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Filtering by: Creator Manchester, Ward Discipline Engineering

Results for "Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model"

Creator:

Jivani, Aniket, Sachdeva, Nishtha, Huang, Zhenguang, Chen, Yang, van der Holst, Bart, Manchester, Ward, Iong, Daniel, Chen, Hongfan, Zou, Shasha, Huan, Xun, and Toth, Gabor

Description:

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).

Keyword:

Uncertainty Quantification, Space Weather, and Global Sensitivity Analysis

Discipline:

Engineering
Data for Solar Flare Intensity Prediction with Machine Learning Models

Creator:

Jiao, Zhenbang, Chen, Yang, and Manchester, Ward

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.

Keyword:

Solar Flare Prediction and Machine Learning

Citation to related publication:

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

Discipline:

Engineering and Science

Results for "Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model"

Data for Solar Flare Intensity Prediction with Machine Learning Models

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Results for "Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model"

Data for Solar Flare Intensity Prediction with Machine Learning Models

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