luizfillip / airglow_data-driven_model

This is the airglow data-driven model repository.

Data-driven modeling of atomic oxygen airglow over a period of three solar cycles

Supplemental materials to the paper in JGR: Space Physics (pre-print, Full Text):

• Python notebook (airglow_data_driven_modeling.ipynb) that contains complete analysis and results presented in the article

• Pandas Data Frames with data for the machine learning techniques described in the Python notebook to model airglow green line (df_i5577_label_features_1964-1993.pkl) and red line (df_i6300_label_features_1964-1993.pkl). Data Frames consists of:

  • airglow data (available via: https://ndmc.dlr.de/)
  • space weather indeces (downloaded from: https://omniweb.gsfc.nasa.gov/form/dx1.html)
  • thermosphere parameters (downloaded from https://ccmc.gsfc.nasa.gov/modelweb/models/nrlmsise00.php)
  • ionosphere parameters (downloaded from: https://ccmc.gsfc.nasa.gov/modelweb/models/iri2016$\_$vitmo.php)
  • Sun-Earth distance (calculated by: https://pypi.org/project/pyephem)

• Pandas Data Frame (df_glow_1964-1993.pkl) that contain airglow intensities calculated by the GLOW model (calculated by: https://github.com/scivision/ncar-glow)

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How to cite:

Mackovjak, Š., Varga, M., Hrivňak, S., Palkoci, O., & Didebulidze, G. G. (2021). Data‐driven modeling of atomic oxygen airglow over a period of three solar cycles. Journal of Geophysical Research: Space Physics, 126, e2020JA028991. https://doi.org/10.1029/2020JA028991

BibTex:
@article{https://doi.org/10.1029/2020JA028991,
author = {Mackovjak, S. and Varga, M. and Hrivnak, S. and Palkoci, O. and Didebulidze, G. G.},
title = {Data-Driven Modeling of Atomic Oxygen Airglow over a Period of Three Solar Cycles},
journal = {Journal of Geophysical Research: Space Physics},
volume = {126},
number = {3},
pages = {e2020JA028991},
keywords = {airglow, machine learning},
doi = {https://doi.org/10.1029/2020JA028991},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JA028991},
year = {2021}
}