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- Creator:
- Liemohn, Michael W
- Description:
- Earth’s upper atmosphere above 500 km altitude constantly loses charged particles to outer space in a process called ionospheric outflow. This outflow is important for the dynamics of the near-Earth space environment (“space weather”) yet is poorly understood on a global scale. A mission is needed to observe the global patterns of ionospheric outflow and its relation to space weather driving conditions. The science objectives of such a mission could include not only the reconstruction of global outflow patterns but also the relation of these patterns to geomagnetic activity and the spatial and temporal nature of outflow composition. A study is presented to show that four well-placed spacecraft would be sufficient for reasonable outflow reconstructions.
- Keyword:
- ionosphere, magnetosphere, satellite mission concept, and space weather
- Citation to related publication:
- Liemohn, M. W., Jörg-Micha Jahn, Raluca Ilie, Natalia Y. Ganushkina, Daniel T. Welling, Heather Elliott, Meghan Burleigh, Kaitlin Doublestein, Stephanie Colon-Rodriguez, Pauline Dredger, & Philip Valek (2024). Reconstruction analysis of global ionospheric outflow patterns. Journal of Geophysical Research Space Physics, 129, e2023JA032238. https://doi/org/10.1029/2024JA032238
- Discipline:
- Science
-
- Creator:
- Al Shidi, Qusai. and Pulkkinen, Tuija.
- Description:
- This is part of the simulation set of geomagnetic storms from 2010 to 2019. The Space Weather Modeling Framework (SWMF) with the configuration of SWPC v2 was used. The output files can be read by the visualization scripts included in the SWMF or the SpacePy Python package.
- Keyword:
- space weather, space physics, geomagnetic storms, and magnetosphere
- Discipline:
- Science
-
- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
-
- Creator:
- McCuen, Brett A.
- Description:
- The data were used to study the high-frequency geomagnetic disturbances within the magnetic field data. Included in this repository are the python scripts that perform an identification and classification of high-frequency signals within the magnetometer data that is downloaded from the databases listed in the Methodology section. All analysis and plots were created using subsequent Python libraries. The machine learning study implemented libraries from the sci-kit learn software. All of the specific methodology can be accessed in the readme.txt script.
- Keyword:
- geomagnetic field, high frequency, space weather, transient-large-amplitude, TLA, high frequency dB/dt, and dB/dt search algorithm
- Discipline:
- Science
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- Creator:
- Liemohn, Michael W, McCollough, James P, Engel, Miles A, Jordanova, Vania K, and Morley, Steven K
- Description:
- There is a directory tree inside this zipped file. The main directory has the Adobe Illustrator plots of the figures in the paper, Space Weather journal manuscript # 2018SW002067, "Model evaluation guidelines for geomagnetic index predictions" by M. W. Liemohn and coauthors. The three subdirectories have the files for the individual models, the data to which they are compared, and the IDL code used to create the figure plots and metrics calculations. and Date coverage is specific to each model. The RAMSCB model covers January 2005, the WINDMI model all of 2014, and the UPOS model 1.5 solar cycles, from 1 October 2001 through 29 July 2013.
- Keyword:
- space weather, model assessment, time series metrics, and geomagnetic indices
- Discipline:
- Science