SPATIAL PREDICTIONS ON PHYSICALLY CONSTRAINED DOMAINS: APPLICATIONS TO ARCTIC SEA SALINITY DATA

• 成果类型: Article
• 署名作者: Jin, Bora; Herring, Amy H.; Dunson, David
• 署名单位: Johns Hopkins University; Duke University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1850
• 发表日期: 2024
• 页码: 1596-1617
• 关键词: gaussian process models; ocean; ice; regression; water

摘要

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.