Sea ice forecast errors corrected using adaptive sparse observations
Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations
Machine Learning
Summary
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.
What this means in practice
- •For weather forecasting teams: Improve daily sea ice forecasts by correcting errors with adaptable correction distances using limited observations.
- •For marine navigation planners: Enhance navigation safety by utilizing more accurate short-term sea ice forecasts that incorporate adaptive corrections near ice edges.
Authors
Tianshuo Zhang, Xianglei Xing, Aowen Yang, Jia Gao, Wenzhe Zhai, ShanShan Liu
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.