OceanDepths：全球地下与表层海洋配对观测数据集
OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations
- Image Processing Laboratory, University of Valencia（瓦伦西亚大学）
- ESA-ESRIN（欧洲空间局欧洲空间研究和技术中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出首个开放全球AI适配数据集OceanDepths，配对卫星表层场与EN4地下剖面，覆盖2000-2024年、0.1°分辨率，可支撑AI海洋相关任务，含950多万配对剖面。
AI中文摘要：
尽管海洋覆盖了地球表面的70%以上，但与陆地表面相比，其观测严重不足。全球海洋需要联合观测表层和地下结构，但目前尚无标准化、高分辨率的数据集将卫星表层场与同位置的现场深度剖面配对，以适配人工智能（AI）应用。现有资源要么是模型重建的网格化产品而非观测数据，要么仅覆盖单一变量或盆地，要么分辨率过粗无法满足中尺度研究需求。本文提出了OceanDepths，这是首个开放、全球范围、重网格化且适配AI的数据集，它将卫星衍生的海表温度（SST）、海表盐度（SSS）和海表高度（SSH）L4产品与同位置的EN4地下温度和盐度剖面配对，并补充了匹配的GLORYS12海洋再分析数据，以支持比较或多阶段分析。该数据集覆盖2000年至2024年，空间分辨率为0.1°×0.1°，时间分辨率为每周，覆盖全球全部海表，包含超过950万个配对剖面，这些剖面被插值到50个标准化深度层。我们提供了一个可配置系统，将全球划分为大小相等的空间区域，以支持4维多变量结构的研究。地下观测的高分辨率、长时间跨度以及极端稀疏性（每个深度层约0.01%）使OceanDepths成为新型AI任务的挑战性测试平台。我们以简单基线模型为例展示了地下状态重构任务，同时期望OceanDepths能支持基于观测的预报方法及其他相关任务的开发。该数据集可在指定网址获取。
英文摘要：
Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite surface fields to co-located in situ depth profiles in an AI-ready format. Existing resources either consist of model-reconstructed gridded products rather than observations, cover only a single variable or basin, or operate at resolutions too coarse for mesoscale dynamics. We introduce OceanDepths, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) L4 products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage learning. The dataset spans 2000-2024 at 0.1 degrees x 0.1 degrees spatial resolution and at weekly temporal resolution, covering the entire globe's sea surface and with over 9.5 million paired profiles interpolated to 50 standardized depth levels. We provide a configurable system to split the globe in equally sized spatial patches. The 4D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations (approximately 0.01% per depth level) make OceanDepths a challenging testbed for novel AI methods. We demonstrate subsurface state reconstruction as an example task with simple baseline models, but also envision OceanDepths to support the development of observation-based forecast methods and other related tasks. Available at: https://huggingface.co/datasets/ESA-philab/OceanDepths.
