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  • GFZ Data Services  (4)
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Years
  • 1
    Publication Date: 2021-09-30
    Description: Abstract
    Description: Global spherical harmonic paleomagnetic field model LSMOD.2 describes the magnetic field evolution from 50 to 30 ka BP based on published paleomagnetic sediment records and volcanic data. It is an update of LSMOD.1, with the only difference being a correction to the geographic locations of one of the underlying datasets. The time interval includes the Laschamp (~41 ka BP) and Mono Lake (~34 ka BP) excursions. The model is given with Fortran source code to obtain spherical harmonic magnetic field coefficients for individual epochs and to obtain time series of magnetic declination, inclination and field intensity from 49.95 to 30 ka BP for any location on Earth. For details see M. Korte, M. Brown, S. Panovska and I. Wardinski (2019): Robust characteristics of the Laschamp and Mono lake geomagnetic excursions: results from global field models. Submitted to Frontiers in Earth Sciences
    Description: Methods
    Description: File overview:LSMOD.2 -- ASCII file containing the time-dependent model by a list of spline basis knot points and spherical harmonic coefficients for these knot points.LSfield.f -- Fortran source code to obtain time series predictions of declination, inclination and intensity from the model file.LScoefs.f -- Fortran source code to obtain the spherical harmonic coefficients for an individual age from the time-dependent model file.The data are licenced under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0) and the Fortran Codes under the Apache License, Version 2.0.The Fortran source code should work with any standard Fortran 77 or higher compiler. Each of the two program files can be compiled separately, all required subroutines are included in the files. The model file, LSMOD.1 or LSMOD.2, is read in by the executable program and has to be in the same directory. The programs work with interactive input, which will be requested when running the program.
    Keywords: paleomagnetic field model ; geomagnetic excursion ; spherical harmonic paleomagnetic field model ; EPOS ; Multi-scale laboratories ; paleomagnetic and magnetic data ; software tools ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMAGNETISM 〉 MAGNETIC FIELD 〉 MAGNETIC INTENSITY ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMAGNETISM 〉 MAGNETIC FIELD 〉 MAGNETIC INCLINATION ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMAGNETISM 〉 MAGNETIC FIELD 〉 MAGNETIC DECLINATION ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMAGNETISM 〉 REFERENCE FIELDS ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMAGNETISM 〉 PALEOMAGNETISM
    Type: Model
    Format: 1 Files
    Format: application/octet-stream
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2022-04-28
    Description: Abstract
    Description: 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.
    Keywords: Platform Magnetometers ; Satellite-based magnetometers ; Earth's magnetic field ; Geomagnetism ; Earth Observation Satellites 〉 Earth Explorers 〉 GOCE ; Earth Remote Sensing Instruments 〉 Passive Remote Sensing 〉 Magnetic Field/Electric Field Instruments 〉 MAGNETOMETERS ; Earth Remote Sensing Instruments 〉 Passive Remote Sensing 〉 Magnetic Field/Electric Field Instruments 〉 MTQ ; Solar/Space Observing Instruments 〉 Magnetic Field/Electric Field Instruments 〉 FLUXGATE MAGNETOMETERS
    Type: Dataset , Dataset
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  • 3
    Publication Date: 2024-01-08
    Description: Abstract
    Description: The Gravity Recovery and Climate Experiment-Follow-On (GRACE-FO) satellite mission, consisting of two satellites, each carry a magnetometer as part of its attitude orbit control system (AOCS). After careful calibration, the data acquired through them 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 et al. (2021, https://doi.org/10.5880/GFZ.2.3.2021.002) and uses a similar format. The platform magnetometer data has been calibrated against CHAOS-7 magnetic field model predictions for core, crustal and large-scale magnetospheric field (Finlay et al., 2020, https://doi.org/10.1186/s40623-020-01252-9) 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 information, 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. Additionally, in the folder ‚fac’, field-aligned current derived from the magnetic field data are provided. There exists a similar dataset with calibrated magnetic data from the GOCE satellite mission under https://doi.org/10.5880/GFZ.2.3.2022.002 (Styp-Rekowski et al., 2022).
    Keywords: Earth Observation Satellites 〉 NASA Earth System Science Pathfinder 〉 GRACE-FO ; Platform Magnetometers ; Satellite-based magnetometers ; Earth's magnetic field ; Geomagnetism ; Earth Remote Sensing Instruments 〉 Passive Remote Sensing 〉 Magnetic Field/Electric Field Instruments 〉 MAGNETOMETERS ; Earth Remote Sensing Instruments 〉 Passive Remote Sensing 〉 Magnetic Field/Electric Field Instruments 〉 MTQ ; EARTH SCIENCE SERVICES 〉 ENVIRONMENTAL ADVISORIES 〉 GEOLOGICAL ADVISORIES 〉 GEOMAGNETISM ; Solar/Space Observing Instruments 〉 Magnetic Field/Electric Field Instruments 〉 FLUXGATE MAGNETOMETERS
    Type: Dataset , Dataset
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  • 4
    Publication Date: 2024-01-08
    Description: Abstract
    Description: 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.
    Description: Methods
    Description: 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.
    Keywords: 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
    Type: Dataset , Dataset
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