We present a new statistical analysis of small-scale (sub-decameter) plasma density irregularities in the topside ionosphere (325-1500 km altitude), using the high-cadence (1000 samples/sec) plasma current data from the imaging and rapid-scanning ion mass spectrometer (IRM) onboard Swarm Echo (Swarm-E) in the first seven years of the mission (2014-2020). IRM is one of the instruments in the Enhanced Polar Outflow Probe (e-POP) scientific payload on Swarm-E, and it measures low-energy ions (0.1-90 eV/q; 1-60 AMU/q) in the vertical plane of the spacecraft velocity (the vertical-ram plane) and resolves the mass-per-charge (M/q), energy-per-charge (E/q) and incident direction of each detector ion using time-of-flight (TOF) and hemispherical electrostatic analysis. In addition, it measures simultaneously the incident plasma (ion and electron) current on the sensor surface. The measurements produce a two-dimensional velocity phase space distribution for each major ion species and an overall ion composition distribution every 16 msec, and the total net plasma current every 1 msec. Using the latter, we present the statistical distributions of small-scale plasma density irregularities and their spectral characteristics down to sub-100 m scale, as well as their altitude, magnetic latitude, and magnetic local time dependences and their variations with solar and geomagnetic activities.

The TOLEOS project will provide new thermosphere density and crosswind observations derived from the accelerometer data of the CHAMP, GRACE, and GRACE-FO missions. The accurate calibration of the accelerometer data and the upgrade of the radiation pressure model are key elements of the project, which is funded by the Swarm Data, Innovation, and Science Cluster (Swarm DISC). To improve the radiation pressure modelling, we use ray tracing techniques in combination with high-fidelity geometry models of the satellites, which were augmented with the thermo-optical properties of the surfaces. This substantially reduces the uncertainty stemming from the satellite geometry modelling and shadowing effects. In addition, we introduce thermal models of the satellites to account for the radiation of heat from the satellites themselves. We will elaborate the accelerometer data calibration and briefly explain the upgraded radiation pressure modelling. Further, we will compare the new thermosphere density and crosswind observations to the existing observations to highlight the differences and demonstrate the effects of the upgraded processing.

Thermospheric neutral winds play an important role in the transport of momentum and energy in the upper atmosphere and affect the composition, dynamics and morphology of the ionospheric plasma. Although the general morphology of the winds is well understood, we are only starting to understand it’s variability. During the last decade it has become inherently clear that the lower atmosphere is an important driver of thermospheric variability, which can, for example, be due to direct penetration of waves from the lower atmosphere into the ionosphere/thermosphere, secondary waves generated on the way, or internal feedback mechanisms in the coupled ionosphere-thermosphere system. Therefore, an understanding of thermospheric variability and its causes is critical for an improved understanding of the coupled ionosphere-thermosphere system and the lower atmosphere. The Gravity Field and Steady-State Ocean Explorer (GOCE) mission provided cross-track (zonal) neutral winds at an altitude of around 260 km from November 2009 to October 2013. Due to the very small orbital precession of the satellite’s orbit in local time (LT), GOCE produced a large data set of zonal winds in the dawn-dusk sectors without the LT−season ambiguity intrinsic to many satellites. We have used GOCE zonal wind observations from low- to mid-latitudes obtained during geomagnetically quiet times to investigate the inter-annual, seasonal and spatial zonal wind variability near dawn and dusk. The temporal and spatial variability is presented as a variation about the zonal mean values and decomposed into its underlying wavenumbers using a Fourier analysis. The obtained wave features are compared between different years and different seasons. It is found that a significant part of the observed variability can be explained as due to waves up to wavenumber 5 and a clear interannual progression of the individual wave components can be observed. The obtained wave features will be compared and contrasted with model results to elucidate their underlying tidal components.

The Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer (EE) European Space Agency (ESA) mission launched on 2 November 2009 in excellent operational status with plans to continue its operational phase beyond 2022. The SMOS mission originally designed to perform global observations of soil moisture over land and salinity over oceans has however gone beyond its original scientific objectives demonstrating its suitability for new real time application purposes such as sea wind speed estimation for hurricane tracking or measuring thin sea-ice thickness in the polar seas.

