Work Description

Title: Data for Solar Flare Intensity Prediction with Machine Learning Models Open Access Deposited

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Attribute Value
Methodology
  • We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0~24, 6~30, 12~36 and 24~48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP). The model makes use of (1) the Space-weather HMI Active Region Patch (SHARP) parameters as predictors and (2) the exact flare intensities instead of class labels recorded in the Geostationary Operational Environmental Satellites (GOES) data set, which serves as the source of the response variables. The data deposited here are the input for our machine learning efforts for predicting solar flare events. The input data are produced by our pre-processing pipeline, which is built to extract from two sources -- the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) . The GOES files provide a list of flare events in excel csv format. The SHARP files provide time sequences of more than 20 physical parameters derived from SDO/HMI data, which are saved in hdf5 format. The input data Space-Weather HMI Active Region Patch (SHARP) parameters are available online from the Stanford Joint Science Operations Center (JSOC) at  http://jsoc.stanford.edu/ and the GOES data are available from NOAA Space Weather Prediction Center (SWPC) at  https://www.swpc.noaa.gov.
Description
  • GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time. SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
Creator
Depositor
  • chipm@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
  • National Science Foundation (NSF)
ORSP grant number
  • F051148 and F056704
Keyword
Citations to related material
  • Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar Flare Intensity Prediction With Machine Learning Models. Space Weather, 18(7), e2020SW002440. https://doi.org/10.1029/2020SW002440
  • Chen, Y., & Manchester, W. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue. https://doi.org/10.7302/qnsq-cs38
Resource type
Last modified
  • 11/18/2022
Published
  • 04/20/2020
Language
DOI
  • https://doi.org/10.7302/b07j-bj08
License
To Cite this Work:
Jiao, Z., Chen, Y., Manchester, W. (2020). Data for Solar Flare Intensity Prediction with Machine Learning Models [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/b07j-bj08

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Files (Count: 5; Size: 2.27 GB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
readme.txt 2020-04-17 2020-04-17 992 Bytes Open Access
readme_GOES_data.txt 2020-04-17 2020-04-17 178 Bytes Open Access
readme_SHARP_data.txt 2020-04-20 2020-04-20 1.4 KB Open Access
GOES_dataset.csv 2020-04-17 2020-04-17 974 KB Open Access
SHARP_with_flares_data.hdf5 2020-04-17 2020-10-06 2.27 GB Open Access

GOES_dataset.csv contains a list of 12,013 flare events. The list has 6 columns, flare classification, NOAA active region number, date, start time end time, emission peak time.

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