SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting
Keywords: Arctic Sea Ice Forecasting, Foundation Model, Multi-granularity
Abstract: Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities.
In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have emerged and showcased superior performance over physics-based dynamical models.
However, previous methods forecast SIC at a fixed temporal granularity, e.g. sub-seasonal or seasonal, thus only leveraging inter-granularity information and overlooking the plentiful inter-granularity correlations.
SIC at various temporal granularities exhibits cumulative effects and are naturally consistent, with short-term fluctuations potentially impacting long-term trends and long-term trends provides effective hints for facilitating short-term forecasts in Arctic sea ice.
Therefore, in this study, we propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data and provide a unified perspective for modeling SIC via our Sea Ice Foundation Model.
SIFM is delicately designed to leverage both intra-granularity and inter-granularity information for capturing granularity-consistent representations that promote forecasting skills.
Our extensive experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 748
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