The payload of SMOS consists of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument, a passive microwave 2-D interferometric full polarization radiometer, operating at 1.413 GHz (wavelength of 21 cm). Because of the SMOS Sun-synchronous orbit geometry and the size of the MIRAS’s antennae, the Sun appears in the field of view and a direct Solar radio observation is captured in the image and removed as a source of interference during the data ground processing.

Within this section, we want to describe our work to further process the removed Sun signal in order to derive a calibrated Solar flux measurements from such interference. The processor is applied to the latest version of the SMOS dataset (baseline V7, available since May 2021) and it is suitable for a near real time delivery of such L-band Solar flux information.

We also present the result of our inter-comparison between the derived SMOS solar flux and the radio-telescope measurements form US Air Force Radio Solar Telescope Network (RSTN) and from intercalibrated Nobeyama Radio Polarimeter of the National Astronomical Observatory of Japan for the entire solar cycle 24.

The validation results for the SMOS solar flux show a very good correlation with the on-ground measurements and the capability of the SMOS measurements to follow the increase of the mean solar activity for the solar cycle 24 during a period of transition from quiet to active Sun as well as to capture the impact of Solar rotation in the microwave flux.
Based on this validation result, we discuss how the SMOS mission can provide a valuable contribution in collecting and delivering in near real time, solar flux measurements for space weather application and navigation application in a frequency band between 1-2GHz which is used by Global Navigation Satellite Systems (GNSS), flight radars and wireless communications, among others. All these services are known to be affected by intense Solar activities, timely information on L-band solar radio bursts (SRBs) can contribute to a better modelling or monitoring of anomalies in the aforementioned frequency range.

As sufficiently recognised in the literature, the quality of SAR measurements may be affected
by the propagation of the radar signals through the ionosphere. The introduced propagation delays and the dispersive nature of the ionosphere may cause strong geolocation errors, defocussing in
range and azimuth in the radar images, as well as the local rotation of the polarisation reference of
fully-polarimetric acquisitions. The impact of the ionosphere is more critical for lower frequencies
and higher bandwidths.

These effects have been assessed for ESA’s Earth Explorer Biomass mission [1], which will be
the first P-band SAR in space and is expected to be launched in 2023. The baseline approach for
the estimation of ionospheric perturbations in Biomass consists of exploiting the Faraday rotation
estimates provided by the Bickel and Bates algorithm [2]. This approach necessitates very accurate
Faraday rotation estimates (e.g., typically better than one tenth of a degree) if they are to be used
for correcting the phase history of the data and not the depolarisation alone. Such high accuracies
typically require high averaging and low-pass estimates which might be incompatible with strong
scintillations scenarios.

As part of the Ground Processor Prototype (GPP) of the mission [3], we are developing an
autofocus algorithm for the recovery of ionospheric phase signatures which can handle such strong
scintillation cases. To support this development, we have enhanced the Biomass end-to-end performance simulator (BEEPS) [4] with tailored ionospheric and scene generators. The scene generator
of BEEPS is extended to use real spaceborne SAR reflectivity images (e.g., Sentinel-1) which provide
similar coverage and realistic contrast, essential for the tuning of the autofocus. For the ionospheric
generation, BEEPS is able to create thin-layer realizations including background and turbulent contributions. The incorporation of the background part (based on the NeQuick2 [5]) in the development
environment is essential for the characterization of integration errors in azimuth. The turbulent part
is based on the well-known Rino’s power law [6]. The superposition of the background and turbulent
components is incorporated in the simulated data as locally-variant phase and delay perturbations,
as well as Faraday rotation.

The classical references on autofocus are typically targeted on the recovery of the contrast of
the image, only minorly worrying about the fidelity of the phase of the images after the correction
[7]. A phase gradient autofocus approach for Biomass was suggested in [8] to mitigate the effect of
ionospheric irregularities along the synthetic aperture. This approach has the limitation of requiring
the presence of point-like targets within the image, which makes it a difficult choice for operational
environments. We propose in this paper a combined approach based on a map-drift kernel [7] and
therefore capable of delivering robust phase error estimates over extended areas in the absence of
point-like targets, while at the same time integrating the information of any point-like or coherent
scatterer present in the image [9] with the purpose of locally improving the estimation accuracy. Due
to the similarity of the phase perturbations for all polarimetric channels, the suggested algorithm
integrates the autofocus estimates of all four polarimetric channels into a single inversion step,
which can be also supported by the residual Faraday rotation estimates as postulated in [10]. An
assessment of the usefulness of estimates of the dispersion in the integration step of the autofocus
will be provided in the final version of the paper. In the paper we will also show how the algorithm
uses the residual errors introduced after each iteration of the algorithm to optimally generate the
ionospheric phase error estimates.

