Understanding Global Patterns of Sea Level Rise and Ocean Heat Redistribution (Project C2)

了解海平面上升和海洋热量重新分布的全球模式（项目 C2）

• 批准号: 498498566
• 负责人: Professor Dr.-Ing. Jürgen Kusche
• 金额: --
• 依托单位: Lehrstuhl für Astronomische, Physikalische und Mathematische Geodäsie
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498498566?language=en
• 关键词: Understanding; Global; Patterns; Sea; Level

Project C2 will address questions that are fundamental to our understanding of climate change: What are theregional rates of sea level rise? How is regional sea level rise related to changes in sea level extremes andto changes in sea state? To what extent do satellite observations of the sea surface fit to modelling of ocean heat change (OHC)? Which patterns of heat redistribution can be derived from satellite observations? Wewill explore the synergy in the RU to better exploit the vast archive of satellite data and ocean modelling.New and efficient methods for geometric representation and clustering data will be tailored to the problem (AI methods have been introduced in ocean remote sensing before, but these were mostly transferredfrom image analysis and often did not properly account for the geometrical nature of the problems). Thus,within this RU, we decided to address four particular problems with novel AI-based methods: (1) Consistent analysis of radar echoes for sea surface height (SSH) and significant wave height (SWH), (2) Combining and analysing SSH variability, (3) Reconstructing multi-decadal SSH with sparse data, and (4) inverting radar observations of SSH into OHC. These problems follow the analysis chain in sea level research, but they can be addressed independently.

项目C2将解决对我们理解气候变化至关重要的问题：海平面上升的区域速率是多少？区域性海平面上升与海平面极值变化和海洋状态变化有何关系？卫星对海面的观测在多大程度上适合海洋热变化（OHC）建模？从卫星观测中可以得出哪些热再分布模式？我们将探索RU的协同作用，以更好地利用大量的卫星数据和海洋建模档案。新的和有效的几何表示和聚类数据的方法将针对问题（人工智能方法以前曾被引入海洋遥感，但这些方法大多来自图像分析，通常不能正确地解释问题的几何性质）。因此，在该RU中，我们决定使用基于人工智能的新方法解决四个特定问题：（1）海面高度（SSH）和有效波高（SWH）的雷达回波的一致性分析，（2）组合和分析SSH变化，（3）用稀疏数据重建多年代SSH，（4）将SSH的雷达观测值反演为OHC。这些问题遵循海平面研究中的分析链，但它们可以独立解决。