staging
Library Digital Collections
Library Digital Collections

Data from: Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion

View Collection Items

Collections »

Data from: Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion

About this collection

Extent

1 digital object.

Cite This Work

Hoffman, Lauren A.; Mazloff, Matthew R.; Gille, Sarah T.; Giglio, Donata; Heimbach, Patrick (2024). Data from: Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0S182Q6

Description

With the aim of using explainable AI to understand predictions made by machine learning models built to predict sea-ice motion in the Arctic on one-day timescales, these data include processed satellite and reanalysis measurements of sea-ice velocity, sea-ice concentration, and wind velocity. Also included are outputs from statistical model predictions. Finally, we include all files required to download and process raw data, run statistical models, and plot analyses of outputs.

Date Collected
  • 1989 to 2021
Date Issued
  • 2024
Author
Advisors
Contributor
Funding

Outputs from machine learning models and XAI were funded by Office of Naval Research grant N00014-20-1-2772. LH was supported by ONR (grant N00014-20-1-2772). MRM was supported by ONR (grant N00014-20-1-2772) and by NSF (award OPP-1936222). STG was supported by NSF (award OPP-1936222) and by U.S. Department of Energy (DOE) (Award DE-SC002007). DG was supported by NSF Award 1928305. PH was supported by ONR (grant N00014-20-1-2772).

Geographic
Topics

Format

View formats within this collection

Language
  • English
Identifier

Identifier: Donata Giglio: https://orcid.org/0000-0002-3738-4293

Identifier: Lauren A. Hoffman: https://orcid.org/0000-0002-9563-8925

Identifier: Matthew R. Mazloff: https://orcid.org/0000-0002-1650-5850

Identifier: Patrick Heimbach: https://orcid.org/0000-0003-3925-6161

Identifier: Sarah T. Gille: https://orcid.org/0000-0001-9144-4368

Related Resources

    Primary associated publication

    • Hoffman, L., Mazloff, M.R., Gille, S.T., Giglio, D., and Heimbach, P. [submitted 2024] Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion.

    Source data

    • Cavalieri, D. J., Parkinson, C. L., Gloersen, P. & Zwally, H. J. (1996). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. Date Accessed 2022-10. https://doi.org/10.5067/8GQ8LZQVL0VL

    • JRA-55 based surface dataset for driving ocean-sea ice models (JRA55-do), wind velocity: https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/

    • Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C. & Maslanik, J. (2019). Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. Date Accessed 2022-10. https://doi.org/10.5067/INAWUWO7QH7B

    • Tsujino, Hiroyuki; Urakawa, Shogo; et al. (2020). input4MIPs.CMIP6.OMIP.MRI.MRI-JRA55-do-1-5-0. Version 2022-10. Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.15017

    Software

    Collection image

    • Image credit: Lauren Hoffman. Schematic of the (a) machine learning model and (b and c) explainable artificial intelligence (XAI) processes applied to understand predictions of Arctic sea-ice motion.

Data from: Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predi...
View Collection Items