DOI: 10.5281/zenodo.6342187
title: Observational Dataset for "Constraining Global Coronal Models with Multiple Independent Observables", Badman et al. (2022). Arxiv : https://arxiv.org/abs/2201.11818

description: Observational Dataset for "Constraining Global Coronal Models with Multiple Independent Observables", Badman et al. (2022). Arxiv : https://arxiv.org/abs/2201.11818 -----------------------------<br> ----------------------------- Contact : Samuel T. Badman (he/him) samuel_badman@berkeley.edu, Space Sciences Lab, UC Berkeley. -----------------------------<br> ----------------------------- License : Creative Commons Attribution 4.0 International -----------------------------<br> ----------------------------- Research Goal of Dataset : Data supports the above titled work in defining a framework for evaluating the magnetic structure of global coronal models via the evaluation of three single valued metrics. This repository contains observational data products used as input for the studies described in this work with the aim to allow external coronal modelers to reproduce and evaluate their own work against the same dataset we used. -----------------------------<br> ----------------------------- Structure of files : This repository contains three subfolders each containing observational data relating to the three metrics defined in Badman et. al. (2022). These are : ----------------------------- 1) ``Metric1_EUVCarringtonMaps'' Content : &lt;carr_maps.####.final.h5&gt; : Carrington maps of extreme ultraviolet (EUV) emission as observed by the SDO/AIA. These files contain slices of different wavelengths together, saved in hdf5 format. Maps for Carrington rotations (#### = 2210,2215,2216,2221) span the time intervals of interest in the associated work. The 193 angstrom wavelength slice from these maps were used as input into the EZSEG algorithm (see manuscript text) to generate ``observations'' of coronal hole boundaries which can then be compared via binary classification to modeled open field boundaries. &lt;read_plot_example_metric1.py&gt; : A python script which demonstrates reading in the hdf5 files and viewing the names of the different slices, then plots the 193 slice. The slice name of primary interest is 193A ('map_0193'), but slices at 171,211 angstrom, and a magnetogram are included. ----------------------------- 2) ``Metric2_StreamerBelt'' &lt;read_plot_example_metric2.py&gt; : A python script which demonstrates reading and plotting an example white light carrington map from this data set, as well as overplotting the downstream data extraction of the streamer maximum brightness (SMB) line. <br> 2a) ``Metric2_StreamerBelt/WL_CarringtonMaps'' Content : &lt;WL_CRMAP_YYYYMMDDTHHmmSS_LC2_5p0Rs.fits&gt; : Carrington maps of white light intensity extracted at 5.0Rs altitude using coronagraph images taken by SOHO/LASCO, using the method described in the manuscript and Poirier et al. (2021). Maps at a daily cadence over each 60 day time interval studied in the manuscript are included here, incorporating the new data available as the sun rotated. Here saved as fits files. &lt;WL_CRMAP_YYYYMMDDTHHMMSS_LC2_5p0Rs.mat&gt; : Carrington maps as above but saved in .mat format (MATLAB). 2b) ``Metric2_StreamerBelt/SMB_Line_Extractions'' &lt;C2_YYYMMDDHHmmSS_5.0Rs_SMB.ascii&gt; : Downstream processed versions of the relevant White light carrington map from which the line of maximum brightness (SMB line) has been extracted, as well as the streamer belt "thickness" at each longitude. This is tabulated as a 3d coordinate gridded evenly in longitude, and each SMB grid point as a northwards and southwards thickness, tabulated in degrees. These data are described in the header of each file and the extraction process is described in detail in the manuscript. ----------------------------- 3)Metric3_InSituTimeSeries Content : &lt;E##_XYZ_polarity.txt&gt; : In situ polarity timeseries for 60 day intervals at 1 hour cadences during PSP encounters ## = [01,02,03], measured by spacecraft XYZ = [PSP,STA,OMN], Parker Solar Probe, STEREO A and OMNI (Earth-L1 dataset). Data values are +/- 1 indicating if magnetic vector is directed sunward or antisunward for each hour. This value is determined as described in the main text by finding the peak of a histogram of 1D B_R values over that hour interval and taking its sign. &lt;read_plot_example_metric3.py&gt; : A python script which demonstrates reading in the in situ timeseries for encounter 1 and plotting them. &lt;gen_ss_footpoints_psp.py&gt; : A python script which demonstrates an open source method to produce source surface footpoints for a given spacecraft (here PSP) which can be used to sub-sample a HCS map provided by a modeler to generate a modeled time series which can be used to produce scores for metric 3 described in the associated manuscript. -----------------------------<br> ----------------------------- Python scripts included in this dataset use python packages astropy - https://github.com/astropy/astropy<br> h5py - https://github.com/h5py/h5py<br> astrospice - https://github.com/dstansby/astrospice<br> matplotlib - https://github.com/matplotlib/matplotlib<br> sunpy - https://github.com/sunpy/sunpy