The GOCE satellite carries three magnetometers as part of its drag-free attitude orbit control system (DFACS). The magnetometers do not belong to the scientific payload of the mission. After postprocessing of the data, information on the geomagnetic field and on electric currents in near Earth space are derived. The GOCE fluxgate magnetometer data (MAG) have been combined into to a single time series. The provided data consists of raw magnetic field data as provided by Level 1b (RAW), magnetic field data aligned, calibrated and corrected (ACAL_CORR), CHAOS7 magnetic model predictions for core, crustal and large-scale magnetospheric field (CHAOS7, Finlay et al., 2020), housekeeping information, e.g. magnetorquer, solar array and battery currents (HK), Magnetic coordinates (APEX) and radial and field-aligned currents derived from magnetic data (FAC). The calibration and characterization follows the approach given in the references for GOCE calibration.
The data are provided in NASA cdf format (https://cdf.gsfc.nasa.gov/) and accessible at: ftp://isdcftp.gfz-potsdam.de/platmag/MAGNETIC_FIELD/GOCE/Analytical/v0205/ and further described in a README.
The Gravity field and steady-state ocean circulation explorer (GOCE) satellite mission carries three platform magnetometers. After careful calibration, the data acquired through these can be used for scientific purposes by removing artificial disturbances from other satellite payload systems. This dataset is based on the dataset provided by Michaelis and Korte (2022) and uses a similar format. The platform magnetometer data has been calibrated against CHAOS7 magnetic field model predic-tions for core, crustal and large-scale magnetospheric field (Finlay et al., 2020) and is provided in the ‘chaos’ folder. The calibration results using a Machine Learning approach are provided in the ‘calcorr’ folder. Michaelis’ dataset can be used as an extension to this dataset for additional infor-mation, as they are connected using the same timestamps to match and relate the same data points. The exact approach based on Machine Learning is described in the referenced publication.
The data is provided in NASA CDF format (https://cdf.gsfc.nasa.gov/) and accessible at: ftp://isdcftp.gfz-potsdam.de/platmag/MAGNETIC_FIELD/GOCE/ML/v0204/ and further de-scribed in a README.