API src

Found 5 results.

Other language confidence: 0.9814974942086085

The 3-rd Order Polynomial Fit Coefficients of Electron Lifetime Caused by Interaction with Chorus Waves

In near-Earth space, a large population of high-energy electrons are trapped by Earth’s magnetic field. These energetic electrons are trapped in the regions called Earth’s ring current and radiation belts. They are very dynamic and show a very strong dependence on solar wind and geomagnetic conditions. These energetic electrons can be dangerous to satellites in the near-Earth space. Therefore, it is very important to understand the mechanisms which drive the dynamics of these energetic electrons. Wave-particle interaction is one of the most important mechanisms. Among the waves that can be encountered by the energetic electrons when they move around our Earth, whistler mode chorus waves can cause both acceleration and the loss of energetic electrons in the Earth's radiation belts and ring current. Using more than 5 years of wave measurements from NASA’s Van Allen Probe mission, Wang et al (2019) developed chorus wave models which depend on magnetic local time (MLT), Magnetic Latitude (MLat), L-shell, and geomagnetic condition index Kp. To quantify the effect of chorus waves on energetic electrons, we calculated the bounce-averaged quasi-linear diffusion coefficients using the chorus wave model developed by Wang et al (2019) and extended to higher latitudes according to Wang and Shprits (2019). Using these diffusion coefficients, we calculated the lifetime of the electrons with an energy range from 1 keV to 2 MeV. In each MLT, we calculate the lifetime for each energy and L-shell using two different methods according to Shprits et al (2007) and Albert and Shprits (2009). We make the calculated electron lifetime database available here. Please notice that the chorus wave model by Wang et al (2019) is valid when Kp <= 6. If the user wants to use this lifetime database for Kp >6, please be careful and contact the authors.

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.

PCEEJ Equatorial Electrojet Model data for years 2003-2010 and 2014-2018

This dataset comprises the PCEEJ equatorial electrojet model current intensity values (mA/m). The PCEEJ is an empirical model based on the principal component analysis of satellite and ground equatorial electrojet data, described in detail in Soares et al. (2022), to which this data publication is supplement to. The model data is provided as text files (.csv extension) and Matlab-formatted files (.mat extension). For text files, there is one file per year (file name labeled with the corresponding year). For the Matlab format, there is only one Matlab file that contains all years as separate variables (variable name labeled with the corresponding year). Each yearly file/variable corresponds to a matrix: the rows represent local time/longitude bins and the columns represent days of year. The local time/longitude bins (rows) always sum up to 432 (12 local time intervals and 36 longitude intervals). The day of year (columns) always starts in January 1st and ends in December 31st, leading to a total of 365 or 366. The PCEEJ model values of 13 years from 2003 to 2010 and from 2014 to 2018 are provided. The PCEEJ basis functions (principal components) are provided in the text and Matlab files labeled as ‘PC_Functions’. The ‘PC_Functions’ data is given as a 432x10 matrix, in which 432 stands for the aforementioned local time/longitude bins and 10 represents the 10 principal components used to obtain the PCEEJ model (in ascending order). Two additional auxiliary indices, namely ‘lt_index’ and ‘lon_index’ are also contained as text and Matlab files. These indices represent the corresponding local time and longitude values of each row of the PCEEJ yearly files and ‘PC_Functions’ files.

MLT-averaged Plasmapause Position Calculated from the PINE Plasmasphere Model for the GEM Challenge Events

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)]

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).

1