2026-06-10T05:08:48Zhttp://doidb.wdc-terra.org/oaip/oai

oai:doidb.wdc-terra.org:76672023-01-20T10:12:38ZDOIDBDOIDB.GFZ

false4DOIDB.GFZ 10.5880/GFZ.2.7.2023.001 Smirnov, Artem Artem Smirnov 0000-0003-3689-4336 GFZ German Research Centre for Geosciences, Potsdam, Germany Shprits, Yuri Yuri Shprits 0000-0002-9625-0834 GFZ German Research Centre for Geosciences, Potsdam, Germany Prol, Fabricio Fabricio Prol 0000-0002-7206-1705 National Land Survey of Finland (NLS), Kirkkonummi, Finland Lühr, Hermann Hermann Lühr 0000-0002-1599-6758 GFZ German Research Centre for Geosciences, Potsdam, Germany Berrendorf, Max Max Berrendorf 0000-0001-9724-4009 Ludwig-Maximilians-University of Munich, Munich, Germany Zhelavskaya, Irina Irina Zhelavskaya 0000-0002-7029-5372 GFZ German Research Centre for Geosciences, Potsdam, Germany Xiong, Chao Chao Xiong 0000-0002-7518-9368 Wuhan University, Wuhan, China GFZ Data Services 2023 ionosphere machine learning empirical model neural network EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS EARTH SCIENCE SERVICES > MODELS > SOLAR-ATMOSPHERE/SPACE-WEATHER MODELS Smirnov, Artem Artem Smirnov 0000-0003-3689-4336 GFZ German Research Centre for Geosciences, Potsdam, Germany Shprits, Yuri Yuri Shprits 0000-0002-9625-0834 GFZ German Research Centre for Geosciences, Potsdam, Germany Prol, Fabricio Fabricio Prol 0000-0002-7206-1705 National Land Survey of Finland (NLS), Kirkkonummi, Finland Lühr, Hermann Hermann Lühr 0000-0002-1599-6758 GFZ German Research Centre for Geosciences, Potsdam, Germany Berrendorf, Max Max Berrendorf 0000-0001-9724-4009 Ludwig-Maximilians-University of Munich, Munich, Germany Zhelavskaya, Irina Irina Zhelavskaya 0000-0002-7029-5372 GFZ German Research Centre for Geosciences, Potsdam, Germany Xiong, Chao Chao Xiong 0000-0002-7518-9368 Wuhan University, Wuhan, China Smirnov, Artem Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Potsdam, Germany 2023-01-04 Model 10.1038/s41598-023-28034-z 1.0 CC BY 4.0 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.
Helmholtz-Gemeinschaft http://doi.org/10.13039/501100001656 ZT-I-0022 MAchine learning based Plasma density model project (MAP)