There have been a number of theories proposed concerning the loss of relativistic electrons from the radiation belts. However, direct observations of loss were not possible on a number of previous missions due to the large field of view of the instruments and often high-altitude orbits of satellites that did not allow researchers to isolate the precipitating electrons from the stably trapped. We use measurements from the ELFIN-L suit of instruments flown on Lomonosov spacecraft at LEO orbit, which allows us to distinguish stably trapped from the drift loss cone electrons. The sun-synchronous orbit of Lomonosov allows us to quantify scattering that occurred into the loss cone on the dawn-side and the dusk-side magnetosphere. The loss at MeV energies is observed predominantly on the dawn-side, consistent with the loss induced by the chorus waves. The companion data publication provides processed measurements.

Low Earth Orbit (LEO) satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit (HEO) that can observe all equatorial pitch-angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a data assimilation approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites (POES) NOAA-15,-16,-17,-18,-19 and MetOp-02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites (GOES) observations into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES dataset and estimate the flux ratios at each time, location and energy. From these ratios we derive energy and $L^*$ dependent recalibration coefficients. To validate our results, we analyse on-orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data-assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of data assimilation allows for improved statistics, as the possible comparisons are considerably increased. Data-assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large datasets using short periods of data.

Space-based measurements of the Earth's magnetic field with a good spatiotemporal coverage are needed to understand the complex system of our surrounding geomagnetic field. High-precision magnetic field satellite missions form the backbone for sophisticated research, but they are limited in their coverage. Many satellites carry so-called platform magnetometers that are part of their attitude and orbit control systems. These can be re-calibrated by considering different behaviors of the satellite system, hence reducing their relatively high initial noise originating from their rough calibration. These platform magnetometer data obtained from non-dedicated satellite missions complement the high-precision data by additional coverage in space, time, and magnetic local times. In this work, we present an extension to our previous Machine Learning approach for the automatic in-situ calibration of platform magnetometers. We introduce a new physics-informed layer incorporating the Biot-Savart formula for dipoles that can efficiently correct artificial disturbances due to electric current-induced magnetic fields evoked by the satellite itself. We demonstrate how magnetic dipoles can be co-estimated in a neural network for the calibration of platform magnetometers and thus enhance the Machine Learning-based approach to follow known physical principles. Here we describe the derivation and assessment of re-calibrated datasets for two satellite missions, GOCE and GRACE-FO, which are made publicly available. We achieved a mean residual of about 7 nT and 4 nT for low- and mid-latitudes, respectively.

The Gravity field and steady-state ocean circulation explorer (GOCE) is part of ESA’s Earth Explorer Program. The satellite carries magnetometers that control the activity of magnetorquers for navigation of the satellite but are not dedicated as science instruments. However, intrinsic steady states of the instruments can be corrected by alignment and calibration, and artificial perturbations, e.g., from currents, can be removed by their characterisation correlated to housekeeping data. The leftover field then shows the natural evolution and variability of the Earth’s magnetic field. This article describes the pre-processing of input data as well as calibration and characterisation steps performed on GOCE magnetic data, using a high precision magnetic field model as reference. For geomagnetic quiet times, the standard deviation of the residual is below 13 nT with a median residual of (11.7, 9.6, 10.4) nT for the three magnetic field components (x,y,z). For validation of the calibration and characterisation performance, we selected a geomagnetic storm event in March 2013. GOCE magnetic field data shows good agreement with results from a ground magnetic observation network. The GOCE mission overlaps with the dedicated magnetic field satellite mission CHAMP for a short time at the beginning of 2010, but does not overlap with the Swarm mission or any other mission flying at low altitude and carrying high-precision magnetometers. We expect calibrated GOCE magnetic field data to be useful for lithospheric modelling and filling the gap between the dedicated geomagnetic missions CHAMP and Swarm.