aiqbench analysis
This research introduces ECHO, a novel machine learning framework that corrects sea ice forecasts by adapting propagation distances based on local ice geometry rather than using fixed methods. By leveraging sparse observations to learn bounded residuals and state-dependent scaling, the approach significantly outperforms traditional fixed-propagation techniques across diverse noise conditions and observation sparsity levels. The work demonstrates how adaptive ML models can effectively handle complex environmental gradients in climate prediction tasks.
Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations
arXiv:2609.24591v1 Announce Type: new Abstract: Sea ice forecasts are issued several days ahead, allowing errors to accumulate while new, often sparse sea ice concentration (SIC) observations become available. We find that fixed-propagation errors concentrate near structured, high-gradient ice edges, whereas homogeneous interiors require limited propagation, suggesting that propagation distance should be state dependent. We therefore introduce ECHO (Evidence-guided Correction with Heterogeneous prOpagation), where ECHO-Scale adapts propagation distance while preserving correction geometry, and ECHO-Delta learns a bounded residual around fixed propagation. Across all 96 standard evaluation settings spanning diverse priors, observation times, sparsity levels, geometries, and noise conditions, both outperform fixed propagation. ECHO-Delta achieves the best average accuracy, while ECHO-Scale is more robust to geometry shifts. Code is available at https://github.com/yingtian22/TAKING-A-SECOND-LOOK.
Abstract:Sea ice forecasts are issued several days ahead, allowing errors to accumulate while new, often sparse sea ice concentration (SIC) observations become available. We find that fixed-propagation errors concentrate near structured, high-gradient ice edges, whereas homogeneous interiors require limited propagation, suggesting that propagation distance should be state dependent. We therefore introduce ECHO (Evidence-guided Correction with Heterogeneous prOpagation), where ECHO-Scale adapts propagation distance while preserving correction geometry, and ECHO-Delta learns a bounded residual around fixed propagation. Across all 96 standard evaluation settings spanning diverse priors, observation times, sparsity levels, geometries, and noise conditions, both outperform fixed propagation. ECHO-Delta achieves the best average accuracy, while ECHO-Scale is more robust to geometry shifts. Code is available at this https URL.
Machine Learning (cs.LG)
arXiv:2609.24591 [cs.LG]
(or arXiv:2609.24591v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.24591 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 211 words
