Dst) indices. For calibration we select data that falls within ± 55° latitude during geomagnetically quiet times. We consider geomagnetically quiet to be when the Kp index does not exceed 3 or the Dst index does not exceed a change of 3 nT per hour when the data were taken. From the attitude files, we flag any data where the attitude solution was not generated by at least one of the star tracker cameras due to the large (up to 30°) error in solutions generated from the CSS as mentioned in section 4. We also flag any data where the solution has dropped out for greater than ten seconds or there is greater than ten seconds until the next signal is obtained due to potentially large errors when interpolating the attitude solution. Lastly, we flag any data where the angular rotation rate of the spacecraft exceeds 0.03 degrees/sec. The new data product typically has residuals from CHAOS below 10 nT during geomagnetically quiet times and is now operationally produced and distributed for use by the scientific community.

Enlarging datasets of magnetic space-based observations of the Earth’s magnetic field is of interest to space physics and geomagnetism. E.g., geodetic satellites carrying platform magnetometers are suitable missions as they fly in low earth and in near polar orbits. Filling gaps of data of high-precision magnetic missions, such as was the case between CHAMP and Swarm, as well as an increased spatio-temporal coverage make the use of satellite’s platform magnetometer data attractive.
We calibrate and characterize platform magnetometer data of different geodetic satellite missions (GOCE, GRACE-FO, and others) by machine learning techniques. By applying machine learning techniques, the measured magnetometer signal is adapted for artificial disturbances from other satellite payload systems. The proposed non-linear regression can automatically identify relevant features as well as their crosstalk, allowing for a wider range of available inputs. This approach reduces analytical work for the calibration of platform magnetometers, thus leading to faster, more precise, and easily accessible magnetic datasets from non-dedicated missions. The calibrated datasets are made publicly available.
With the use of as much information as possible about the satellite’s payload systems’ activations as possible, our approach models the magnetic behavior in-situ and post-launch. To this aim, data on the temperature, currents as well as telemetric data is collected and incorporated to automatically find relevant inputs for the magnetic behavior of the satellite as a system. In addition, the proposed method automatically identifies arbitrary time shifts in the inputs, e.g., in measurements of the platform magnetometers. In this process, the CHAOS-7 model acts as the reference model which is backed by high-precision (Swarm magnetic) data as well as ground observatory data.
The evaluation shows promising results, reducing the error compared to the reference model significantly. Results of the characterized and calibrated magnetic datasets by machine learning techniques are evaluated by demonstrating enhanced representations of ionospheric and magnetospheric currents and lithospheric signatures. It shows that appropriate handling of multi-satellite magnetic data can increase the geomagnetic datasets significantly in parallel to only one high-precision mission.

Observations from the Swarm mission have made possible imaging of the magnetic field generated inside Earth’s core to unprecedented detail. These data, along with physics-based assumptions concerning the dynamics of the liquid iron within the core, permit us to construct new data-constrained models of the direction and speed of flow at the edge of the Earth’s core. Such maps of the flow lie at the heart of understanding how the Earth’s magnetic dynamo operates some 3000km beneath the surface.

The unique constellation configuration of Swarm’s polar orbiting satellites has provided new insights into magnetic field change at high latitude. Of particular interest is the behaviour of a cluster of localised patches of magnetic field at the core surface, located at around 70 degrees latitude, which serve as a powerful indicator of core dynamics. The movement and change in structure of these patches was recently explained by an accelerating jet (Livermore et al., 2017), inferred to be localised on the region of core surface at high-latitude stretching beneath Siberia to Canada. Such behaviour of the flow has not yet been identified elsewhere. We will present new images and models of the dynamics of this phenomenon which is apparently unique to the north polar region, based on the most recent data from Swarm and ground-based observatories. This work comprises one of the goals of the Swarm 4D-Earth Core ESA-funded consortium.


The identification of localised core flow acceleration at high latitude has profound implications for our understanding of the large-scale dynamics of the core, as this high-latitude region forms an important component of the core’s general circulation pattern which consists of an planetary-scale eccentric gyre (Pais & Jault, 2008). This gyre connects, on the one extreme, strong westward-directed flows at high latitude, with, on the other, inferred westward directed flows under the Atlantic on the equator. A better understanding of the global pattern of flow within Earth’s core requires not only continued space-based monitoring of high latitude magnetic field by Swarm, but also an improved understanding of equatorial core dynamics from the monitoring of low-latitude internal magnetic field that will be made possible by new geomagnetic missions with improved local time coverage, such as the proposed NanoMagSat mission.

