Knowing charged particle densities high above Earth is key for forecasting space weather effects on satellites and communications, but they are difficult to estimate at high latitudes because of auroras. We built an artificial intelligence model for northern Norway using radar observations, magnetic field measurements, geophysical indices and solar activity. It produces more accurate estimates than existing methods, even during auroral events, and can be adapted to other regions.

The EISCAT_3D radar is a new ionospheric radar under construction in the Fennoscandia region. The radar will make measurements of plasma characteristics at altitudes above approximately 60 km. The capability of the system to make these measurements at spatial scales of less than 100 m using multiple digitised signals from each of the radar antenna panels is highlighted. There are many ionospheric small-scale processes that will be further resolved using the techniques discussed here.

In situ observations are crucial for understanding interplanetary dust, yet not every spacecraft has a dedicated dust detector. Dust encounters happen at great speeds, leading to high energy density at impact, which leads to ionization and charge release, which is detected with electrical antennas.  Our work looks at how the transient charge plume interacts with Solar Orbiter spacecraft. Our findings are relevant for the design of future experiments and the understanding of present data.

We present a second-level calibration method for electron density measurements from multi-beam incoherent scatter radars. It is based on the well-known Flat field correction method used in imaging and photography. The methods improve data quality and useability as they account for unaccounted, and unpredictable variations in the radar system. This is valuable for studies where inter-beam calibration is important such as studies of polar cap patches, plasma irregularities and turbulence.

Collisional fragmentation of asteroids, comets and meteoroids is the main source of dust in the solar system. The dust distribution is however uncharted and the role of dust in the solar system is largely unknown. At present, the interplanetary medium is explored by the Solar Orbiter spacecraft. We present a novel method, based on artificial intelligence, that can be used for detecting dust impacts in Solar Orbiter observations with high accuracy, advancing the study of dust in the solar system.