再看一眼：利用稀疏观测修正海冰预报
Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations
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中文总结 AI 辅助
针对海冰预报中稀疏观测带来的误差累积问题，提出 ECHO 方法，通过状态依赖的传播距离修正，在 96 种设置下优于固定传播，其中 ECHO-Delta 精度最优，ECHO-Scale 稳健性最佳。
中文摘要 AI 辅助
海冰预报提前数天发布，在此期间，新的稀疏海冰浓度（SIC）观测数据不断出现，而误差会随时间累积。我们发现，固定传播的误差集中在结构化的、高梯度的冰缘附近，而均匀的内部区域则需要有限的传播，这表明传播距离应依赖于状态。因此，我们引入了 ECHO（基于证据的异构传播修正），其中 ECHO-Scale 自适应调整传播距离，同时保持修正的几何形状，而 ECHO-Delta 则学习围绕固定传播的有界残差。在所有 96 种标准评估设置中，涵盖不同的先验、观测时间、稀疏程度、几何形状和噪声条件，两种方法均优于固定传播。ECHO-Delta 实现了最佳的平均精度，而 ECHO-Scale 对几何变化更为稳健。代码可在 https://URL 获取。
英文摘要
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.
发表机构
- Harbin Engineering University（哈尔滨工程大学）
机构由 AI 辅助整理，请以论文原文为准。