Livermore, P. W., Hollerbach, R., & Finlay, C. C. (2017). An accelerating high-latitude jet in Earth’s core. Nature Geoscience, 10(1), 62–68.

Pais, M. A., & Jault, D. (2008). Quasi-geostrophic flows responsible for the secular variation of the Earth's magnetic field. Geophysical Journal International, 173(2), 421–443.

Detailed mapping of the Earth's magnetic field brings key constraints on the composition,
dynamics, and history of the crust. Satellite and near-surface measurements are complementary as they detect different length scales.
We present a magnetic field model built after a selection and processing of magnetic field measurements from the German CHAMP and ESA Swarm satellites, which we merge with a world compilation of near-surface scalar anomaly data. The global model is constructed from a series of independent regional models that are then transformed into a unique set of spherical harmonic (SH) Gauss coefficients. This procedure relies on parallelization and memory optimization and allows us to generate the first global model to SH degree 1050 derived by inversion of all available measurements from near surface to satellite altitudes.
The model agrees with previous satellite-based models at large wavelengths and fits the CHAMP and Swarm satellite data down to expected noise levels. Further assessment in the geographical and spectral domains shows that the model is stable when downward continued to the Earth's surface. However, we observe a possible reduction of the expected power spectrum between SH degrees 120 and 200 that arises from a low signal to noise ratio in the available satellite and near surface data. Numerical simulations demonstrate that the measurements of the magnetic field scalar and vector gradients at steadily decreasing altitudes during the Swarm A and C satellites re-entry will provide invaluable low altitude measurements for filling this gap. Additional simulations carried out in the framework of the NanoMagSat project, which aims to deploy a new constellation of three identical nanosatellites, using two inclined (approximately 60°) and one polar Low Earth Orbiting satellites, show that the Earth’s lithospheric field will also be better depicted in the spectral range.

The solar activity in the form of coronal mass ejections leads to abnormal geomagnetic field fluctuations. These fluctuations, in their turn, generate so-called geomagnetically induced currents (GIC) in electric power grids, which may pose a significant risk to the reliability and durability of such infrastructure. Hence, nowcasting and ultimately forecasting GIC is one of the grand challenges of modern space weather studies. One of the critical components of such now/forecasting is a real-time simulation of the ground electric field (GEF), which depends on the electrical conductivity distribution inside the Earth and the spatiotemporal structure of geomagnetic field fluctuations. In this contribution, we present a methodology that allows researchers to simulate the GEF in a real time, irrespective of the complexity of the Earth's conductivity and geomagnetic field fluctuations models. The methodology relies on a factorization of the source by spatial modes and time series of respective expansion coefficients and exploits precomputed frequency-domain GEF generated by corresponding spatial modes.

The validation of the presented concept is performed using Fennoscandia as a test region. The choice of Fennoscandia is motivated by several reasons. First, it is a high latitude region where the GEF is expected to be particularly large. Second, there exists a 3-D ground electrical conductivity model of the region. Third, the regional magnetometer network, IMAGE, allows us to build a realistic source model for a given geomagnetic disturbance using the Spherical Elementary Current Systems (SECS) method. Factorization of the SECS-recovered source is then performed using the principal component analysis. Finally, taking several geomagnetic storms as space weather events, we show that the real-time high-resolution 3-D modelling of the GEF at a given time instant on a 512x512 lateral grid is feasible and requires only a fraction of a second provided that frequency-domain GEF due to the preselected spatial modes are computed in advance.

We also discuss how one could implement the presented methodology for nowcasting and forecasting the GEF using ground-based magnetic field data, ACE/DSCOVR satellite observations of the solar wind parameters at the L1 Lagrangian point, and magnetic field data from the low-Earth-orbit active geomagnetic missions, like Swarm and CSES, and planned missions like Macao and NanoMagSat.

Heliophysics, the science of understanding the Sun and its interaction with the Earth and the solar system, has a large and active international community, with significant expertise and heritage in the European Space Agency and Europe. Several ESA directorates have activities directly connected with this topic, including ongoing and/ or planned missions and instrumentation, comprising a ESA Heliophysics observatory. A particularly strong example of this has been the cross-directorate collaboration between Swarm and Cluster. Discussions in this area began in the early stages of Swarm, shortly after selection and grew into funded activities examining synergies between the missions, including dedicated ISSI working groups and a forum. Both missions continue to operate with evolving scientific targets, which include specific cross- mission science, only possible through such collaboration. This paper will provide a brief overview of Cluster -Swarm activities, past present and future.