In this paper, we predict sea surface salinity (SSS) in the Arctic Ocean based on satellite measurements. SSS is a crucial indicator for ongoing changes in the Arctic Ocean and can offer important insights about climate change. We particularly focus on areas of water mistakenly flagged as ice by satellite algorithms. To remove bias in the retrieval of salinity near sea ice, the algorithms use conservative ice masks, which result in considerable loss of data. We aim to produce realistic SSS values for such regions to obtain more complete understanding about the SSS surface over the Arctic Ocean and benefit future applications that may require SSS measurements near edges of sea ice or coasts. We propose a class of scalable nonstationary processes that can handle large data from satellite products and complex geometries of the Arctic Ocean. Barrier Overlap-Removal Acyclic directed graph GP (BORA-GP) constructs sparse directed acyclic graphs (DAGs) with neighbors conforming to barriers and boundaries, enabling characterization of dependence in constrained domains. The BORA-GP models produce more sensible SSS values in regions without satellite measurements and show improved performance in various constrained domains in simulation studies compared to state-of-the-art alternatives. An R package is available on https://github.com/jinbora0720/boraGP.

翻译：本文基于卫星测量数据，对北冰洋海表盐度（SSS）进行预测。SSS是北冰洋持续变化的关键指标，可为气候变化研究提供重要见解。我们重点关注卫星算法误判为冰盖的水域。为消除近海冰区域盐度反演中的偏差，现有算法采用保守的冰盖掩膜，导致大量数据缺失。本研究旨在为这些区域提供实际SSS值，以更全面地理解北冰洋SSS分布特征，并为需要近海冰边缘或海岸SSS测量的未来应用提供支持。我们提出一类可扩展的非平稳过程，能够处理卫星产品的大规模数据及北冰洋的复杂几何结构。屏障重叠消除无环有向图高斯过程（BORA-GP）通过构建符合屏障与边界约束的稀疏有向无环图（DAG），实现对约束域相关性的表征。该模型在无卫星测量区域生成更合理的SSS值，并在模拟研究中验证了其在多种约束域中相较于现有先进方法的优越性能。相关R语言工具包可在https://github.com/jinbora0720/boraGP获取。