calculating-spaceweather-keywords

The Solar Dynamics Observatory (SDO) takes about a terabyte and a half of data a day, which is more data than any other satellite in NASA history. SDO data are stored in a publicly-available, web-accessible pSQL database at Stanford University. These data are also accessible via a JSON API called jsoc_info and a python library called drms.

One of the data products released by the Solar Dynamics Observatory is called Space-weather HMI Active Region Patches, or SHARPs. SHARPs include patches of vector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI) instrument aboard SDO. These patches encapsulate automatically-detected active regions. SHARP data also include spaceweather keywords describing these active regions. Bobra & Couvidat (2015), Bobra & Ilonidis (2016), and Jonas et al. (2018) used machine-learning algorithms to show that these spaceweather keywords are useful for predicting solar activity.

Contents

This repository contains several codes designed to show you how to interact with and understand SHARP data (view ipython notebooks on the ipython notebook viewer).

• plot_swx_d3.ipynb generates interactive d3 plots of keywords and images, and movies using image data.
• movie.ipynb generates movies of SHARP data.
• hedgehog.ipynb provides a way to visualize the vector magnetic field in SHARP data.
• feature_extraction.ipynb takes images from another instrument on SDO, called the Atmospheric Imaging Assembly (AIA), to determine which AIA pixels fall within the SHARP bounding boxes; this code also contains examples of how to automatically extract features from AIA data.
• calculate_swx_fits.py contains all the functions to calculate spaceweather keywords from vector magnetic field data. calculate_swx_workflow.ipynb provides a workflow to calculate these keywords by fetching the vector magnetic field data from the JSOC database using the drms package and using Dask to parallelize the calculations.
• disambiguation.py contains several functions that disambiguate the azimuthal component of the vector magnetic field data and construct the field vector in spherical coordinate components on a CCD grid. See disambiguate_data.py for some examples.

Sample data are included in this repository under the test_fits_files directory. All SDO data are publicly available.

Citation

If you use the Space-weather HMI Active Region Patch data in your research, please consider citing our paper. Here is the bibtex entry for the paper:

@ARTICLE{2014SoPh..289.3549B,
   author = {{Bobra}, M.~G. and {Sun}, X. and {Hoeksema}, J.~T. and {Turmon}, M. and 
	{Liu}, Y. and {Hayashi}, K. and {Barnes}, G. and {Leka}, K.~D.
	},
    title = "{The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs - Space-Weather HMI Active Region Patches}",
  journal = {\solphys},
archivePrefix = "arXiv",
   eprint = {1404.1879},
 primaryClass = "astro-ph.SR",
 keywords = {Active regions, magnetic fields, Flares, relation to magnetic field, Instrumentation and data management},
     year = 2014,
    month = sep,
   volume = 289,
    pages = {3549-3578},
      doi = {10.1007/s11207-014-0529-3},
   adsurl = {http://adsabs.harvard.edu/abs/2014SoPh..289.3549B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}