Hoffman, Lauren; Mazloff, Matthew R.; Gille, Sarah T.; Giglio, Donata; Bitz, Cecilia M.; Heimbach, Patrick; Matsuyoshi, Kayli. (2023). Data From: Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0X06774

With the aim of using machine learning as a tool to predict and understand 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.

• 1989 to 2021

• 2023

• Hoffman, Lauren A.

• Giglio, Donata
• Gille, Sarah T.
• Mazloff, Matthew R.

• Bitz, Cecilia M.
• Heimbach, Patrick
• Matsuyoshi, Kayli

MATLAB R2021b; Python 3.8.5; TensorFlow 2

Outputs from machine learning models were funded by Office of Naval Research grant N00014-20-1-2772.

• Arctic Ocean

• Algorithm: Supervised learning
• Machine learning
• Operational forecasting
• Prediction
• Sea-ice velocity
• Task: Forecasting
• Task: Regression
• Wind velocity

Polygon: 90.0,-180.0 90.0,180.0 60.0,180.0 60.0,-180.0 90.0,-180.0

• data

• English

Identifier: Cecilia M. Bitz: https://orcid.org/0000-0002-9477-7499

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

Primary associated publication

• Hoffman, L., Mazloff, M. R., Gille, S. T., Giglio, D., Bitz, C. M., Heimbach, P., & Matsuyoshi, K. (2023). Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill. Artificial Intelligence for the Earth Systems, 2(4), 230004. https://doi.org/10.1175/AIES-D-23-0004.1

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. https://doi.org/10.5067/8GQ8LZQVL0VL
• 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. https://doi.org/10.5067/INAWUWO7QH7B
• Tsujino, Hiroyuki; Urakawa, Shogo; et al. (2020). input4MIPs.CMIP6.OMIP.MRI.MRI-JRA55-do-1-5-0. Version 1.5.0 (v20200916). Earth System Grid Federation. Access at URL: https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/. https://doi.org/10.22033/ESGF/input4MIPs.15017
• Tsujino, Hiroyuki; Urakawa, Shogo; et al. (2020). 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/

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2024-09-16