Data from: Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill
Data from: Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill
About this collection
- Extent
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1 digital object.
- Cite This Work
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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
- Description
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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.
- Date Collected
- 1989 to 2021
- Date Issued
- 2023
- Author
- Advisors
- Contributors
- Funding
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Outputs from machine learning models were funded by Office of Naval Research grant N00014-20-1-2772.
- Geographic
- Topics
Formats
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- Language
- English
- Identifier
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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
- Related Resources
- 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
- 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/
- Image source: Lauren Hoffman, "Performance of machine learning model built to predict sea-ice motion in the Arctic." Maps showing the performance of a machine learning model built to predict sea-ice motion in the Arctic. (Left) A convolutional neural network (CNN) makes skillful predictions of sea-ice motion in the Arctic, where lighter colors are indicative of a higher predictive skill in the map of the first panel. (Right) A CNN typically outperforms a traditional linear regression (LR) model in predicting Arctic sea-ice motion, where red is indicative of areas where the CNN outperforms LR in the map of the second panel.
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