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    Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM (Papers Track)

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    Links to Files
    https://www.climatechange.ai/papers/icml2021/50
    Permanent Link
    http://hdl.handle.net/11603/25884
    Collections
    • UMBC Faculty Collection
    • UMBC Information Systems Department
    • UMBC Mathematics and Statistics Department
    • UMBC Student Collection
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    Author/Creator
    Ali, Sahara
    Huang, Yiyi
    Huang, Xin
    Wang, Jianwu
    Author/Creator ORCID
    https://orcid.org/0000-0002-9933-1170
    Type of Work
    Moving Image
    Text
    conference papers and proceedings
    presentations (communicative events)
    video recordings
    Citation of Original Publication
    "Ali, Sahara et al. Forecasting Sea Ice Concentrations using Attention-based Ensemble LSTM. ICML 2021 Workshop Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/icml2021/50."
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    decline in sea ice
    Arctic sea ice forecasting
    attention-based Long
    Short Term Memory (LSTM) ensemble method
    UMBC Big Data Analytics Lab
    Abstract
    Accurately forecasting Arctic sea ice from sub-seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.