References
[1] Rogers, Neil C., et al., "Impacts of ionospheric scintillation on the BIOMASS P-band satellite
SAR." IEEE Transactions on Geoscience and Remote Sensing 52.3 (2013): 1856-1868.
[2] Kim, Jun Su, et al., "Correcting distortion of polarimetric SAR data induced by ionospheric
scintillation." IEEE Transactions on Geoscience and Remote Sensing 53.12 (2015): 6319-6335.
[3] Prats-Iraola, Pau, et al., "The BIOMASS ground processor prototype: An overview." EUSAR
2018; 12th European Conference on Synthetic Aperture Radar. VDE, 2018.
[4] Sanjuan-Ferrer, Maria Jose, et al., "End-to-end performance simulator for the BIOMASS mission." EUSAR 2018; 12th European Conference on Synthetic Aperture Radar. VDE, 2018.
[5] Nava, B., P. Coisson, and S. M. Radicella. "A new version of the NeQuick ionosphere electron
density model." Journal of Atmospheric and Solar-Terrestrial Physics 70.15 (2008): 1856-1862.
[6] Rino, C. L. "A power law phase screen model for ionospheric scintillation: 1. Weak scatter."
Radio Science 14.6 (1979): 1135-1145.
[7] Carrara, Walter G., R. S. Goodman, and Rd M. Majewski. "Spotlight synthetic radar: signal
processing algorithms." Artech House (1995).
[8] Li, Zhuo, et al., "Performance analysis of phase gradient autofocus for compensating ionospheric
phase scintillation in BIOMASS P-band SAR data." IEEE Geoscience and Remote Sensing
Letters 12.6 (2015): 1367-1371.
[9] Dexin Li. "Research on the technology of sinal processing and simulation of geosynchronous
SAR". PhD Thesis.
[10] Gracheva, Valeria, et al., "Combined Estimation of Ionospheric Effects in SAR Images Exploiting Faraday Rotation and Autofocus." IEEE Geoscience and Remote Sensing Letters (2021)

Ionized plasma in the high-latitude ionosphere-thermosphere is at constant motion. Plasma flow in the ionospheric F region is driven by magnetosphere-ionosphere coupling and the original energy source stems from the coupling between the solar wind and the magnetosphere. Typically, two convection cells are formed in the high-latitude ionosphere, one centered on the dusk and one on the dawn side. Several statistical models exist, which estimate the flow velocities based on solar wind properties and geophysical conditions.

However, at times very large plasma flow velocities have been measured, which exceed the statistical average velocity values several times, up to tenfold. The horizontal spatial extent of these strong flows is expected to be relatively narrow, and at the moment their duration is unclear. To study those events, we utilize the following measurements. Swarm is ESA's Earth Explorer 5 mission and a constellation of three satellites in circular polar orbits at 450-515 km altitudes. Swarm makes highly accurate measurements of Earth's magnetic field since November 2013 and carries several other instruments as well, which provide us with information about the local plasma environment and the field-aligned electrical currents flowing into/away from the ionosphere. The EISCAT incoherent scatter radars, the UHF and VHF radars in Tromsø (69.6oN latitude) and the EISCAT Svalbard radar (ESR, 79oN latitude) near Longyearbyen, have been operational for several decades. EISCAT radars yield electron density, electron and ion temperature and ion drift measurements from a large altitude range in the ionosphere.

In this study, we carry out a search of conjugate Swarm satellite Thermal Ion Imager (TII) measurements over the EISCAT Tromsø and Svalbard radars to study the characteristics of high-speed plasma flows and to confirm their existence by two independent measurements. High plasma speeds produce increased ion-neutral frictional heating, which can be seen as an increase of ion temperature, measured by the EISCAT radar. Increased ion temperature affects the chemistry of the ionosphere, and the flow channels play a role in the electrodynamical magnetosphere-ionosphere coupling. By combining satellite and ground-based observations, we will be able to get information about the physical processes, spatial and temporal scales, as well as geomagnetic conditions of frictional heating events. These events are interesting also from the space weather perspective, because heating of the atmosphere may produce increased satellite drag at low-Earth orbits (LEO).