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Filtering by: Creator Chen, Yang Creator Iong, Daniel Creator Zou, Shasha

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
Results for "New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines"

Creator:

Iong, Daniel, Chen, Yang, Toth, Gabor, Zou, Shasha, Pulkkinen, Tuija I., Ren, Jiaen, Camporeale, Enrico, and Gombosi, Tamas I. I.

Description:

In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.

Keyword:

SYM-H forecasting

Citation to related publication:

Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3

Discipline:

Science

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

Results for "New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines"

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

Results for "New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines"

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