In this study, we show that coronal mass ejection (CME) simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) coronagraph observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. L1 observations are used to constrain the background solar wind, while LASCO coronagraph observations filter the initial ensemble simulations by constraining the simulated CME propagation speed. We then construct probabilistic predictions for CME arrival time using the data-assimilated ensemble. Scripts in this work are written in R, Python and Julia.
In this work, we perform Global Sensitivity Analysis (GSA) for the background solar wind in order to quantify contributions from uncertainty of different model parameters to the variability of in-situ solar wind speed and density at 1au, both of which have a major impact on CME propagation and strength. Scripts written in the Julia language are used to build the PCE and calculate the sensitivity results. Data is available in csv, NetCDF and JLD files. A `Project.toml` file is included to activate and install all required dependencies (See README for details).
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.
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 and 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
