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  • 1
    Publication Date: 2022-10-14
    Description: Abstract
    Description: This dataset is the MLT-averaged plasmapause position calculated for the NSF GEM Challenge Events. We use the recently developed Plasma density in the Inner magnetosphere Neural network-based Empirical (PINE) model [Zhelavskaya et al., 2017]. The PINE density model was developed using neural networks and was trained on the electron density data set from the Van Allen Probes Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) [Kletzing et al., 2013]. The model reconstructs the plasmasphere dynamics well (with a cross-correlation of ~0.95 on the test set), and its global reconstructions of plasma density are in good agreement with the IMAGE EUV images of the global distribution of He+. We compare the electron number density value given by the PINE model with the density threshold separating plasmaspheric-like and trough-like density given by [Sheeley et al., 2001] and get the plasmapause position in each MLT. Then, we calculate the MLT-averaged plasmapause position. The. time resolution is 1 hour. These data files presenting the Magnetic Local Time (MLT)-averaged plasmapause position used in the simulations in Wang et al [2020]. The data are presented as the following three tabular ASCII files (.dat) : Lpp_PINE_Sheely_Mean_Mar15_Mar20.dat: content, column1 time [day], column 2 L [Re (Earth Radii)] Lpp_PINE_Sheely_Mean_May30_Jun02.dat: content, column1 time [day], column 2 L [Re (Earth Radii)] Lpp_PINE_Sheely_Mean_Sep17_Sep26.dat: content, column1 time [day], column 2 L [Re (Earth Radii)]
    Keywords: Plasmasphere ; Plasmapause ; EARTH SCIENCE 〉 SUN-EARTH INTERACTIONS 〉 IONOSPHERE/MAGNETOSPHERE DYNAMICS 〉 PLASMA WAVES ; EARTH SCIENCE SERVICES 〉 MODELS 〉 SOLAR-ATMOSPHERE/SPACE-WEATHER MODELS
    Type: Dataset , Dataset
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  • 2
    Publication Date: 2023-01-20
    Description: Abstract
    Description: 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.
    Keywords: ionosphere ; machine learning ; empirical model ; neural network ; EARTH SCIENCE 〉 SUN-EARTH INTERACTIONS 〉 IONOSPHERE/MAGNETOSPHERE DYNAMICS ; EARTH SCIENCE SERVICES 〉 MODELS 〉 SOLAR-ATMOSPHERE/SPACE-WEATHER MODELS
    Type: Model , Model
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