Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations

2026-09-21

AI Summary

该研究通过引入ECHO方法，利用稀疏观测数据校正海冰预测中的累积误差，提高了预报准确性。

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.

Problem

• Sea ice forecasts
• Sparse observations
• Error accumulation
• State dependent

Innovation

• ECHO
• state-dependent propagation
• sparse observations
• sea ice forecasts
• adaptive correction