Papers for

marine navigation planners

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Sea ice forecast errors corrected using adaptive sparse observations

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

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.

Mon 21 SeptMachine Learning

The gist

Forecasts of sea ice several days ahead can have errors, especially near ice edges where conditions change quickly. The authors found that these errors spread differently across ice areas, depending on whether the ice is stable or near borders. They created ECHO, a method that adjusts how far correction information travels based on ice conditions, improving forecast accuracy. Their approach works better than traditional methods across many different scenarios, handling sparse and noisy data more effectively.

Open → 2609.24591v1