论文标题
对物理受限域的空间预测：北极海盐度数据的应用
Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data
论文作者
论文摘要
在本文中，我们根据卫星测量值预测北极海洋中的海面盐度（SSS）。 SSS是北极海洋持续变化的关键指标，可以提供有关气候变化的重要见解。我们特别关注卫星算法错误地标记为冰的水区域。为了消除海冰附近盐度检索中的偏见，算法使用保守的冰面膜，从而导致大量数据丢失。我们旨在为此类地区生成现实的SSS值，以使对北极海洋上的SSS表面有更全面的了解，并使未来的应用程序受益于海冰或海岸边缘附近的SSS测量。我们提出了一类可扩展的非组织过程，这些过程可以处理卫星产品和北极海洋复杂几何形状的大数据。屏障重叠式无环针对图GP（BORA-GP）构建稀疏的定向无环形图（DAG），邻居符合障碍和边界，从而表征受约束域中的依赖性。与最先进的替代方案相比，在没有卫星测量的区域中，BORA-GP模型在没有卫星测量的区域中产生更明智的SSS值，并显示出模拟研究中各种受约束域的性能提高。可在https://github.com/jinbora0720/boragp上找到R包。
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 at https://github.com/jinbora0720/boraGP.
