2026-05-01T12:09:22Zhttp://doidb.wdc-terra.org/oaip/oai
oai:doidb.wdc-terra.org:73592024-01-08T07:37:09ZDOIDBDOIDB.GFZ
false4DOIDB.GFZ 10.5880/GFZ.2.3.2022.002 Styp-Rekowski, Kevin Kevin Styp-Rekowski 0000-0001-8267-521X TU Berlin, Berlin, Germany GFZ German Research Centre for Geosciences, Potsdam. Germany Michaelis, Ingo Ingo Michaelis 0000-0001-9741-4063 GFZ German Research Centre for Geosciences, Potsdam. Germany Stolle, Claudia Claudia Stolle 0000-0002-5717-1623 IAP - Leibniz Institute of Atmospheric Physics at the University of Rostock, Kühlungsborn, Germany Baerenzung, Julien Julien Baerenzung 0000-0002-0607-3695 GFZ German Research Centre for Geosciences, Potsdam. Germany Korte, Monika Monika Korte 0000-0003-2970-9075 GFZ German Research Centre for Geosciences, Potsdam. Germany Kao, Odej Odej Kao 0000-0001-6454-6799 TU Berlin, Berlin, Germany GOCE ML-calibrated magnetic field data GFZ Data Services 2022 GOCE satellite machine learning platform magnetometers calibration Earth Observation Satellites > Earth Explorers > GOCE Earth Remote Sensing Instruments > Passive Remote Sensing > Magnetic Field/Electric Field Instruments > MAGNETOMETERS EARTH SCIENCE > SOLID EARTH > GEOMAGNETISM > MAGNETIC FIELD EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS Styp-Rekowski, Kevin Kevin Styp-Rekowski 0000-0001-8267-521X TU Berlin, Berlin, Germany GFZ German Research Centre for Geosciences, Potsdam. Germany Styp-Rekowski, Kevin Kevin Styp-Rekowski 0000-0001-8267-521X TU Berlin, Berlin, Germany GFZ German Research Centre for Geosciences, Potsdam. Germany Styp-Rekowski, Kevin Kevin Styp-Rekowski 0000-0001-8267-521X TU Berlin, Berlin, Germany GFZ German Research Centre for Geosciences, Potsdam. Germany Styp-Rekowski, Kevin Kevin Styp-Rekowski 0000-0001-8267-521X TU Berlin, Berlin, Germany GFZ German Research Centre for Geosciences, Potsdam. Germany Michaelis, Ingo Ingo Michaelis 0000-0001-9741-4063 GFZ German Research Centre for Geosciences, Potsdam. Germany Michaelis, Ingo Ingo Michaelis 0000-0001-9741-4063 GFZ German Research Centre for Geosciences, Potsdam. Germany Michaelis, Ingo Ingo Michaelis 0000-0001-9741-4063 GFZ German Research Centre for Geosciences, Potsdam. Germany Stolle, Claudia Claudia Stolle 0000-0002-5717-1623 IAP - Leibniz Institute of Atmospheric Physics at the University of Rostock, Kühlungsborn, Germany Baerenzung, Julien Julien Baerenzung 0000-0002-0607-3695 GFZ German Research Centre for Geosciences, Potsdam. Germany Korte, Monika Monika Korte 0000-0003-2970-9075 GFZ German Research Centre for Geosciences, Potsdam. Germany Korte, Monika Monika Korte 0000-0003-2970-9075 GFZ German Research Centre for Geosciences, Potsdam. Germany Kao, Odej Odej Kao 0000-0001-6454-6799 TU Berlin, Berlin, Germany Styp-Rekowski TU Berlin, GFZ Potsdam 2022-04-10 2009-11-01/2013-09-30 Dataset 10.21203/rs.3.rs-1607576/v1 10.1186/s40623-020-01252-9 10.5880/GFZ.2.3.2022.001 0204 CC BY 4.0 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.
The data was recorded onboard the GOCE satellite mission with varying time intervals of the differ-ent subsystems measuring. The magnetometer measurements (16s intervals) were aligned to match the closest position measurement (1s intervals) and interpolated accordingly. All other avail-able data of different intervals was interpolated and aligned to the same timestamps.

The data was calibrated using a Machine Learning approach involving Neural Networks, the whole method of calibration is described precisely in the referenced publication. The data was mainly processed for its calibration which yields a lower residual compared to a refer-ence model than the uncalibrated data, more details about the many steps involved can be found in the referenced publication.

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