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GOCE ML-calibrated magnetic field data

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.

GRACE-FO calibrated and characterized magnetometer data

GRACE-FO carries a magnetometer as part of its attitude orbit control system (AOCS). The magnetometer does not belong to the scientific payload of the mission. However, after postprocessing of the data, information on the geomagnetic field and on electric currents in near Earth space are derived. Each GRACE-FO satellite (GF1 and GF2) carries two fluxgate magnetometers (FGM), an active one, FGM A, and a redundant one, FGM B. So far, the redundant magnetometers were not switched and are not included in the data set. The provided data consists of raw magnetic field data as provided by L1b (RAW), Magnetic field data aligned, calibrated and corrected (ACAL_CORR), CHAOS7 magnetic model predictions for core, crustal and large-scale magnetospheric field (CHAOS7), Magnetic coordinates (APEX) and Radial and field-aligned currents derived from magnetic data in ACAL_CORR (FAC). The data are provided in NASA CDF format (https://cdf.gsfc.nasa.gov/). Data categories RAW: Raw magnetic field data as provided by L1b ACAL_CORR: Magnetic field data, aligned, calibrated and corrected CHAOS7: CHAOS7 magnetic model predictions for core, crustal and large-scale magnetospheric field APEX: Magnetic coordinates (Emmert et al, 2010) FAC: Radial and field-aligned currents derived from magnetic data in ACAL_CORR

Model files for the Neural network-based model of Electron density in the Topside ionosphere (NET)

Here, we present model files and example scripts for the Neural network-based model of Electron density in the Topside ionosphere (NET). The model is based on radio occultation data from Gravity Recovery And Climate Experiment (GRACE), Challenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC-1) missions from 2001 until 2019. The NET model is based on alpha-Chapman functions with a linear decay of scale height with altitude, and consists of 4 sub-models (2 parameters of the F2-peak and 2 parameters of the linear scale height decay). The model uses geographic and magnetic latitude and longitude, magnetic local time, day of year, altitude, solar flux index P10.7, geomagnetic activity index Kp, storm-time SYM-H index as inputs. An example data frame to run the model is provided, as well as the Jupyter notebook to perform an example run.

An empirical model of electron pitch angle distributions in the outer radiation belt based on Van Allen Probes data

Here, we present an empirical model of the equatorial electron pitch angle distributions, based on the Magnetic Electron Ion Spectrometer (MagEIS) instrument aboard the Van Allen Probes. The model was created for energies from 37 keV up to 2.65 MeV. The model uses the solar wind dynamic pressure as a driving parameter and has a continuous dependence on Lm, magnetic local time and activity. It works for L-shells from 3.05 up around 5.95. For each channel of the MagEIS instrument, there are two files with model coefficients, one for Pdyn <5.5-6 nPa (e.g., “Pijk_246_keV.dat’) , and the second one for very high dynamic pressure values above 5.5 nPa (e.g., “Pijk_246_keV_HIGH.dat’). The script to read both file types is provided (“read_coefs.py”), and the data format is explained in the readme file.

TIE-GCM simulation data for the equatorial F region vertical plasma drift

This dataset comprises numerical outputs from the thermosphere-ionosphere-electrodynamics general circulation model (TIE-GCM) simulations described in the article "Modeling of planetary wave influences on the pre-reversal enhancement of the equatorial F region vertical plasma drift" (Yamazaki & Diéval, 2021).

Upper thermosphere neutral wind cross-track component deduced from CHAMP accelerometer data

This dataset comprises global upper thermospheric cross-track neutral wind measurements obtained from accelerometer data of the CHAMP satellite during its almost ten year’s lifetime from 2001 to 2009. One key scientific instrument on-board CHAMP was a sensitive triaxial accelerometer. It was located at the spacecraft's centre of mass and sampled effectively accelerations due to non-gravitational forces with an accuracy of ~3×10^-9 ms^-2 (Doornbos et al., 2010). The along-track air drag measurements resulted in thermospheric mass density estimations, while the instrument was sensitive enough to deduce also the horizontal neutral wind component from the cross-track accelerations.The CHAllenging Minisatellite Payload (CHAMP) spacecraft circled the Earth from July 2000 to September 2010 on a near-polar orbit (inclination 87.3°). Each orbit period took about 93 minutes at an altitude of initially 455 km, and decaying to about 320 km in 2009. Due to CHAMP's precession, the satellite achieved full coverage of all local times within about 131 days in each case.This work was part of a study in 2007-2009 (Doornbos et al., 2009) funded by the European Space Agency’s General Studies Program which aimed at a more precise estimation of the non-gravitational forces, considering the precise satellite geometry and its optical and mechanical surface properties. To obtain the actual air drag forces, the modelled accelerations due to radiation pressure forces from the sun, the Earth's albedo, and the Earth's infrared radiation had to be computed and removed from the calibrated and edited accelerometer data to get the observed aerodynamic acceleration vector. The modelling of the radiation pressure forces comprised several nontrivial components like the modelling of eclipse and semi-shadow conditions for solar radiation pressure, values for the reflectivity and infrared emissivity of Earth surface elements, and models of the geometry and optical properties of the satellite surfaces (Doornbos et al., 2010).The detailed description of supersonic flow of the neutral gas particles across the satellite's surface and its reflection requires a model of the gas–surface interaction, which specifies the angular distribution and energy flux of the reflected particles. One has to make assumptions and educated guesses, because information on the gas–surface interaction, as well as in situ observations of aerodynamic model parameters like air temperature and neutral gas species' concentrations should be measured by independent instruments on the accelerometer-carrying satellite.Here, we relied on the empirical atmosphere model NRLMSISE-00 (Picone et al., 2002) and the rarefied aerodynamic equations for flat panels, derived by Sentman (1961). These equations take into account the random thermal motion of the incident particles and assume a completely diffuse distribution of the reflected particle flux. The energy flux accommodation coefficient alpha (Moe et al., 2004), which determines whether the particles retain their mean kinetic energy (alpha = 0) or acquire the temperature of the spacecraft surface wall (alpha = 1), was found to be optimally chosen with alpha = 0.8 for this data set.This thermospheric cross-track neutral wind data set consists of a series of annual CDF data files for both CHAMP wind measurements (subfolder: CH_PN_R03_denswind_iter2_Sentman_alpha08) and CHAMP orbital data (subfolder: CH_orbit_GEO_RSO). The CDF data files are documented in the header. The complete dataset contains more than 25 million data points with a temporal cadence of 10 sec.In addition to the data, we are providing supplementary Figures to Aruliah et al. (2019, subfolder: 2019-001_Foerster-Doornbos_Figures). They are complementary, in particular, to Figs. 1-4 of this paper, but additionally show the original data as “cloud” of data points in the background of the statistical averages. Each figure plot (png-format) has an accompanying txt-file of the same name (except the extension) with ASCII tables of the hourly statistical averages and their standard deviations.The data were used in various previous publications mainly with respect to high-latitude upper thermosphere studies (Förster et al., 2008, 2011) and investigations of the interhemispheric coupling processes of the magnetosphere, ionosphere, and thermosphere (Förster et al., 2017). Actually, this data publication serves as supplement to Aruliah et al. (2019).

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