OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

OceanVerse: Evaluable 4D Ocean Element Reconstruction Dataset under Realistic Sparsity

The first oceanic 4D sparse observation reconstruction dataset, named OceanVerse, is presented, providing a novel large-scale dataset that meets the MNAR (Missing Not at Random) condition, supporting more effective model comparison, generalization evaluation and potential advancement of scientific reconstruction architectures.

Wide-swath satellite altimetry and novel subsurface temperature observations improve predictions in a dynamic western boundary current: System optimization and performance

OceanBench: A Benchmark for Data-Driven Global Ocean Forecasting systems

OceanBench is a benchmark designed to evaluate and accelerate global short-range data-driven ocean forecasting, constructed from a curated dataset comprising first-guess trajectories, nowcasts, and atmospheric forcings from operational physical ocean models, typically unavailable in public datasets due to assimilation cycles.

Data Driven Reconstruction of Upper Ocean Profiles for Improved State Estimation in the Philippine Sea

Despite significant advances in observational systems such as the global Argo array of autonomous profiling floats, the spatiotemporal coverage of subsurface ocean observations remains limited compared to the dense data provided by satellite platforms. This study develops a data‐driven framework to reconstruct synthetic profiles of upper ocean temperature and salinity by training a self‐attention‐based neural network with satellite‐derived sea surface height (SSH) and sea surface temperature anomalies, using 17 years of collocated Argo float measurements. Daily synthetic profiles for the upper 650 m of the Philippine Sea were generated for the entirety of 2010 and assimilated into a regional ocean model via 4D‐Var data assimilation. Results show overall improved effectiveness of state estimation when synthetic profiles are utilized. Diagnostic variables like temperature, salinity, SSH, horizontal velocity all show improvement. Synthetic profiles of subsurface temperature had an overall positive impact on SSH analysis and forecast, especially in regions east of the Luzon Strait and the southern domain influenced by the North Equatorial Current. In the vertical range of 150–600 m, the impact of synthetic profiles on various observations was promising, leading to substantial reductions in the analysis and forecast error.

RoughNet: Mapping Arctic Sea Ice Roughness Using Diffusion-Based Super-Resolution of Satellite Imagery

It is shown that high-resolution sea ice topography can be reconstructed directly from optical satellite imagery using a conditional diffusion framework, providing a scalable pathway for high-resolution sea ice mapping and roughness estimation in data-sparse environments.

Deep Ocean Temperature from the Stratus Ocean Reference Station (85 °W, 20 °S), 2012-2025.

Long-term deep ocean temperature monitoring is crucial for understanding the ocean's role in climate variability and storage of heat in the deep ocean. Observation of the ocean surface is relatively accessible via both in-situ and remote sensing; however, continuous, high-temporal resolution, decade-long temperature records from abyssal depths face the technical challenges of sustained deep ocean deployments. The addition of calibrated, internally-recording temperature sensors to deep-ocean moorings not far from the sea floor provides a means of making high temporal resolution temperature observations. Quality controlling and merging records from successive mooring deployments results in a decade-long time series. Our work has been to optimize approaches for ensuring data quality and continuity in multi-year deep ocean temperature datasets. Here we show a comprehensive processing framework that yields 13 years (2012 to 2025) of continuous temperature measurements at approximately 4200 to 4500 meters depth from the Stratus Ocean Reference Station near 22 °S, 85 °W, 1500 km off the coast of Chile in the Southeast Pacific. Our framework incorporates timing checks, automated spike detection, systematic multi-sensor validation, statistical validation, human-in-the-loop quality control, and merging protocols. This framework establishes reproducible standards for processing long term oceanographic observations from multiple deployments. For the Stratus data set, the result is a unique, decade-long abyssal temperature record with quantified uncertainties that constitutes a benchmark time series for evaluating the realism of deep ocean temperature in models.